Chapter Four

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GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED STUDY OF THE EVOLUTION OF MALARIA & DENGUE BETWEEN YEARS 1991 - 2000 IN SRI LANKA by

G.P.T.S.Hemakumara

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GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED STUDY OF THE EVOLUTION OF MALARIA & DENGUE BETWEEN YEARS 1991 - 2000 IN SRI LANKA Author(s): G.P.T.S.Hemakumara

Title:

Edition: Volume:

First I

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ISBN: 978-93-86675-39-2

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……i

ABSTRACT Malaria was Public health problem in Sri Lanka, with almost 300,000 infections being reported yearly in a population of 20 million in between 1991-2000. Malaria had made a major impact on the health, economy, education and general development of the population at that time. Dengue fever epidemic is causing widespread anxiety in Sri Lanka and Dengue/DHF has become a major leading cause of hospitalization and death among children in the Region. DHF incidence is showing an increasing trend and is also spreading to new areas. In 2000 Jan-May, 827 suspected dengue cases had been reported in Sri Lanka. By November 26, the figure had reached 7,177 with 4,972 cases reported during November alone. However none of the researches done in Sri Lanka involved the simultaneous evaluation of the two diseases distribution patterns, along with the climatic factors and environmental condition. Study of a ten years time series data on incidences, and environmental factors demonstrate that even if these two diseases are spreading and caused directly by mosquitoes, they are characterized by ―inverse‖ climatic and land cover patterns. Highest incidence of Malaria is taking place in low rainfall more Malaria zones, while highest incidence of Dengue is in high rainfall more urbanized zones. Risk maps and rainfall-based models of incidence have been produced at detailed local level (MOH are). These result wouldn’t have been problem without consequent use and research in the field of remote sensing and geographic information system, mainly certain input parameters calculating the risk maps and extracting the environmental characteristics and models of the incidence levels evaluating.

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GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……ii

TABLE OF CONTENT Chapter

1.

2.

3.

4.

Title

Page

Abstract Table of Content List of Figures List of Table

i ii iv v

INTRODUCTION 1.1 Background 1.2 Rationale of Study 1.3 Problem statement 1.4 Objective of the study 1.5 Scope of the study, its limitations and significance

1 4 4 5 5

LITERATURE REVIEW 2.1 DENGUE FEVER AND CLIMATE 2.2 Malaria transmission in Sri Lanka using GIS 2.3 Geographic Information Systems and Health diseases 2.4 Remote Sensing Data to identify and monitor the Health Diseases 2.5 Local Application of Remote Sensing Techniques (LARST) for Malaria detection 2.6 Use of Numerical models for Health Diseases 2.7 Geographical information systems and dynamic modeling 2.8 GIS and spatial epidemic models

6 6 7 7 9 11 11 12

STUDY AREA 3.1 Location 3.2 Climatic Condition 3.3 Topography 3.4 Ecological Zones 3.5 Land Use and Settlement Patterns 3.6 Population of Study area

13 13 14 15 17 19

METHODOLOGY 4.1 Brief Methodology 4.2 Data Acquisition 4.2.1 Administration data 4.2.2 Medical data 4.2.3 Climatic Data 4.2.4 Physical Environmental data 4.3 ArcView GIS Spatial Analyst 4.4 Shape file, Theme and Grid themes in ArcView GIS 4.5 Map Calculation in GIS ArcView 4.6 ArcView GIS based Jenks’ Optimization method 4.7 Risk based map on Malaria and Dengue

20 20 20 20 20 21 27 27 27 28 26


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……iii

4.8 Malaria / Dengue relationship with Rainfall level 4.9 Geometric Correction of Remote Sensor Data 4.10 Isodata Unsupervised Classification algorithms 4.11 Land Cover map based on Malaria risk and Dengue risk 4.12 The Model based on Rainfall as a main climatic factors of Malaria and Dengue 5

6

7

8

MALARIA AND DENGUE RISK AT DISTRICT LEVEL 5.1 District level GIS Data base of study area 5.2 Risk base-map of Malaria at district level: 1991-2000 5.3 Risk base-map of Dengue at district level: 1991-2000 5.4 Risk based on Climatic factors of Malaria (Rainfall) 5.4.1 Rainfall Observation stations 5.4.2 Rainfall based classification of study area 5.4.3 Relation between Rainfall and Malaria cases 5.4.4 Relation between Rainfall and Dengue cases 5.4.5 Relation between Rainfall and Malaria / Dengue with Population

LAND COVER CLASSIFICATION OVER RISK AREA 6.1 From Regional to Local scale study 6.2 GIS Data base for Medical Officer for Health area on the Risk base of Malaria & Dengue 6.3 Malaria risk on MOH area level: 1995- 2000 6.4 Land cover classification over Malaria risk 6.5 Dengue risk on MOH area level: 1996 - 2000 6.6 Land Cover classification over Dengue risk 6.7 Land Cover type percentage of Malaria and Dengue MODEL DEVELOPMENT 7.1 Seasonal behavior of Malaria and Dengue 7.2 Impact of Mosquito Life Cycle 7.3 Model Development 7.4 Malaria Seasonal pattern -Relation with rainfall in Puttalam District 7.5 Malaria Seasonal pattern -Relation Model with rainfall in Puttalam MOH area 7.6 Dengue Seasonal pattern Model-Relation Model with rainfall in Colombo District. 7.7 Dengue Seasonal pattern-Relation Model with rainfall in Colombo MOH area CONCULUSION AND RECOMMENDATIONS

REFERENCES APPENDIXES ACRONYMS

26 34 34 35 36

37 38 39 40 40 41 42 43 44

45 46 48 49 50 51 52

53 54 55 56 57 58 59 60 62 63 95


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……iv

LIST OF FIGURES Figures

Title

Page

2.1

Local Application of Remote Sensing Techniques (LARST)

10

3.1 3.2

Topography of Sri Lanka Wet zone and Dry zone of Sri Lanka

14 16

4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9

Presentation of study Administrative data collection and Input Medical data collection & Input Climatic data collection & Input Physical Environmental Collection & data Input Malaria / Dengue risk distribution Malaria & Dengue with Climatic parameter (Rainfall) Land cover map for Malaria and Dengue Model of Malaria / Dengue with Rainfall

22 23 24 25 26 30 31 32 33

5.1 5.2 5.3

District level GIS Data base of Study area Malaria risk spread map of Sri Lanka Dengue risk spread map of Sri Lanka

37 38 39

5.4.1 5.4.2 5.4.3 5.4.4 5.4.5

Rainfall Observation Stations in Sri Lanka Rainfall classes of Sri Lanka Relation on Rainfall and Malaria cases Relation on Rainfall and Dengue cases Relation between Rainfall and Malaria / Dengue with Population

40 41 42 43 44

6.1 6.2 6.2.1 6.3 6.4 6.5 6.6 6.7

Risk area based on risk distribution map Select the area for MOH area database MOH area map in Puttalam, Gampaha and Colombo district Malaria risk on MOH area level 1995 to 2000 Land cover classification over Malaria highest risk area Dengue risk on MOH area level 1996 to 2000 Land cover classification on Dengue a) Land cover type over Malaria risk area

45 46 47 48 49 50 51 52

b) Land cover type percentage over Malaria risk

52

a) Land cover types over Dengue risk area

52

b) Land cover type percentage over Malaria risk

52

6.8

7.2

Impact of Mosquito Life Cycle

7.4

a.Malaria Seasonal -Relation Model with rainfall in Puttalam District 56 Ideal International E- Publication www.isca.co.in

54

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……v

7.4.

b.Malaria Seasonal – Relation Model with rainfall in Puttalam District According to Mosquito life cycle 56

7.5.

a.Malaria Seasonal-Relation Model with rainfall in Puttalam MOH area 57

7.5.

b.Malaria Seasonal -Relation Model with rainfall in Puttalam MOH area According to Mosquito life cycle 57

7.6.

a.Dengue Seasonal -Relation Model with rainfall in Colomobo District 58

7.6.

b.Dengue Seasonal pattern -Relation with rainfall in Colombo district according to Mosquito life cycle

58

a.Dengue Seasonal pattern -Relation Model with rainfall in Colombo MOH area

59

b.Dengue Seasonal pattern – Relation Model with Rainfall in Colombo MOH are acceding to Mosquito life cycle

59

7.7.

7.7.

Tables 1.1 2.1 3.1

Table 1.1 highlighted Malaria and Dengue have possible change of distribution as a result of climate change Research using remote sensing data to map diseases vectors Population of Districts


3 8 19

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……1

CHAPTER 1 INTRODUCTION 1.1 Background The global incidence of malaria is presently estimated to be more than 300 million cases per year, about half of which are caused by the parasite Plasmodium falciparum and the balance by three other species that infect man, namely P. vivax, P. malariae, and P. ovale. Malaria is responsible for at least one million deaths annually according to the World Health Organization. Many of these occur among very young children in African countries south of the Sahara Desert. In these countries medical treatment is not readily available in rural areas and complications of malaria such as cerebral malaria, which can be fatal, are relatively common. Malaria is a major public health problem in Sri Lanka, with almost 300,000 infections being reported yearly in a population of 16 million. As much as two-thirds of the entire national public health budget is spent on controlling malaria, and the disease constitutes the fourth highest cause of hospital admission in the country. Malaria has made a major impact on the health, economy, education, and general development of the population (Administration Report 1991). Although in the past P. falciparum malaria was a rarity, its vector is now almost as prevalent as that of P. vivax; further, with chloroquine-resistant strains spreading rapidly, malaria promises to become a more serious problem in the future. related to the distribution of vector-borne and other diseases. in 1991 there were 400,263 cases of malaria in Sri Lanka (excluding the North), of which 76,541 were caused by P. falciparum and 323,722 by P. vivax infections. Nineteen deaths were directly attributed to malaria in Sri 1991. Sri Lanka is divided into three climatic zones based on rainfall (Figure 2). The dry zone receives < 2,000 mm of rain every year, mainly from the northeast monsoon (OctoberJanuary). The wet zone receives > 2,500 mm of rain from the southwest monsoon (MayJuly) as well as from the northeast monsoon. An intermediate zone with mixed properties lies between the dry and wet zones. Malaria has traditionally been endemic in the dry and intermediate zones. In more recent times, new agricultural settlements, increasing population density, and environmental degradation have resulted in the spread of malaria to many areas in the wet zone. Occasional malaria outbreaks owing to local transmission have occurred in the suburbs of Colombo (Ragama) and Kandy (Waratenne). Only the high hill country appears to be entirely free of malaria transmission. An increase in internal travel tends to favour the spread of malaria within the country. Hence, it is not uncommon to find patients in Colombo or Nuwara Eliya hospitals who have contracted malaria during pilgrimage to Anuradhapura or Kataragama. Rainfall is a critical factor in determining the occurrence of malaria epidemics. Less than normal rainfall in the wet zone results in the drying up of rivers and formation of pools in river beds. These are favorite breeding sites of mosquitoes of the genus Anopheles that transmit malaria. Excessive rain in the dry zone, which results in the formation of large numbers of surface pools, also favours increased malaria transmission.



A dengue fever epidemic is causing widespread anxiety in Sri Lanka According to official figures, up to the end of May,2000, 827 suspected dengue cases had been reported in Sri Lanka this year. By November 26, the figure had reached, 7,177, with 4,972 cases reported during November alone. Forty victims have died, with the worst-affected being children under 15 years of age. The casualty rate is the greatest since the disease was first reported in Sri Lanka in 1965, far higher than the previous record—last year's total of 1,699. Dengue fever or dengue hemorrhagic fever is a viral disease carried by mosquitoes. The peak incidence of the disease is generally after the monsoon season, when the mosquito population increases because of the poor environmental and sanitation conditions in Sri Lanka.It is impossible to obtain a full picture of the mosquito problem because no systematic mosquito control and research systems exist. But random surveys by the Medical Research Institute and the Anti Filarial Campaign have revealed breeding levels of the two main dengue-carrying mosquito species, Aedes Aegypti and Aedes Albopictus, far in excess of World Health Organisation risk guidelines.One Colombo suburb, Boralesgamuwa, had a Breteau Index reading of 58 for Aedes Aegypti, compared to the usual Colombo city reading of 20. The WHO warns that a reading of more than 5 indicates a dengue risk and 50 signifies a high risk. In the Matara area, the readings were 15 for Aedes Aegypti and 53 for Aedes Albopictus. According to a recent report, Sri Lankans have sought to avoid mosquito bites by burning 1,110 metric tons of mosquito coils worth 102.28 billion rupees up to August this year. Working class and rural poor families are worst affected. The wealthy minority lives comfortably in air-conditioned homes. Although the media has expressed concern about the rapid spread of dengue, none of the reports have discussed the social conditions responsible—poverty, unplanned urbanization driven by profit, poor sanitation and a drastically curtailed public health service. Even in the main cities of Sri Lanka, uncovered garbage dumps, open pits and potholes filled with polluted water, stagnant canals and sewer channels are common sights. Many people live in shanties and slums, which are congested and lack sanitation. These areas are paradises for mosquitoes. Yet poverty-stricken people often cannot even afford mosquito nets to cover themselves while sleeping.

Climatic impact of Vector borne diseases Above details indicate that there are relation Malaria and Dengue with Climate especially with rainfall in Sri Lanka. Malaria rate may be increased on northeast and southwest monsoon periods and also dry seasonal of the country. Especially dengue increase on southwest monsoon periods and spread rapidly because of poor sanitation condition and sewerage system.



Table 1.1 highlighted Malaria and Dengue have possible change of distribution as a result of climate change.

Geographic information system (GIS), which is able to read, process, analyze, and present spatially-related data for effective interpretation and use for a variety of environmental and resource management purposes. Within the context of tropical vector-borne disease forecasting and control, such a system would enable the presentation of temporal and spatial dynamics of the disease, in a meaningful way, to planners responsible for national and regional control strategies. Real-time information relevant to potential surges in disease transmission could be included, enabling the initiation of rapid response strategies. Advances in computer processing and in geographic information system and global positioning system technologies facilitate integration of remotely sensed environmental parameters with health data so that models for disease surveillance and control can be developed. For most environmental modeling projects, GIS are seen as convenient and well-structured databases for handing the large quantities of spatial data demanded of them. GIS will also become important in model building, validation, and operation.



1.2 Rationale of Study Climatic patterns of Malaria and Dengue and its parameters behaviors are important to monitor, plan and improve early warming system in the country to beware of the above two vector borne diseases. Other hand studying of Environmental condition through Land cover data of highest risk area give comprehensive picture of diseases spread threat with comparing climatic seasonal patterns. It can be planned to control Malaria and Dengue spread through Socio-Economic condition if we have the early warming system of Climatic and Environmental condition. GIS system is a decision sport system to plan, monitor and analysis data and easily can be updated and validation modeling. There has been increasing studying in the field of vector borne health diseases and its control strategies using GIS techniques. Therefore it is important to use GIS techniques to model the Malaria and Dengue statues in Sri Lanka because spread of both diseases by bit of mosquitoes but different climatic and environmental background. Then Remotely sensing data can be applied to monitor the environmental condition because it is only way to detect medium to large-scale areas environmental background without spending long time and easily can be detected that is difficulties to reach on the ground. After Remote sensing can be integrated with GIS, which is accurate and time saving method.

1.3 Problem statement Almost all research carried out with respect to malaria and Dengue infections, incorporate factors such as land cover, swampy lands/forests, temperature, and rainfall. However any one research did not involve to evaluate two diseases differences factors and distribution patterns in Sri Lanka. Other hand there is similar source to spread above both diseases because of mosquitoes but very different climatic and Land cover patterns. Therefore it is more reliable to apply RS/GIS to evaluate Climatic and Land cover condition of above both vector borne diseases. In this particular study, Climatic and Land cover data would be used in order to come up with a more reliable model that could be used against the present malaria & Dengue situation in Sri Lanka.

1.4 Objective of the study The study area was selected as Sri Lanka and the following were to be carried out to suit the major Objectives. Objective of this study is to identify the malaria and dengue diseases spread in relation to parameters such as climatic, environmental. In order to identify such relationships rainfall distributions, and land cover information from remote sensing data, Malaria and Dengue cases in different districts and sub units are to be improved in to a GIS model. Ten years data of Rainfall in two hundred stations and Malaria and Dengue cases of ten years are to be modeled in the district levels. In the second level called Medical Officer for Health area (MOH area) Data will be collected in six years monthly reported Malaria Ideal International E- Publication www.isca.co.in


and five years monthly reported Dengue cases in MOH area wise. This study carries on two levels in the study area. 1.

a.) To create Risk base-map of Malaria at district level. b.) To create Risk base-map of Dengue at district level.

2.

a.) To characterize Malaria risk levels in relation to rainfall patterns. b.) To characterize Dengue risk levels in relation to rainfall patterns.

3.

a.) To analyze Land cover in the highest Malaria risk region. b.) To analyze Land cover in the highest Dengue risk region.

4.

a.) To build a Malaria risk model based on rainfall. b.) To build a Dengue risk model based on rainfall.

1.5 Scope of the study, its limitations and significance. The study carries a comprehensive analysis to determining the Risk levels distribution of Malaria and Dengue of last decade in Sri Lanka using RS/GIS techniques and also mention how to its behaviors with climatic and Land cover. There are build four model to get past patterns of diseases with climatic and Land cover. In this reported data may be some monthly seasonal patterns to spread the diseases to be discuses in this study and study is focus to evaluate last decade reported data how to distribute in country where are the area most suffering the both diseases in long time, how to behaviors of climatic parameter to increase or reduce the both diseases and what are its Land cover to make environmental to increase the diseases in risk area. During the data collection period it has understand the availability of temperature data and humidity data not sufficient to compare with the more than two hundred rainfall stations. In the Meteorological department of Sri Lanka has being observing only twenty stations for collect the temperature and humidity data in Sri Lank. Requested Satellite image for the study have twenty-three meter resolution that is suitable resolution for Land cover classification such as above levels.



CHAPTER 2 LITERATURE REVIEW 2.1 Dengue fever and climate Scientists in New Zealand have found that outbreaks of the tropical disease dengue in some South Pacific islands are directly related to climate change in the region, raising fears that "global warming could lead to outbreaks of tropical disease in new locations." Simon Hales and his colleagues at the Wellington School of Medicine in New Zealand, publishing their findings in the medical journal The Lancet (``Dengue Fever in the South Pacific: driven by El Niño Southern Oscillation?'' 1996;348:1664-65), attributed the mosquito-borne disease, which generally occurred after floods in tropical zones such as Southeast Asia, China and Cuba, to climate change caused by the El Niño Southern Oscillation. El Niño is a pattern of currents and weather that affect world climate. Hales compared data from previous studies on dengue to El Niño and its effects, which demonstrated that the higher the Southern Oscillation Index the greater the instances of dengue fever. "The result is consistent with other studies and is biologically plausible, since the mosquitoes that transmit dengue are sensitive to temperature and rainfall," the scientists wrote. "These findings suggest that dengue will be an increasing problem if the global climate continues to warm, as predicted by the Intergovernmental Panel on Climate Change," the report said. 2.2 Malaria transmission in Sri Lanka using GIS Dhanapala (1998) used mapping used to study potential malaria transmission in Sri Lanka. Zones of perennial and seasonal malaria transmission and malaria-free zones were defined under present climate conditions. These zones were then correlated with a moisture index using historical temperature and precipitation data, and the threshold moisture indiceswhich defined the three zones were determined. Temperature outputs from two global circulation models were combined with assumptions of either increased or decreased total precipitation (due to uncertainty) and moisture indices were calculated for each pixel of GCM output. The IDRISI GIS was used to store and display this geographically based data. Threshold moisture indices between current malaria transmission zones were applied to the new map and new borders of malaria transmission zones were estimated. It was estimated that the area of malaria-free zone might decrease by 45.6% to 55.1% and the area of the perennial transmission zone would increase by 45.1% to 65.1%. Further analyses could assess the implications of the shifts in transmission zones for specific population centres, and begin to formulate possible adaptive strategies. Clear limitations to this method include the fact that other geographic and anthropogenic factors affect malaria transmission, for example, pesticide use. It is not possible to account for these other factors either in the present situation or in the projections. Nonetheless, this exercise provides a number of benefits. First, the relation between areas potentially vulnerable to increases in malaria transmission can be estimated and compared to existing



population centres. Second, areas, which may have decreases in malaria transmission, can be noted as well. 2.3 Geographic Information Systems and Health diseases Geographic information systems (GIS) are defined here as computer-based systems for entering, storing, analyzing, and displaying digital geo-referenced data sets. The problems arising from the use and development of GIS in developing countries, and some advice on their solutions, are outlined by Hastings and Clark (1991), in a special issue of the International Journal of Geographical Information Systems devoted to the consideration of GIS within a developmental context. Rajan (1991) has, with the support of the Asian Development Bank, produced a valuable overview of the use of remote sensing in GIS. The document includes a useful section covering some of the key issues and problems surrounding their use, such as increasing commercialization, human resources, institutional capacity, training, and technology transfer. A consultation report to FAO on the use of GIS in strengthening information systems for veterinary services in developing countries offers a comprehensive outline of hardware requirements and software considerations (Perry and Kruska 1993). At present, GIS are seen primarily as research tools in the field of vector-borne disease; indeed, they will become an increasingly important research tool as geographic databases, models, and analysis procedures continue to develop at a rapid pace. However, the use of GIS in decision support has also become an area of growing interest. Spatially referenced, interactive models have been developed to simulate the broader effects of development policy in Senegal (Engelen et al. 1992; Connor and Allen 1994). A system for the national control of foot and mouth disease in New Zealand (Morris et al. 1993) is probably the best example of how geographic databases and disease epidemiology models can be integrated into a decision support system. GIS designers are also beginning to provide analytical decision support tools as part of their options, and such systems are being promoted for a more participatory planning process (Eastman et al. in press; Hutchinson and Toldano in press).

2.4 Remote Sensing Data to identify and monitor the Health Diseases In 1985, the National Aeronautics and Space Administration (NASA) initiated the Biospheric Monitoring and Disease Prediction Project, the aim of which was to determine if remotely-sensed data could be used to identify and monitor environmental factors that influence malaria vector populations. Initial studies supported by this project used high resolution images from the LANDSAT (American commercial Earth observation satellite) TM sensor to monitor the development of canopy cover in Californian rice fields. Changes in rice canopy cover over the season were successfully used to predict fields with high or low mosquito densities (Wood et al. 1991). Remote sensing data enable scientists to study the earth's biotic and abiotic components. These components and their changes have been mapped from space at several temporal and spatial scales since 1972. A small number of investigators in the health community have explored remotely sensed environmental factors that might be associated with diseasevector habitats and human transmission risk. However, most human health studies using Ideal International E- Publication www.isca.co.in


remote sensing data have focused on data from Landsat's Multispectral Scanner (MSS) and Thematic Mapper (TM), the National Oceanic and Atmospheric Administration (NOAA)'s Advanced Very High Resolution Radiometer (AVHRR), and France's Système Pour l'Observation de la Terre (SPOT). In many of these studies (Table 2.1) remotely sensed data were used to derive three variables: vegetation cover, landscape structure, and water bodies. Table 2. 1. Research using remote sensing data to map disease vectorsa Disease

Vector

Location

Sensor

Ref.

Dracunculiasis

Cyclops spp. Cyclops spp. Culiseta melanura

Benin Nigeria Florida, USA

TM TM TM

1 2 3

Culex pipiens Cx. pipiens Phlebotomus Papatasi Ixodes scapularis I. scapularis Anopheles Albimanus An. Albimanus An. Albimanus An. Albimanus An. Spp.

Egypt Egypt SW Asia

AVHRR TM AVHRR

4 5,6 7

Eastern equine Encephalomyelitis Filariasis Leishmaniasis Lyme disease Malaria

New York, USA TM Wisconsin, USA TM Mexico TM

8, 9 10 11

SPOT SPOT TM AVHRR, Meteosat

Aedes & Cx. Spp. Cx. spp.

Belize Belize Mexico Gambia Mexico Kenya Kenya

Cx. spp.

Senegal

SPOT, AVHRR

Biomphalaria spp. Glossina spp. Glossina spp. Glossina spp. Glossina spp.

Egypt Kenya, Uganda Kenya West Africa Africa

AVHRR AVHRR TM AVHRR AVHRR

Glossina spp.

Southern Africa

AVHRR

12 13 14 15, 16 17, 18 19, 20 21 22 23 24 25 26 27 28

An. Albimanus

Rift Valley fever

Schistosomiasis Trypanosomiasis

a

TM

AVHRR TM, SAR

See Appendix B explanation of sensor acronyms

For many parts of the world, it is very difficult to acquire high quality, geographically referenced data on ecological factors related to disease transmission (such as types of vegetation or temperature and composition of surface waters). Political or geographic obstacles may impede the collection of such data through traditional field methods. Technological advances have enabled the use of remote sensing devices to assist in providing these data. Using low-flying aircraft or satellites, these devices are able to measure either directly or indirectly water and air temperatures, vegetative cover, and even water flows. Those entities measured indirectly often require significant initial fieldwork to Ideal International E- Publication www.isca.co.in


establish the relations between factors, which can be measured directly, such as light absorption, and the desired entity, such as vegetative cover. An example of the use of remote sensing for vector-borne disease is the study on human African trypanosomiasis by Rogers and colleagues. Vegetation indices (specifically the NDVI, normalized vegetation index) obtained from satellite images and tsetse fly abundance have been correlated for regions in Central and East Africa (Rogers and Randolph, 1991; Hay et al., 1996).Two important points are evident. First, the ability to use remote sensing for a given area is dependent on sufficient ground-based study in that area. The linkage of remotely sensed vegetation indices and vector populations was made possible by previous studies associating climate factors such as saturation deficit with vector survival. By associating both vector survival and vegetation indices to the same climate variables, the remotely sensed data could then be applied to human disease prediction. The second point is that different local species of vector may have very different responses to changes in climate. In this study, it was shown that the population of the species Glossina palpalis increased with increasing vegetation index (indicative of greater moisture), while the species G. tachinoides decreased in number with increasing vegetation index. This emphasises the need for regional models developed along with expert judgment.

2.5 Local Application of Remote Sensing Techniques (LARST) for Malaria detection A national malaria database cannot predict all the areas that are having problems. The population in Belize, like that of most third world nations, is expanding at an exponential rate and people constantly erect new houses. Moreover, many people do not report the disease when they get sick because it is hard to diagnose on sight. To augment the database, the researchers have been working to create satellite maps of Belize that highlight the areas where the major malaria-carrying mosquitoes breed. With such maps, the government could identify regions of potential risk where there aren't many records. Since satellite maps can be updated on a regular basis, Belizians would also be able to gather information on land use, land cover, where people are and what new developments have taken place. Creating a system to map Belize's mosquito-ridden areas is far more complicated than manipulating a database. The first step involved identifying the types of environments in which each species of mosquito thrives. To find where these mosquitoes breed best, the Uniformed Services team went out into the wetlands and rivers of Belize and took samples of the various species of mosquitoes and their larvae. They looked for correlations between habitat variables, such as types of plants and water depth, and the number of mosquitoes and larvae present. Generally, the diet of the male mosquito determines the type of vegetation a species prefers. Water, shade and protection from predators are also factors. (Rejmankova et al., 1998)

With the exception of one river species, the team determined where all the major malaria carriers breed. They found that Anopheles albimanus is abundant in reed marshes with Ideal International E- Publication www.isca.co.in


flooded mats of blue-green algae. (Montgomery et al., 1996) The Anopheles punctimacula, on the other hand, breeds best in swamp forest with lots of rotting organic matter in the water. The Anopheles vestitipennis were found in both swamp forests and in reedy marshes. (Rejmankova et al., 1998) The next step was to locate these areas with a satellite imaging system. The Uniformed Health Services team used data from Landsat 5 and the French Systeme. Local Application of Remote Sensing Techniques (LARST)

Figures: 2.1 Local Application of Remote Sensing Techniques (LARST)

Methodology * Field survey to identify good breeding grounds for mosquitoes. * The highlighted areas (marsh, flooded forest) are likely filled with mosquitoes ( Supervised Classification by ER mapper ) * Use these maps to locate houses and communities that are exposed to large numbers of mosquitoes ( early warming ) * To allocate resources for mosquiot control, including insecticides and bed nets. Satellite imagery allows scientists to map the environment. Regions that flood seasonally, or have standing water, are good breeding grounds for mosquitoes. The yellow circle in this Landsat image indicates an area around a cluster of houses with multiple malaria cases, and was used to generate a landcover map. The data contained in the image to the left was used to generate the map of landcover types below it. The highlighted areas (marsh, flooded forest) are likely filled with mosquitoes. Scientists use these maps to locate houses and communities that are exposed to large numbers of mosquitoes. They then know where to allocate resources for mosquiot control, including insecticides and bed nets. "With these satellites, [scientists] can essentially look for the reflectance values, or spectral signatures of each of the types of habitats," said Brian Montgomery. He's a remote epidemiologist at NASA's Goddard Space Flight Center who worked for Roberts during the early 1990s. In very basic terms, the researchers locate an area on an image of Belize that they know, for instance, contains marshes with blue-green algae. Using computers, they then identify those specific colors that distinguish this type of marsh or wetland from anything else covering the ground. They can then take this color combination and apply it to uncharted areas of Belize to find other reed marshes with blue-green algae. Similar classifications can be done with all the basic ground covers—cropland, urban areas, forest swamp, rivers and roads. The end result is a satellite map showing a wide range of land covers and mosquito habitats across large areas of Belize. (Roberts et al., 1996)



2.6 Use of Numerical models for Health Diseases The modelling approach is orientated toward a vertical integration of global atmos-pheric disturbances and their respective health effects. The models try to cover as much as possible of the cause-effect relationship with respect to global atmospheric changes and human health. In the vector-borne disease model, the dynamics of malaria, schistosomiasis, and dengue are simulated in relation to climate changes. Relationships between temperature, precipitation, and vector characteristics are based on a variety of field and laboratory data. Changes in transmission dynamics of malaria and schistosomiasis are modelled using the basic infectious disease models described in Anderson and May (1991); for dengue a well-validated, dynamic life-history model of dengue transmission (Focks et al., 1993a,b) is used. Recognising the need for continu-ing cross-validation of large-scale and small-scale studies (Root and Schneider, 1995), simulations have been performed of the transmission potential of malaria in Zimbabwe and dengue in five cities (Bangkok, San Juan, Mexico City, Athens, and Philadelphia) (Focks, 1993 a and b; Jetten and Focks, 1997; Patz et al., 1998). The historical data available for these locations are used for validation, i.e., testing the performance of the model.[ John M. Balbus et al ] 2.7 Geographical information systems and dynamic modeling GIS data usually are voluminous. Some of the spatial information is static, but other information is actually dynamic. It takes time and is expensive to update voluminous information. Therefore data in GIS tend to be static, although the dynamic information should be dynamic. Some of the information, e.g. on soil types or microclimates, has been collected during years or even decades. Some date, e.g. several characteristics of microclimate, are longterm average values. By definition it takes time to determine long-term averages. Rapid updates are technically all but impossible for such types of information. Hence GIS data are often crucially deviating from the real situation. Mechanisms or procedures are needed to achieve a faster ―tracking‖ of the real situation by the data in the GIS. The different types of dynamic maps described to created by combinations between different types of models and different kinds and levels of details processed with the GIS (first results: Grossmann et al. 1984) The model dynamics are combined with GIS held ― base maps‖ to produce time series of maps, so called ―Dynamic Maps‖. Base maps combine spatial features, which are locally important for the dynamic process and are used to either modify or even form the dynamics. Different types of models need different types of GIS- held base maps and are adequate for different types of problems. Combinations of dynamic models and Geographical Information Systems (GIS) have a vast potential to solve problems. Deficiencies and advantages of GIS and dynamic models are described. A multifaceted description of complex systems allows three different types of combining dynamic models and GIS. Different classes of dynamic models are used within these combinations. These are:-1 complex aggregated dynamic feedback models.2 simple generic dynamic models ( in particular object oriented models ) 3.models of physics based on partial differential equations. The resulting three different combinations are adequate for different types of problems.



2.8 GIS and spatial epidemic models Currently, few GIS offer much capability for spatial epidemic models of the sort envisaged in Fig.17.3. As described in Goodchild et al (1992) and summarized in Gatrell ( 1995 ) , there are several possible approaches. While the full integration of spatial models into a proprietary GIS will eventually be achieved, other intermediate solutions are possible until that goal is reached. These include developing loose coupling between modeling and statistical packages and GIS, with data moved between components in reasonably seamless way ; the EPI-INFO/EPI-MAP combination produced by the United States Centers for Disease Control and Prevention is an example ( Dean et al 1994a, 1994b ). Or we might have close coupling, either seeking to embed statistical and modeling capability within the GIS ( for example, by using the AML language of ARC/INFO ) or by adding a restricted range of GIS tools into an analytical package. The pioneering work on close coupling by adding an analytical capability to standard GIS packages is proceeding at a number of centers. These include the National Center for Geographic Information and Analysis (NCGIA) at Buffalo in the USA and, associated with work on disease incidence where the realization of the spatial process may be treated as a point pattern ( such as possible cancer hot spots near point sources of environmental pollution), at Lancaster University in the UK. Coupling within the proprietary GIS ARC/INFO system has been provided both for exploratory tools for detecting spatial clusters of disease and for a confirmatory raised-incidence model ( Gatrell and Rowlingson 1993). Each of these methods is embodied in external FORTRAN code but, within these programs, calls are made to ARC/INFO routines, and point and polygon coverages are thereby accessed.



CHAPTER 3 STUDY AREA 3.1 Location Sri Lanka is situated in the center of the Indian Ocean and located very close to the Indian Sub Continent lying between Northern latitudes 5 55` and 9 50` and Eastern longitudes 79 42` and 81 52`, The Island is separated from India by the 35 km wide Palk straight . Sri Lanka has a land area of 65,610 Sq. km , and maximum length of 432 km and a width of 224 km. 3.2 Climatic Condition Sri Lanka's position between 5 and 10 north latitude endows the country with a warm climate, moderated by ocean winds and considerable moisture. The mean temperature ranges from a low of 15.8° C in Nuwara Eliya in the Central Highlands (where frost may occur for several days in the winter) to a high of 29° C in Trincomalee on the northeast coast (where temperatures may reach 37° C). The average yearly temperature for the country as a whole ranges from 26° C to 28° C. Day and night temperatures may vary by 4 to 7 . January is the coolest month, causing people, especially those in the highlands, to wear coats and sweaters. May, the hottest period, precedes the summer monsoon rains. The rainfall pattern is influenced by the monsoon winds of the Indian Ocean and Bay of Bengal and is marked by four seasons. The first is from mid-May to October, when winds originate in the southwest, bringing moisture from the Indian Ocean. When these winds encounter the slopes of the Central Highlands, they unload heavy rains on the mountain slopes and the southwestern sector of the island. Some of the windward slopes receive up to 250 centimeters of rain per month, but the leeward slopes in the east and northeast receive little rain. The second season occurs in October and November, the inter monsoonal months. During this season, periodic squalls occur and sometimes tropical cyclones bring overcast skies and rains to the southwest, northeast, and eastern parts of the island. During the third season, December to March, monsoon winds come from the northeast, bringing moisture from the Bay of Bengal. The northeastern slopes of the mountains may be inundated with up to 125 centimeters of rain during these months. Another inter monsoonal period occurs from March until mid-May, with light, variable winds and evening thundershowers. Humidity is typically higher in the southwest and mountainous areas and depends on the seasonal patterns of rainfall. At Colombo, for example, daytime humidity stays above 70 percent all year, rising to almost 90 percent during the monsoon season in June. Anuradhapura experiences a daytime low of 60 percent during the inter monsoonal month of March, but a high of 79 percent during the November and December rains. In the highlands, Kandy's daytime humidity usually ranges between 70 and 79 percent.



3.3 Topography of Sri Lanka

Figures 3.1: Topography of Sri Lanka

Extensive faulting and erosion over time have produced a wide range of topographic features, making Sri Lanka one of the most scenic places in the world. Three zones are distinguishable by elevation: the Central Highlands, the plains, and the coastal belt (see fig. 3 .1). The south-central part of Sri Lanka--the rugged Central Highlands--is the heart of the country. The core of this area is a high plateau, running north-south for approximately sixty-five kilometers. This area includes some of Sri Lanka's highest mountains. (Pidurutalagala is the highest at 2,524 meters.) At the plateau's southern end, mountain ranges stretch 50 kilometers to the west toward Adams Peak (2,243 meters) and 50 kilometers to the east toward Namunakuli (2,036 meters). Flanking the high central ridges are two lower plateaus. On the west is the Hatton Plateau, a deeply dissected series of ridges sloping downward toward the north. On the east, the Uva Basin consists of rolling hills covered with grasses, traversed by some deep valleys and gorges. To the north, separated from the main body of mountains and plateaus by broad valleys, lies the Knuckles Massif: steep escarpments, deep gorges, and peaks rising to more than 1,800 meters. South of Adams Peak lie the parallel ridges of the Rakwana Hills, with several peaks over 1,400 meters. The land descends from the Central Highlands to a series of Ideal International E- Publication www.isca.co.in


escarpments and ledges at 400 to 500 meters above sea level before sloping down toward the coastal plains. Most of the island's surface consists of plains between 30 and 200 meters above sea level. In the southwest, ridges and valleys rise gradually to merge with the Central Highlands, giving a dissected appearance to the plain. Extensive erosion in this area has worn down the ridges and deposited rich soil for agriculture downstream. In the southeast, a red, lateritic soil covers relatively level ground that is studded with bare, monolithic hills. The transition from the plain to the Central Highlands is abrupt in the southeast, and the mountains appear to rise up like a wall. In the east and the north, the plain is flat, dissected by long, narrow ridges of granite running from the Central Highlands. A coastal belt about thirty meters above sea level surrounds the island. Much of the coast consists of scenic sandy beaches indented by coastal lagoons. In the Jaffna Peninsula, limestone beds are exposed to the waves as low-lying cliffs in a few places. In the northeast and the southwest, where the coast cuts across the stratification of the crystalline rocks, rocky cliffs, bays, and offshore islands can be found . Sri Lanka's rivers rise in the Central Highlands and flow in a radial pattern toward the sea. Most of these rivers are short. There are sixteen principal rivers longer than 100 kilometers in length, with twelve of them carrying about 75 percent of the mean river discharge in the entire country. The longest rivers are the Mahaweli Ganga (335 kilometers) and the Aruvi Aru (170 kilometers). In the highlands, river courses are frequently broken by discontinuities in the terrain, and where they encounter escarpments, numerous waterfalls and rapids have eroded a passage. Once they reach the plain, the rivers slow down and the waters meander across flood plains and deltas. The upper reaches of the rivers are wild and usually unnavigable, and the lower reaches are prone to seasonal flooding. Human intervention has altered the flows of some rivers in order to create hydroelectric, irrigation, and transportation projects. In the north, east, and southeast, the rivers feed numerous artificial lakes or reservoirs (tanks) that store water during the dry season.

3.4 Ecological Zones The pattern of life in Sri Lanka depends directly on the availability of rainwater. The mountains and the southwestern part of the country, known as the "wet zone," receive ample rainfall (an annual average of 250 centimeters). Most of the southeast, east, and northern parts of the country comprise the "dry zone, which receives between 120 and 190 centimeters of rain annually. Much of the rain in these areas falls from October to January; during the rest of the year there is very little precipitation, and all living creatures must conserve precious moisture. The arid northwest and southeast coasts receive the least amount of rain--60 to 120 centimeters per year-- concentrated within the short period of the winter monsoon (see fig. 3.2).



Wet Zone and Dry Zone of Sri Lanka

Figures 3.2: Wet Zone and Dry Zone of Sri Lanka The natural vegetation of the dry zone is adapted to the annual change from flood to drought. The typical ground cover is scrub forest, interspersed with tough bushes and cactuses in the driest areas. Plants grow very fast from November to February when rainfall is heavy, but stop growing during the hot season from March to August. Various adaptations to the dry conditions have developed. To conserve water, trees have thick bark; most have tiny leaves, and some drop their leaves during this season. Also, the topmost branches of the tallest trees often interlace, forming a canopy against the hot sun and a barrier to the dry wind. When water is absent, the plains of the dry zone are dominated by browns and grays. When water becomes available, either during the wet season or through proximity to rivers and lakes, the vegetation explodes into shades of green with a wide variety of beautiful flowers. Varieties of flowering acacias are well adapted to the arid Ideal International E- Publication www.isca.co.in


conditions and flourish on the Jaffna Peninsula. Among the trees of the dry-land forests are some valuable species, such as satinwood, ebony, ironwood, and mahogany. In the wet zone, the dominant vegetation of the lowlands is a tropical evergreen forest, with tall trees, broad foliage, and a dense undergrowth of vines and creepers. Subtropical evergreen forests resembling those of temperate climates flourish in the higher altitudes. Montane vegetation at the highest altitudes tends to be stunted and windswept. Forests at one time covered nearly the entire island, but by the late twentieth century lands classified as forests and forest reserves covered only one-fifth of the land. The southwestern interior contains the only large remnants of the original forests of the wet zone. The government has attempted to preserve sanctuaries for natural vegetation and animal life, however. Ruhunu National Park in the southeast protects herds of elephant, deer, and peacocks, and Wilpattu National Park in the northwest preserves the habitats of many water birds, such as storks, pelicans, ibis, and spoonbills. During the Mahaweli Garga Program of the 1970s and 1980s in northern Sri Lanka, the government set aside four areas of land totalling 190,000 hectares as national parks. 3.5 Land Use and Settlement Patterns The dominant pattern of human settlement during the last 2,500 years has consisted of village farming communities. Even in the 1980s, the majority of people lived in small villages and worked at agricultural pursuits. Traditional farming techniques and life-styles revolve around two types of farming--"wet" and "dry"--depending upon the availability of water. The typical settlement pattern in the rice-growing areas is a compact group of houses or neighborhood surrounding one or several religious centers that serve as the focus for communal activities. Sometimes the houses may be situated along a major road and include a few shops, or the village may include several outlying hamlets. The life-sustaining rice fields begin where the houses end and stretch into the distance. Some irrigated fields may include other cash crops, such as sugarcane, or groves of coconut trees. Palmyra trees grow on the borders of fields or along roads and paths. Individual houses also may have vegetable gardens in their compounds. During the rainy seasons and thereafter, when the fields are covered by growing crops, the village environment is intensely verdant. The nature of agricultural pursuits in Sri Lanka has changed over the centuries and has usually depended upon the availability of arable land and water resources. In earlier times, when villagers had access to plentiful forests that separated settlements from each other, slash-and-burn agriculture was a standard technique. As expanding population and commercial pressures reduced the amount of available forestland, however, slash-and-burn cultivation steadily declined in favor of permanent cultivation by private owners. Until the thirteenth century, the village farming communities were mainly on the northern plains around Anuradhapura and then Polonnaruwa, but they later shifted to the southwest, wide expanses of the northern and eastern plains were sparsely populated, with scattered villages each huddled around an artificial lake. The Jaffna Peninsula, although a dry area, is densely populated and intensively cultivated. The southwest contains most of the people, and villages are densely clustered with little unused land . In the Central Highlands around Kandy, villagers faced with limited flat land have developed intricately terraced hillsides Ideal International E- Publication www.isca.co.in


where they grow rice. In the 1970s and 1980s, the wet cultivation area was expanding rapidly, as the government implemented large-scale irrigation projects to restore the dry zone to agricultural productivity. The coastal belt surrounding the island contains a different settlement pattern that has evolved from older fishing villages. Separate fishing settlements expanded laterally along the coast, linked by a coastal highway and a railway. The mobility of the coastal population during colonial times and after independence led to an increase in the size and number of villages, as well as to the development of growing urban centers with outside contacts. In the 1980s, it was possible to drive for many kilometers along the southwest coast without finding a break in the string of villages and bazaar centers merging into each other and into towns.

3.5 Population of the Study Area Urbanization has affected almost every area of the country since independence. Local market centers have grown into towns, and retail or service stores have cropped up even in small agricultural villages. The greatest growth in urban population, however, has occurred around a few large centers. In 1981 the urbanized population was 32.2 percent in Trincomalee District and 32.6 percent in Jaffna District, in contrast to the rural Moneragala District where only 2.2 percent of the people lived in towns. Colombo District, with 74.4 percent urban population, experienced the largest changes. Between 1881 and 1981, the city of Colombo increased its size from 25 to 37 square kilometers and its population from 110,502 to 587,647. ( Table 3.1 Population by district)



Population by district

District

Area sq.km

Population (thousands) 1981

1994

Colombo Gampaha

676 1,341

1,699 1,391

2,057 1,708

Kalutara

1,576

830

938

Kandy

1,917

1,048

1,221

Matale

1,952

357

423

Nuwara Eliya

1,706

604

671

Galle

1,617

815

955

Matara

1,270

644

754

Hambantota

2,496

424

516

831

-

Jaffna

929

Kilinochchi

1,205

Mannar

1,880

106

-

Vavuniya

1,861

95

-

Mullaitivu

2,415

77

-

Batticaloa

2,610

330

-

Ampara

4,222

389

-

Trincomalee

2,529

256

-

Kurunegala

4,624

1,212

1,378

Puttalam

2,882

493

601

Anuradhapura

6,664

588

676

Polonnaruwa

3,077

262

332

Badulla

2,827

641

750

Monaragala

5,508

274

365

Ratnapura

3,236

797

917

Kegalle

1,685

685

760

Table 3.1 Population by Districts



CHAPTER 4 METHODOLOGY 4.1 Research Framework for Methodology Research framework is divided into three main parts: Data Input, Methodology and Output. Data Input consists of four main data acquisition sector. Administrative data, Health data, Climatic data and Physical Environmental data. In the second step all acquisition data will be manipulated and analyzed by using Statistic Analysis (SA), Geographical Information System (GIS) and Remote Sensing (RS). Finally Objectives 1:Malaria Risk Distribution / Dengue Risk Distribution at district base, Objective 2: Characterize Malaria / Dengue risk levels in relation to rainfall patterns 3: Analyze Land cover in the highest Malaria / Dengue risk region 4: Build a Malaria / Dengue risk model based on rainfall. Figures 4.1 illustrate General framework of this methodology.

4.2 Data Acquisition. 4.2.1 Administrative data: There are twenty-four administration districts in Sri Lanka all districts should be digitized to the GIS database and MOH area of Puttalam, Gampaha & Colombo district which is consist of thirty two MOH areas have been digitized. 4.2.2 Medical data Reported Malaria and Dengue cases data from 1991 to 2000 ( ten years ) to be collected from Department of Health, Medical Research Institute , Department of Senses and Statistic and District Medical Offices in Sri Lanka .Anti Malaria Campaign, and Epidemiological unit of Sri Lanka. Data should be monthly cases in district and MOH area level. Figures 4.3. 4.2.3 Climatic Data. Total monthly Rainfall data of 1991 to 2000 to be collected from Metrological department of Sri Lanka. Approximately two hundred Stations cover the whole country. Figures 4.4. indicate the procedure of climate data input in this research .


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……21 4.2.4 Physical Environmental data At least two satellites images of study area are required to analyze the land cover classification over the Malaria and Dengue highest risk area. Therefore it is expected to collect following images of study area from Department survey of Sri Lanka.Figures 4.5. a.) Satellites Imageries: Characteristic of IRS data: Three axis, body stabilized Remote Sensing Satellite with Polar orbit, Sun synchronous , 817 km altitude with equatorial crossing time of 10.30 a.m. Sensors:

IRS IC LISS 111 four bands image Band 2 ( 0.52-0.59 u ) Band 3 ( 0.62-0.68 u ) Band 4 ( 0.77-0.86 u ) Band 5 ( 1.55-1.70 u )

SLISS- 111 23.5 m for B2, B3, B4; 70.5 m for

b.) Topography maps: 1:50,000 Scale Topography maps and Land use map 1:100000 of Malaria / Dengue High Risk area of Sri Lanka can be collected from Survey Department of Sri Lanka. Ideal International E- Publication www.isca.co.in


Research Framework of Study

Data Input

Administrative Data

Medical Data

Climatic Data

Physical Environmental Data

Data Processing GIS-Database

SA

GIS

RS

Out put

Risk base map of Malaria

Risk base map of Dengue

Characterize Malaria risk levels in Relation to Rainfall Patterns

Characterize Dengue risk levels in Relation to Rainfall Patterns

Analyze in the highest Malaria risk region’s Land cover

Analyze in the highest Dengue risk region’s Land cover

Model: Based on Rainfall ( for Malaria )

Model: Based on Rainfall (for Dengue)

Results and Conclusions Figures 4. 1: Presentation of study



Administrative data Collection & Input

Map of Sri Lanka

Digitized to 32 MOH area Of Puttalam , Gampaha & Colombo

Digitized Sri Lanka map into 25 districts

Prepare MOH areas Prepare Sri Lanka GIS data base GIS data base G Produce Sri Lanka District wise Layers & MOH areas wise Layers . Figures 4.2: Administrative data Collection & Input



Medical Data Collection & Input

Malaria Cases Data Collected form AMC , Sri Lanka

Dengue Cases Data Collected form Epidemiological Division

District wise Cases 1991 to 2000 By Monthly

District wise Cases 1991 to 2000 By Monthly

MOH area wise Cases 1995 to 2000 By Monthly

Mal:

Districts Colombo Gampaha Kaluthara

Enter Date to District Level GIS data base

Dengue Mal:

Dengue Mal:

MOH area wise Cases 1996 to 2000 By Monthly

Dengue Mal:

Dengue

Patient Patient Patient Patient Patient Patient Patient Patient 1991 1991 1992 1992 ….. ……. 2000 2000 Jan (M/D ) MOH are 2 3 4 5

Feb

Mar

…….

1995 to 2000 for Malaria 1996 to 2000 for Dengue

Enter Data to MOH area Level GIS data base ase Enter Data to MOH area Level District GIS dataLevel base

Produce ten years Layers from 1991 to 2000 & Six years layers at MOH area Level For Malaria

Enter Date to District Level GIS data base

Enter Data to MOH area Level GIS data base

Produce ten years District Level Layers from 1991 to 2000 & Six years layers at MOH area Level For Dengue

Figures 4.3: Medical data collection & Input



Climatic Data Collection & Input

From 1991 to 2000 ( Ten years data )

Monthly average Rainfall Data 1991 to 2000

Monthly Total Districts wise data from Meteorological department

Convert to Annual Average per each Districts Year : 1991 Climatic Data Collection Table Rainfall m.m Rainfall m.m Rainfall mm

Districts Colombo

Annual Avg: Annual Avg: Annual Avg: 1991 1992 1993 Station 1 Station 2 Station n Avg: Value

Gampaha Kaluthara Enter yearly data to GIS database

Produce Ten Climatic data layers (1991 to 2000)

Figures 4.4: Climatic Data Collection & Input



Physical Environmental data Collection & Input

Malaria highest risk area

Dengue highest risk area

Satellites images of Malaria

Satellites images of Dengue

Highest risk Area

Highest risk Area

IRS IC LISS 111 four band image

Land Cover Classification

Figures 4.5: Physical Environmental data Collection & Input



4.3 Arc View GIS Spatial Analyst The ArcView Spatial Analyst helps to discover and better understand spatial relationships in data, from viewing and querying data to creating an integrated custom application. New type of analysis are possible using the Spatial Analyst because it can be used to model rater data, in addition to the vector data Arc View GIS already supports. 4.4 Shape file, Theme and Grid themes in Arc View GIS ArcView shapefiles is the format used for the data that comes with ArcView. When create the own spatial data in ArcView, it is saved in shapefile format. Other format such as ARC/INFO can be used in ArcView but it saved as shapefile format when working with ArcView. A grid theme, like other ArcView themes, is based upon a data source. A grid data source is a raster data set comprised of rows and columns of data. A view is made up of layers of geographic information for a particular area or place. Each layer is a collection of geographic features, such as rivers, lakes, countries, or cities. In ArcView, these layers are called themes. Grid themes can be based on integer or floating-point grid data sets. Grid themes based on integer data can have an associated table that stores the list of unique cell values in the theme. Only the Graduated Color legend panel is enabled for grid themes based on floating point data. The Graduated Color and Unique Value panels are enabled for grid themes based on integer grid data sets. Floating point grid sets represent features that have continuous values, such as elevation. Values in a floating-point grid theme will change continuously as move from one location to next. Integer grid data sets are usually used to represent features that have discrete values. Discrete values represent phenomena in categories, such as counties. With discrete data it’s easy to define precisely where an object begins and ends. An integer grid data set can represent continuous data like a floating-point grid data set can, but measurements will only be accurate to a whole number. 4.7 Map Calculation in GIS ArcView This is Map composite method in Arc View. Grid objects can be used within the Map Calculator. Logarithmic, arithmetic, trigonometric, and exponential functions are available as menu choices and other requests can be manually entered.



4.5 ArcView GIS based Jenks' optimization method It can be shown how a statistical measure called the Goodness of Variance Fit, or GVF, changes when we use alternative data classifications on the same set of numeric data. This statistic is used in a classification strategy introduced by George F. Jenks and usually called the optimization method, or Jenks' optimization method. Borden Dent, in his cartography text, introduces the optimization method this way: [It] incorporates the logic most consistent with the purpose of data classification: forming groups that are internally homogeneous while assuring heterogeneity among classes... ...The steps in computing the GVF are as follows: 1. Compute the mean of the entire data set and calculate the sum of the squared deviations of each observation in the total array from this array mean: This will be called SDAM (squared deviations, array mean). 2. Develop class boundaries for the first iteration. Compute the class means . Calculate the deviations of each observation from its class mean

, square these, and calculate the grand sum: This will be called SDCM (squared deviations, class

means). 3. Compute the goodness of variance fit (GVF): The computed difference betwen SDAM and SDCM is the sum of squared deviations between classes. 4. Note the value of GVF for iteration one. The goal through the various iterations is to maximize the value of GVF. 5. Repeat the above procedures [step 2 - 4] until the GVF cannot be maximized further.



4.7. Risk based map on Malaria and Dengue . Collected ten years Malaria cases and Dengue cases data of districts wise in Sri Lanka 1991 to 2000 should be entered into ten district layers and It can be applied to GIS ArcView based the Jenks’ Optimization method to classified different level of cases in each year. After that it can be manipulated and analyzed into the risk distribution of both diseases using GIS. Twenty layers of Malaria and Dengue distribution from 1991 to 2000 should be overlaid into two layers one for Malaria another for Dengue using Map Calculation functions of spatial analysis extension in GIS ArcView 3.2. Finally it’s mentioned the highest risk districts of both diseases separately. It is important to determine the risk districts for health administration and planning control strategy. Other hand in this study, it is important to determine the highest risk diseases area to build the Land cover model for diseases, which is mentioned in Objective three. Methodology for objective one is shown in figures 4.3.1 4.8 Malaria / Dengue relation with Rainfall levels. Ten years data of Rainfall from 1991 to 2000 should be collected to build relation of Malaria / Dengue with Rainfall because in this study there are evaluate same parameter for both diseases to determine how to correlate rainfall as a changing factor to spread both diseases. Following steps can be taken to build relation Malaria / Dengue with Rainfall. Total Rainfall value of every month in year by year should be added to find the annual rainfall value in the station and this value can be averaged with Several station’s annual value in the same district. After the calculation of twenty-five districts Rainfall average value in the stations of each district, it should be entered to GIS database to produce the each year Rainfall layer. After that it can be presented ten layers from 1991 to 2000 and should be applied to the GIS based Jenks’s Optimization method to classify. Finally it can be analysis to rainfall levels in each year. Second step in the classification, Every year shape format themes should be converted as grid format themes. Grid formats should be applied to again Jenks’s Optimization method and After that It can be classified Rainfall levels in Sri Lanka using Map calculation functions in GIS ArcView spatial extension. After finding of three level on Rainfall in the period of ten years in Sri Lanka using above GIS technique. Rainfall level theme should be active and use the summarized zone faculties in ArcView 3.2 and find the Relationship of Malaria & Rainfall, Dengue & Rainfall using comprising techniques to overlay girds themes ( with out classified ) of Malaria and Dengue to find the mean against rainfall levels . Brief methodology for objective 2 is shown in figures 4.7.



Methodology for Objective one: Risk based map

Definition of the study area in GIS (Digitize the study area in GIS ArcView 3.2 )

Prepare the GIS database

Produce new ten layers for 1991 to 2000 Malaria Cases

Produce new ten layers for 1991 to 2000 Dengue Cases Reclassify using Jenks’ Optimization method

Convert to Grid themes

Convert to Grid themes

Yearly Malaria & Dengue risk of SL

Reclassify grids to 5 Classes Using Jenks’s Optimization method

Reclassify grids to 5 Classes Using Jenks’s Optimization method.

Combine ten layers using Map Calculations of GIS ArcView

Combine ten layers using Map Calculations of GIS ArcView

Reclassify to find the Levels of Malaria

Reclassify to find the Levels of Dengue

Convert to shape file

Covert to shape file

Risk based map of Malaria at district level

Dengue based map of Dengue at District level

Figures:4.6 Malaria and Dengue risk distribution. Ideal International E- Publication www.isca.co.in


Methodology for Objective 2: Characterized diseases risk in relation to rainfall levels

Annual Average Value of Rainfall stations of Each Districts.

GIS System ( GIS ArcView 3.2 new view & Add Sri Lanka district.shp file )

Prepare the database in GIS System ( Start to Edit the table of Sri Lanka District.shp file & Enter 1991:2000 Rainfall date in the ten field.)

Convert to grid themes

Reclassify using Jenks’ optimization method

Produce ten years Rainfall layer of Sri Lanka :1991 to 2000

Rainfall Map by Yearly

Combine ten lays using Map Calculation

Rainfall levels

(M91grid+M92grid+ …+M2000grid)= Map Calculation theme has been chosen as y-axis to summarized Zone

Active Rainfall Levels theme

Summarized using ―Summarized Zones‖ function in GIS ArcView

(D91grid+D92grid +…+ D2000grid)= Map Calculation theme has been chosen as y-axis to summarized Zone

Rainfall & Malaria and Rainfall & Dengue relationships

Characterize risks in relation to rainfall levels Figure:4.7 Malaria and Dengue with Climatic Paramerter ( Rainfall ) Ideal International E- Publication www.isca.co.in


Objective 3:Analyzed in the highest Malaria / Dengue risk region’s Land Cover Select the Risk areas of Malaria and Dengue Definition of the highest risk area

Enter Annual MOH area Cases of Malaria & Dengue

(Digitized the MOH area, Prepare database)

Prepare the GIS database (Build ArcView shape file for MOH areas & Edit the table)

Produce 6 years Malaria Risk MOH Layers

Produce 6 years Dengue Risk MOH layers

Convert to grid

Convert to grid

Reclassify grid themes

Reclassify grid themes

Combine using Map Calculation

Highest Risk area of Malaria

Highest Risk area of Dengue

Satellite image subset of Highest Malaria Risk area

Satellite image subset of Highest Dengue Risk area

Image rectify

Land Cover Model for Malaria Percentages of Land Cover types

Unsupervised Classification using ER Mapper 6.1 Environmental Condition of Diseases

Figure:4.8 : Land cover map for Malaria and Dengue Ideal International E- Publication www.isca.co.in

Land Cover Model for Dengue Percentages of Land Cover types


Objective 4:Build the model based on Rainfall of Malaria and Dengue

Malaria / Dengue highest risk district Malaria / Dengue highest risk MOH area

Monthly Malaria cases by MOH area Level from 1995 to 2000

Monthly Dengue Cases by MOH area level from 1996 to 2000

Monthly Rainfall data in MOH area Level from 1995 to 2000

Monthly average Value

Seasonal pattern of Malaria and Rainfall

Regression Analysis: Model 1 : Y= A + BX1 X= Rainfall (Independent Variable) Y= Malaria/Dengue Cases

Seasonal pattern of Dengue and Rainfall

Malaria/Dengue model based on Rainfall (RESULT) Mosquito Life cycle

Regression Analysis: Model 2 : Y= A + BX1 X= Rainfall (Independent Variable) Y= Malaria/Dengue Cases

Malaria/Dengue model based on Rainfall (RESULT)

Figure 4.9: Model of Malaria / Dengue with Rainfall.



4.9 Geometric Correction of Remote Sensor Data Remotely sensed data usually contain both systematic and unsystematic geometric errors. There are two kinds of geometric correction one is systematic those that can be corrected using data from platform ephemeris and knowledge of internal sensor distortion and other one is Nonsystematic distortions those that cannot be corrected with acceptable accuracy without a sufficient number of ground control points. A ground control point (GCP ) is a point on the surface of Earth where both image coordinates ( measured in degrees of latitude and longitude, feet, or meters ) can be identified. Those geometric distortions that can be corrected through analysis of sensor characteristics and ephemeris include scan skew, mirror-scan velocity nonlinearities, panoramic distortion, spacecraft velocity, and perspective geometry ( including Earth’s curvature ) . Those that can only be only be corrected through the use of GCPs are sensor system attitude ( roll, pitch, and yaw ) and/or altitude. Most commercially available remote sensor data have much of the systematic error removed. However, the unsystematic error remains in the image. There are two common geometric correction procedures often used by Earth scientists to make the digital remote sensor data of value: image to map rectification and image to image registration. Image to map rectification is the process by which the geometry of an image is made planimetric. Whenever accurate area, direction, and distance measurements are required, image to map geometric rectification should be performed. It may not, however, remove all distortion caused by topographic relief displacement in images. The image to map rectification process normally involves selecting GCP image pixel coordinated (row and column ) with their map coordinate counterparts. It will be demonstrated how the mathematical relationship between the image coordinates and map coordinates of selected GCPs is computed and the image is made to fit the geometry of the map. GCP map coordinate information does not always have to come from a planimetric map. Instead, global positioning system (GPS ) instruments may be taken int the field the field to obtain the coordinates of objects. GCP collection of map coordinate information to be used for image rectification is especially effective in poorly mapped regions of the world or where rapid change has made existing maps obsolete.

4.10 ISODATA Unsupervised Classification algorithms Today several different unsupervised classification algorithms are commonly used in remote sensing. The two most frequently used algorithms are the K-mean and the ISODATA clustering algorithm. Both of these algorithms are iterative procedures. In general, both of them assign first an arbitrary initial cluster vector. The second step classifies each pixel to the closest cluster. In the third step the new cluster mean vectors are calculated based on all the pixels in one cluster. The second and third steps are repeated until the "change" between the iteration is small. The "change" can be defined in several different ways, either by measuring the distances the mean cluster vector have changed from one iteration to another or by the percentage of pixels that have changed between iterations.



The ISODATA algorithm has some further refinements by splitting and merging of clusters (JENSEN, 1996). Clusters are merged if either the number of members (pixel) in a cluster is less than a certain threshold or if the centers of two clusters are closer than a certain threshold. Clusters are split into two different clusters if the cluster standard deviation exceeds a predefined value and the number of members (pixels) is twice the threshold for the minimum number of members. Using the ISODATA algorithm to perform an unsupervised classification. ISODATA stands for "Iterative Self-Organizing Data Analysis Technique." It is iterative in that it repeatedly performs an entire classification (outputting a thematic raster layer) and recalculates statistics. "Self-Organizing" refers to the way in which it locates the clusters that are inherent in the data. The ISODATA algorithm is similar to the k-means algorithm with the distinct difference that the ISODATA algorithm allows for different number of clusters while the k-means assumes that the number of clusters is known a priori. The ISODATA clustering method uses the minimum spectral distance formula to form clusters. It begins with either arbitrary cluster means or means of an existing signature set, and each time the clustering repeats, the means of these clusters are shifted. The new cluster means are used for the next iteration. The ISODATA utility repeats the clustering of the image until either:  

a maximum number of iterations has been performed, or a maximum percentage of unchanged pixels has been reached between two iterations.

4.11 Land Cover map based on Malaria risk and Dengue risk. According to the Objective one district level risk based of both diseases can be identified. After observing the risk distribution, it should be decided the area to carry on study to MOH area level, which should be under the highest risk level. After the selection of risk area in the district level it must be created GIS data base using digitizing and building the data table. The data table of MOH area level comprises Malaria cases in MOH area level from 1995 to 2000 and Dengue cases 1996 to 2000. After entering the data to database each year situation of Malaria and Dengue can be presented. Second step is to make two maps for the highest risk MOH area of Malaria and Dengue in the data available period. It can be makes using Map Calculation in the ArcView GIS spatial extension after converted to grid format of each year MOH level themes. Those MOH area level risk based map identify the highest risk levels. Next step is search the environmental condition of the risk area of the both diseases. Therefore remote sensing based Satellite imagery data is reliable and most sophisticated technique to search environmental condition. Therefore Subset of Satellite imagery of the most risk MOH area of both diseases should be collected. Selection of Datum and Projection to prepare image rectify, what are basic technique to be fulfill the before calcification. After that it can be use unsupervised classification to classified the image and searching the Topographic map, Land use map and Ground truth data, it can be mention Ideal International E- Publication www.isca.co.in

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……36 the land cover of the Risk area of Malaria and Dengue. Brief methodology for objective 1 is shown in figures 4.5.1 4.12. The Model based on Rainfall as a main climatic factors of Malaria / Dengue. a. Search the seasonal behavior using average monthly value of 1995-2000 on Malaria & Rainfall, 1996-2000 average monthly values on Dengue & Rainfall.

b. Building a Relationship model using regression analysis X1 = Rainfall Y = Malaria/Dengue Cases The following models will be calculated:

Predictor Variable Rainfall

Regression Results Equation Y = A + BX1


r2


CHAPTER 5 MALARIA AND DENGUE RISK AT DISTRICT LEVEL

5.1 District level GIS Data base of study area: Figure: 5.1 present the GIS Database layer of twenty-five districts, which are the secondary level of the administrative system in Sri Lanka.

Figure 5.1: District level layer of the GIS Data base of the study area.


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……38 5.2 Risk base-map of Malaria at district level: 1991-2000

Risk based map of Malaria at district level : 1991-2000 78°30'

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The figure 5.2 represents the Malaria risk distribution, based on a ten years time series data. This map was composed by integrating the ten years malaria cases over 25 districts in Sri Lanka. These ten years data were overlaid and modeled to produce five classes of incidence levels: very low, low, moderate, high, and very high. (Figure: 4.6 in Chapter 4 have shown the methodology for above map) This regional district base map leads to the identification of highest risk zones of Malaria where MOH area study is carried on resulting in risk areas delineation at local scale.



5.3 Risk base-map of Dengue at district level: 1991-2000

Risk based map of Dengue at district level : 1991-2000 78°30'

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Figure 5.3: Dengue risk spread map of Sri Lanka

The figure 5.3 represents the Dengue risk distribution, based on a ten years time series data. This map was composed by integrating the ten years malaria cases over 25 districts in Sri Lanka. These ten years data were overlaid and modeled to produce five classes of incidence levels: very low, low, moderate, high, and very high. (Figure: 4.6 in Chapter 4 have shown the methodology for above map) This regional district base map leads to the identification of highest risk zones of Malaria where MOH area study is carried on resulting in risk areas delineation at local scale.



5.4 Risk based on Climatic factors of Malaria (Rainfall) 5.4.1 Rainfall Observation Station in Sri Lanka

Figure 5.4.1 Rainfall Observation Stations in Sri Lanka

This study has analyzed 200-rainfall stations, which have an island wide covering daily precipitation status. The above map indicates the rainfall observation stations, at which data have been collected for the analysis of the rainfall-based model. Some stations have been ignored due to inadequate data. In the above map, each station bears ten years (1991 to 2000) rainfall data in mm for every month.



5.4.2 Rainfall based classification in study area: 1991-2000

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Figure 5.4.2 Rainfall based classification in study area: 1991-2000;

The figure 5.4.2 represents the Rainfall levels, based on a ten years time series data. This map was composed by integrating the ten years malaria cases over 25 districts in Sri Lanka. These ten years data were overlaid and modeled to produce five classes of incidence levels: very low, low, moderate, high, and very high. (Figure: 4.7 in Chapter 4 have shown the methodology for above map) This regional district base map leads to the identification of rainfall level where relationship between rainfall and diseases are carried on resulting at regional scale rainfall levels.



5.4.3 Relation between Rainfall and Malaria cases

Figure 5.4.3: Model on Rainfall and Malaria cases. The figure 5.4.3 represents relationship between rainfall and Malaria on ten years data. This figure was built integrating ten years malaria cases over 25 districts in Sri Lanka. ―Summarized Zone‖ facility of Arc View was used to make relationship between rainfall and Malaria cases for 10 years of data as explained by the following formula. All ten layers were converted to grid format and were not reclassified. ((1991 Malaria grid theme + 1992 Malaria grid theme + 1993 Malaria grid layer +……….+ 2000 Malaria grid theme)) = Map Calculation 4 Three classes of Rainfall have been put as x value and mean value of Map Calculation 4 has been applied as y value using summarized zone facilities of Arc View GIS. Then the relationship between Rainfall levels and Malaria cases can be understood. Figure 5.4.3 indicates that low rainfall area has been occurred at 220,000 cases mean value within 1991-2000 period while high rainfall area have been occurred at below 400,00 cases and moderate at about 160,000 cases.



5.4.4 Relation between Rainfall and Dengue cases

Figure 5.4.4:Model on Rainfall and Dengue cases The figure 5.4.3 represents the relationship between rainfall and Malaria on ten years data. This figure was built integrating ten years malaria cases over 25 districts in Sri Lanka. ―Summarized Zone‖ facility of Arc View was used to make relationship between rainfall and Malaria cased for 10 years of data as explained by the following formula. All ten layers were not reclassified to direct convert to grid format. ((1991 Dengue grid theme + 1992 Dengue grid theme + 1993 Dengue grid theme +……….+ 2000 Dengue grid theme)) = Map Calculation 5 Rainfall three classes have been put as x value and mean value of Dengue grids 1991 – 2000 as y value. The relationship between Rainfall levels related to the Dengue cases is evident from the summarized graph above. Figure 5.4.4 indicates that 700 Dengue cases has occurred in the high rainfall level and low cases have been reported in the low rainfall level.


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……44 5.4.5: Relation between Rainfall and Malaria / Dengue with Population.

5.4.5.a. Relation between rainfall and Malaria per 100,000 population.

5.4.5.b.: Relation between rainfall and Dengue with 100,000 population

The Malaria and Dengue cases for each district were normalized by the population of each district and multiplied by 100,000. These figures represent cases per each 100,000 of the population. Then, each Malaria and Dengue cases were summarized within the zones of rainfall levels as above. The Figure 5.4.5.a and b indicate that highest Malaria cases occurred at lower rainfall levels while the highest Dengue cases occurred at higher rainfall levels and vice versa. Note – Since the population data was available only for the year 1998, the above analysis was carried out for 1998. It may be assumed that the above assumption is true for the other years too.



CHAPTER 6 LAND COVER CLASSIFICATION OVER RISK AREA 6.1 From Regional to Local scale study

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Figure 6.1: Risk area based on ten years risk distribution map According to the explanation of chapter five, highest risk conditions of both diseases at district level during the period 1991 to 2000 can be identified from the Figurer 6.1.1a and b. Malaria cases have been classified under the five levels namely, very low, low, moderate, high and very high. Very high districts have been identified as Puttalam, Anuradapurea, Monaragala and Jaffana. The Puttalam district has been selected as one of the highest risk Malaria district for further study due to the following reasons. a. Puttalam MOH area stands as the main risk MOH area in Sri Lanka for the past decade according to the above analysis. b. Other infrastructure and aided sources such as remotely sensed data, topographic maps and comprehensive data are available in Puttalam district. c. Puttalam and Colombo districts respectively highest Malaria and Dengue cases area have located along the coastal line, which would allow the parallel study and comparison of two diseases. Figure 6.1.1.b shows that Colombo district has the highest dengue risk in Sri Lanka during each year of the last decade. Therefore these districts have been selected for further study as the most risk Malaria and Dengue, which is explained on Figure 6.1.


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……46 6.2 GIS Data base for Medical Officer for Health area on the Risk base of Malaria & Dengue

Figure 6.2.1: Select the area for MOH area database

Study of MOH area for Malaria and Dengue risks associated to land cover classification will be done on the basis of the area represented on Figure 6.2.1. These areas are found to be within Puttalam, Gampaha and Colombo districts comprising thirty-two MOH areas.



MOH areas in Puttalam, Gampaha and Colombo district

Figure 6.2.2 MOH area map in Puttalam, Gampaha and Colombo district

Figure 6.2.2 indicate MOH areas of both Colombo, Puttalam and Gampaha districts. Most of the MOH areas in the Puttalam district can be identified as the highest risk malaria prone areas. Most of the MOH areas in Colombo district can be identified as the highest Dengue risk areas.


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……48 6.3 Malaria risk at MOH area level: 1995 - 2000

Figure 6.3: Malaria risk on MOH area level 1995 to 2000 The Figure 6.3 represents Malaria risk distribution, based on a six years time series data. This map was composed by integrating six years malaria cases over 32 MOH areas in Puttalam, Gampaha, and Colombo districts. These six years data were overlaid and modeled to produce five classes of incidence levels: very low, low, moderate, high and very high. (Figure: 4.8 in Chapter 4 has shown the methodology for above map.) This local area base map leads to the produce land cover classification over highest Malaria MOH area, which is presented in the map.

6.4: Land Cover Classification over Malaria highest risk area Ideal International E- Publication www.isca.co.in


1

Classification Key

Figure 6 .4: Land cover classification over Malaria highest risk area


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……50 6.5 Dengue risk at MOH area level: 1996 - 2000

Figure 6.5: Dengue risk on MOH area level 1996 to 2000 The Figure 6.4 represents the Dengue risk distribution, based on five years time series data. This map was composed by integrating five years Dengue cases over 32 MOH areas in Puttalam, Gampaha, and Colombo districts. These five years data were overlaid and modeled to produce five classes of incidence levels: very low, low, moderate, high and very high. (Figure: 4.8 in Chapter 4 has shown the methodology for above map.) This local area base map leads to the produce land cover classification over highest Dengue MOH area, which is presented in the map.



6.6 Land Cover Classification over Dengue highest risk area

Classification Key

Figure 6.6 Land cover classification over Dengue highest risk area


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……52 6.7 Percentage of Land Cover types over Malaria / Dengue highest risk area Percentage of Land Cover types over Malaria highest risk area Clear Water Sea Water

Land cover types on Malaria highest area 0%

Water with Green Marsh

1% 6%

0% 3% 4%

2% 1% 1%

Mangrcves 20%

Forest Open forest 33% Agricultrue Urban & Suburban Grass Land 29% Barren Land Sand

Figure 6.7.a: Land cover types over Malaria risk area

Figure 6.7.b Land cover types percentage over Malaria area

Percentage of Land Cover types over Dengue highest risk area Land Cover types on Dengue highest area 5%

Sea water & Clear Water Water in Costal area Delta Wate

15%

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Sub Uraban Agriculture Lands Rice Field Urban Grass & Bare Land

Figure 6.8.a : Land cover types over Dengue risk area

Figure 6.8.a:Land cover types percentage over Dengue risk area

Figure 6.7.a and 6.7.b indicate that the highest Malaria risk areas covered with vegetation (forest 33%, open forest 29% and agriculture 20%). These areas are mostly rural areas at a total of 82%. Figures 6.8.a and 6.8.b indicate that highest Dengue risk area are covered with built up area 18%, sub urban 25%, agriculture lands and garden 19%. These areas are mostly urban areas at a total of 62 %.



CHAPTER 7 MODEL DEVELOPMENT 7.1 Seasonal behavior of Malaria and Dengue Six year monthly average of Malaria and Dengue cases over the highest risk area have been analyzed and plotted along with rainfall in order to find the offset of peaks in both rainfall and the cases. Using the statistical software Malaria and Dengue cases can be compared with rainfall data and it came to know that the malaria peak occurs two months after rainfall peak appears and Dengue peak appears after one month of rainfall peak due to the effect of mosquito life cycle. 7. 2 Impact of Mosquito Life Cycle All mosquitoes have one common requirement; they need water to complete their life cycle. Some mosquitoes lay individual eggs on the sides of treeholes or discarded containers, or in depressions in the ground that will hold water. The eggs can lay dormant for several years. Some eggs will hatch when they are flooded by rainfall. Several flooding and drying cycles are usually required for all of the eggs to hatch that are laid by a particular female mosquito. Other mosquitoes lay eggs directly on the surface of water. The eggs are attached to one another to form a raft or the individual eggs float on the water. These eggs hatch in 24-48 hours releasing larvae that are commonly called "wrigglers" because you can often see the larvae wriggling up and down from the surface of the water. Generally, the larvae feed on microorganisms and organic material in the water, but some mosquitoes prey on the larvae of other mosquito species and are regarded to be beneficial. In about 7-10 days after eggs hatch, larvae change to the pupal or "tumbler" stage in preparation for adult life. Female mosquitoes begin to seek an animal to feed on several days after emerging from water. Male mosquitoes mate with females one to two days after the females emerge. Males do not bite, but they do feed on plant juices.



Life Cycle of Malaria.

5-9 days

2-4 days

3-5 days

8-14 days

18-32 days

Malaria Symptoms usually occur 3 to 6 weeks

Dengue Symptoms usually occur 46 days

Two Month before Rainfall data

One Month before Rainfall data

Figure 7.2: Mosquito life cycle



7.3 Model Development The aim is to construct a Malaria cases model & Dengue cases model in the most highest risk area that takes into account the majority of main factors known to influence the cases of both diseases. The development of a regression model to identify and to assess Malaria / Dengue highest risk area can be analyzed in relation to rainfall as its’ main factor. Simple regression analysis allowed to investigation of the relationship of the Malaria & Rainfall, Dengue & Rainfall.

Model 1: Y= A+BX1 Definition and Comments X= Six years Rainfall data by Monthly average wise (For Malaria) X= Five years Rainfall data by Monthly average wise (For Dengue) Y = 1995 to 2000 (Six years) Malaria cases by Monthly average Y= 1996 to 2000 (Five years) Dengue cases by Monthly average

Above model has been built using same month cases and same month related rainfall date but Generally it came to know that it takes some time to report the cases of both malaria and Dengue after the rainfall. a. After the raining it increases the breeding site of mosquitoes and make large amount of pool to breed the mosquito. It takes some time likely 20 to 30 days to grow as a adult female mosquito. b. Secondly Malaria is occurring after 4-6 weeks after the bite of female mosquito and dengue occur after 4-6 days after the first day of the related mosquito bite. According to the above reasons of mosquito life cycle, it can be applied to second model as explained below .

Model 2: Y= A+BX1 X= Two month before Six years Rainfall data by Monthly average wise (For Malaria) X= One month before five years Rainfall data by Monthly average wise (For Dengue) Y = 1995 to 2000 (Six years) Malaria cases by Monthly average Y= 1996 to 2000 (Five years) Dengue cases by Monthly average



7.4 Malaria Seasonal pattern - Relation Model with rainfall in Puttalam District. Malaria & Rainfall seasonal pattern & its relationship in the Puttalam District at 1995 to 2000 are shown in fingers ( Value according to the monthly average )

Figure 7.4.a: Malaria Seasonal Model-Relation Model with rainfall in Puttalam District. Malaria & Rainfall seasonal pattern & its relationship in whole Puttalam District at 1995 to 2000 are shown in fingers ( Value according to the monthly average )

Figure 7.4.b: Malaria Seasonal Model-Relation Model with rainfall in Puttalam District. According to the mosquito life cycle. Above two Figures 7.4.a &.b explain the relation of Malaria cases and Rainfall as main parameter in Puttalam district. First frame of both figures explain seasonal occurrence of Malaria and behavior of Rainfall which affect Malaria cases. The second frame of 7.4.a indicate impact of mosquito life cycle when analysis with rainfall. Ideal International E- Publication www.isca.co.in

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……57 7.5 Malaria Seasonal pattern - Relation Model with rainfall in Puttalam MOH area.

Malaria & Rainfall seasonal pattern & its relationship in Puttalam MOH area at 1995 to 2000 are shown in fingers ( Value according to the monthly average )

Figure 7.5.a: Malaria Seasonal Model-Relation Model with rainfall in Puttalam MOH area. Malaria & Rainfall seasonal pattern & its relationship in Puttalam MOH area at 1995 to 2000 are shown in fingers ( Value according to the monthly average )Two month before rainfall data

Figure 7.5.b: Malaria Seasonal Model-Relation Model with rainfall in Puttalam MOH area according to Mosquito life cycle. Above two figures 7.4.a & 7.4.b explain the relation of Malaria cases and Rainfall as main parameter in Puttalam MOH area. First frame of both figures explain seasonal occurrence of Malaria and behavior of Rainfall to increase or decrease the Malaria cases. The first frame of 7.4.b indicate impact of mosquito life cycle when analysis with rainfall.



7.6 Dengue Seasonal pattern - Relation Model with rainfall in Colombo District. Dengue & Rainfall seasonal pattern & its relationship in Colombo District at 1995 to 2000 are shown in fingers ( Value according to the monthly average )

Figure 7.6.a: Dengue Seasonal pattern Model-Relation Model with rainfall in Colombo district.

Dengue & Rainfall seasonal pattern & its relationship in Colombo District at 1995 to 2000 are shown in fingers ( Value according to the monthly average ) one month before rainfall data

Figure 7.6.b: Dengue Seasonal pattern Model-Relation Model with rainfall in Colombo district according to the mosquito life cycle.



7.7 Dengue Seasonal pattern - Relation Model with rainfall in Colombo MOH area. Dengue & Rainfall seasonal pattern & its relationship in Colombo MOH area at 1995 to 2000 are shown in fingers ( Value according to the monthly average )

Figure 7.7.a: Dengue Seasonal pattern Model-Relation Model with rainfall in Colombo MOH area . Dengue & Rainfall seasonal pattern & its relationship in Colombo MOH area at 1995 to 2000 are shown in fingers ( Value according to the monthly average ) Two month before rainfall data

Figure 7.7.b: Dengue Seasonal pattern Model-Relation Model with rainfall in Colombo MOH area according to Mosquito life cycle.



CHAPTER 8 CONCLUSION AND RECOMMENDATION This study focuses the evolution of two mosquito-led diseases (Malaria and Dengue) prevalent in Sri Lanka for the last decade from 1991 to 2000. Sufficient data was available to fulfill the objectives of the study. Initially, the risk area distribution for both diseases has been analyzed for each year from 1991 to 2000 for Sri Lanka. Secondly, a risk map indicating risks with respect to the whole decade has been prepared in order to delineate the highest risk area at district level. This has led to the initial conclusion that Puttalam, Anuradhapura, Moneragala, Jaffna and Kilinochchi districts have recorded as the most risk for Malaria. For Dengue, Colombo district showed as the highest risk district in each year. The next in the rank for Dengue, goes to Gampaha district, which is also a highly populated district close to Colombo. Secondly, cases of both diseases have been analyzed with rainfall data at district level. This process was carried out annually initially and then for the whole decade. It can be concluded that the low rainfall was highly correlated to the Malaria cases while highest rainfall levels were well correlated to the Dengue cases. With the help of remotely sensed data (IRS LISS III image), it was found that Malaria risk was high at rural and forestry led environment while, urban led environment for high risk in Dengue. This land cover analysis was obtained through unsupervised classification of remote sensing image for the MOH areas where there is a high risk for both diseases. Peaks of both rainfall and cases (both Malaria and Dengue) observed for the highest risk district delineated based on the above mentioned preliminary conclusions, has showed an offset which has been assumed owing to the effect of mosquito life cycle. According to the Malaria mosquito life cycle and the findings of the literature review, resulting Malaria cases occur after two months period from the rainfall. It is one month in case of Dengue. Therefore, the regression models developed after shifting the rainfall (two months for Malaria and one month for Dengue), have proved strong results compared to those developed without shifting. The R-squared values obtained are in the order of 0.65 for the model calibrated using Malaria cases in the Puttalam district. It was 0.029 for the model derived without shifting the rainfall. This model was validated using data for an MOH area falling within the same district and the r-squared value obtained was 0.72. Analysis for Dengue cases done in the same manner have also showed similar results for Colombo district. The calibrated model for Colombo district resulted an R-squared value of 0.46 and the model validated using an MOH area in the same district resulted an Rsquared value of 0.71.



Outcomes of the study 

Health planning and administrative system to make decision on risks. This system can be useful to allocate the facilities, conducting prevention program and strategies to preserve resources.



The risk distribution of diseases and its environmental conditions are also helpful to monitor both diseases. Rainfall levels and its seasonal patterns over the risk areas, can be used as indicators for early warning system in the prevention of diseases.



Basically this study exhibits the usefulness of managing an information database for the public health sector in Srilanka.. It can also be mentioned that GIS and Remote Sensing techniques provide reliable and time saving means for health research. The study is directly related to public health originations such as Anti Malaria Campaign, Epidemiological Unit of Ministry of Health and Health Education Bureau in Sri Lanka.



This study can be adapted to an up-datable database to provide Malaria/Dengue related information over the whole country, including other aided data such as temperature, humidity etc.

Recommendations The approach of this study is to apply GIS and Remote Sensing for Malaria and Dengue for health planning. The results could be applied along with other epidemiological information, to predict the spread of the current epidemic and to indicate where resources might be best mobilized. Also the approach would be applied for mapping of high-risk areas of vector-borne disease. Therefore, the use of GIS in these applications is very important in decision support that has also become an area of growing interest in health care. Remote Sensing satellites images are rapidly becoming a powerful, cost-effective, easily accessible and more reliable method to monitor the vector borne diseases, which have been studied in this study. High-resolution satellites images such as five-meter resolution and one-meter resolution are highly recommended for more sophisticated. In this study, rainfall has been used as the main factor affecting the spread of Malaria and Dengue. Other factors such as temperature and humidity data are also more important but they were available only at a limited number of stations in Sri Lanka. If available, these factors can be used to develop a multiple regression models in future. In this study population data has been compared with cases but only for 1998 because Population data of study area is not available for each year. It is recommended that the population data in each year too be used to predict the number of cases per population in a further study.



REFERENCES; Bailey TC and Gatrell AC. (1995) Interactive spatial data analysis: Longman Scientific & Technical Essex. Dean Jr JW and Bowen DE. (1994) Management theory and total quality: improving research and practice through theory development. Academy of management review 19: 392-418. Goodchild M, Haining R and Wise S. (1992) Integrating GIS and spatial data analysis: problems and possibilities. International Journal of Geographical Information Systems 6: 407-423. Jetten TH and Focks DA. (1997) Potential changes in the distribution of dengue transmission under climate warming. The American journal of tropical medicine and hygiene 57: 285-297. Patz JA, Epstein PR, Burke TA, et al. (1996) Global climate change and emerging infectious diseases. Jama 275: 217-223. Perry B and Young A. (1995) The past and future roles of epidemiology and economics in the control of tick-borne diseases of livestock in Africa: the case of theileriosis. Preventive Veterinary Medicine 25: 107-120. Rejmankova E, Pope K, Roberts D, et al. (1998) Characterization and detection of Anopheles vestitipennis and Anopheles punctimacula (Diptera: Culicidae) larval habitats in Belize with field survey and SPOT satellite imagery. Journal of vector ecology: journal of the Society for Vector Ecology 23: 74-88. Rogers D and Randolph S. (1991) Mortality rates and population density of tsetse flies correlated with satellite imagery. Nature 351: 739. Root TL and Schneider SH. (1995) Ecology and climate: research strategies and implications. Science 269: 334-341. White R and Engelen G. (1992) Cellular Dynamics and GIS: Modelling Spatial: Research Institute for Knowledge Systems.


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……63 APPENDIX 1 Yearly Malaria risk distribution at district level










GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……68 1. Yearly Dengue risk distribution at district level









Yearly District base Precipitation of Sri Lanka:1991-2000












GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……78 Yearly Malaria risk on MOH area level:1995-2000

Malaria risk distribution in Puttalam,Gampaha & Colombo districts in 1995 79°00'

79°20'

79°40'

80°00'

80°20'

80°40'

81°00'

8°20'

8°20'

8°00'

8°00'

7°40'

7°40'

7°20'

7°20'

Mal-Std-95 0 - 29 30 - 219 220 - 632 633 - 1922 1923 - 2806

N

W 7°00'

7°00'

79°00'

79°20'

20

79°40'

0

80°00'

20

80°20'

80°40'

E S

81°00'

40 Kilometers


79°20'

79°40'

80°00'

80°20'

80°40'

81°00'

8°20'

8°20'

8°00'

8°00'

7°40'

7°40'

7°20'

7°20'

7°00'

7°00'

Mal-Std-96 0 - 193 194 - 561 562 - 1203 1204 - 3720 3721 - 9172

N

W

79°00'

79°20'

50

79°40'

80°00'

0

80°20'

80°40'

81°00'

50 Kilometers


E S



79°20'

79°40'

80°00'

80°20'

80°40'

81°00'

8°20'

8°20'

8°00'

8°00'

7°40'

7°40'

7°20'

7°20'

Mal-Std-97 0 - 199 200 - 534 535 - 1169 1170 - 2920 2921 - 5784

N

W 7°00'

7°00'

79°00'

79°20'

79°40'

50

80°00'

0

80°20'

80°40'

E S

81°00'

50 Kilometers


79°20'

79°40'

80°00'

80°20'

80°40'

81°00'

8°20'

8°20'

8°00'

8°00'

7°40'

7°40'

7°20'

7°20'

Mal-Std-98 0 - 56 57 - 302 303 - 841 842 - 2005 2006 - 3209

N

W 7°00'

7°00'

79°00'

79°20'

50

79°40'

80°00'

0

80°20'

80°40'

81°00'

50 Kilometers


E S



79°20'

79°40'

80°00'

80°20'

80°40'

81°00'

8°20'

8°20'

8°00'

8°00'

7°40'

7°40'

7°20'

7°20'

7°00'

7°00'

Mal-Std-99 0 - 118 119 - 629 630 - 1142 1143 - 2270 2271 - 4052

N

W

79°00'

79°20'

79°40'

50

80°00'

80°20'

0

80°40'

E S

81°00'

50 Kilometers


79°20'

79°40'

80°00'

80°20'

80°40'

81°00'

8°20'

8°20'

8°00'

8°00'

7°40'

7°40'

7°20'

7°20'

Mal-Std-00 0 - 75 76 - 334 335 - 1623 1624 - 2514 2515 - 4301

N

W 7°00'

7°00'

79°00'

79°20'

90

79°40'

80°00'

80°20'

0

80°40'

81°00'

90 Kilometers


E S

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……81 Yearly Dengue risk on MOH area level:1996-2000

Dengue risk distribution in Puttalam,Gampaha & Colombo districts in 1996 79°00'

79°15'

79°30'

79°45'

80°00'

80°15'

80°30'

80°45'

81°00'

8°30'

8°30'

8°15'

8°15'

8°00'

8°00'

7°45'

7°45'

7°30'

7°30'

7°15'

7°15'

Den-Std-96 0-6 7 - 20 21 - 42 43 - 92 93 - 279 N

7°00'

7°00'

6°45'

6°45' 79°00'

79°15'

79°30'

50

79°45'

80°00'

0

80°15'

80°30'

80°45'

W

E S

81°00'

50 Kilometers


79°15'

79°30'

79°45'

80°00'

80°15'

80°30'

80°45'

81°00'

8°30'

8°30'

8°15'

8°15'

8°00'

8°00'

7°45'

7°45'

7°30'

7°30'

7°15'

7°15'

Den-Std-97 0-4 5 - 11 12 - 28 29 - 51 52 - 410 N

7°00'

7°00'

6°45'

6°45' 79°00'

79°15' 50

79°30'

79°45' 0

80°00'

80°15'

80°30'

80°45'

81°00'

50 Kilometers


W

E S



79°15'

79°30'

79°45'

80°00'

80°15'

80°30'

80°45'

81°00'

8°30'

8°30'

8°15'

8°15'

8°00'

8°00'

7°45'

7°45'

7°30'

7°30'

7°15'

7°15'

Den-Std-98 0-6 7 - 23 24 - 64 65 - 124 125 - 480 N

7°00'

7°00'

6°45'

6°45' 79°00'

79°15'

79°30'

50

79°45'

80°00'

0

80°15'

80°30'

80°45'

W

E S

81°00'

50 Kilometers


79°15'

79°30'

79°45'

80°00'

80°15'

80°30'

80°45'

81°00'

8°30'

8°30'

8°15'

8°15'

8°00'

8°00'

7°45'

7°45'

7°30'

7°30'

7°15'

7°15'

Den-Std-99 0 - 16 17 - 37 38 - 75 76 - 137 138 - 512 N

7°00'

7°00'

6°45'

6°45' 79°00'

79°15' 50

79°30'

79°45' 0

80°00'

80°15'

80°30'

80°45'

81°00'

50 Kilometers


W

E S



79°15'

79°30'

79°45'

80°00'

80°15'

80°30'

80°45'

81°00'

8°30'

8°30'

8°15'

8°15'

8°00'

8°00'

7°45'

7°45'

7°30'

7°30'

7°15'

7°15'

Den-Std-00 0 - 11 12 - 42 43 - 115 116 - 248 249 - 695 N

7°00'

7°00'

6°45'

6°45' 79°00'

79°15' 50

79°30'

79°45' 0

80°00'

80°15'

80°30'

80°45'

81°00'

50 Kilometers


W

E S


Appendix 2: Related data table Malaria cases 1995 to 2000 MOH area wise ( Colombo, Gampaha & Putthalam districts )

MOH AREA PUTTALAM ANAMADUWA CHILAW MARAWILA KARUWALAGASWEWA

1995 2806 1307 1754 112 1922

1996 9172 2682 3397 90 3720 1063 1203 46 93 193 561 75 8 148 150 3155 3 19

1997 5784 2421 1904 82 2920 870 760 54 168 534 390 59 14 155 199 1169

90 1

13

536 72

29

ARACHCHIKATTUWA KALPITIYA DANKOTUWA KATANA NEGOMBO MIRIGAMA MINUWANGODA JA-ELA GAMPAHA ATTANAGALLA DIVULAPITIYA

313 24 103 427 373 64 15 116 219 632

KIRINDIWELA WATTALA

29

1998 3209 1756 2005 33 1439 484 469 38 32 148 302 56 21 111 108 841

1999 4052 1717 1142 30 2270 318 1025 55 21 96 192 45 7 118 91 629

15

9

8 49

2000 4301 2514 769 48 1623 137 1196 68 10 101 334 35 0 143 62 1074 0 0 0 0 17 75

334 65

280 63

276 54

252 65

195 51

24

3

6

18

RAGAMA KELANIYA BIYAGAMA HOMAGAMA DEHIWALA MORATUWA KOTTE PADUKKA COLOMBO NUGEGODA PILIYANDALA KADUWELA


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……85 Dengue Cases 1995 to 2000 MOH areas wise ( Colombo, Gampaha & Puttalam )

MOH AREA PUTTALAM ANAMADUWA CHILAW MARAWILA KARUWALAGASWEWA ARACHCHIKATTUWA KALPITIYA DANKOTUWA KATANA NEGOMBO MIRIGAMA MINUWANGODA JA-ELA GAMPAHA ATTANAGALLA DIVULAPITIYA KIRINDIWELA WATTALA

Mahara RAGAMA KELANIYA BIYAGAMA Kolonnawa HOMAGAMA DEHIWALA MORATUWA KOTTE PADUKKA COLOMBO NUGEGODA PILIYANDALA KADUWELA Maharagama TOTAL

D-96

D-97 1 2 5 4 0 0 0 11 15 16 2 0 23 17 5 1 3 26 9 30 20 12 9 15 92 25 34 1 279 42 13 9 6 727

D-98 1 1 10 0 0 0 0 3 2 10 1 0 2 13 0 2 4 3 6 28 10 4 5 0 51 17 6 13 410 11 7 7 16 643

D-99 0 0 5 3 0 0 5 0 9 14 0 10 6 9 2 1 5 13 7 64 12 14 8 1 124 29 33 31 480 18 23 14 20 960

D-00 0 0 8 2 0 0 1 2 19 52 9 12 33 58 27 4 16 56 24 72 25 12 34 26 137 25 53 6 512 34 75 37 33 1404

11 0 11 7 0 3 1 16 29 115 20 19 42 62 26 8 15 71 35 94 58 25 73 29 248 90 69 17 695 82 86 90 71 2218



MICROSCOPICALLY CONFIRMED MALARIA CASES IN SRI LANKA :1991-2000 1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Colombo

1913

4180

3607

1012

638

423

346

336

380

321

Gampaha

4132

5835

5165

5019

2068

4418

2721

1628

1207

1839

125

779

1145

787

530

153

220

307

293

279

Kalutara Kandy

4011

4092

2708

2064

1197

1003

1491

1113

967

710

Matale

38142

22130

19968

12074

9423

8529

9617

3966

3335

1429

Nuwara Eliya

421

569

436

159

52

167

132

100

88

206

Galle

398

190

129

102

82

196

93

39

41

18

1747

1735

1157

1494

1552

606

548

613

731

410

Matara Hambantota Jaffna Killinochchi

10761

5012

4595

6206

11075

2557

1576

2646

4755

5319

4794

12626

13977

37304

20237

14158

38778

47802

32915

7253

13246

10853

6717

14829

15575

33783

40711

44575

60692

47326

5409

2869

1840

5991

9631

6370

8569

8844

253

3458

699

495

8398

16939

19518

33974

25099

Vavuniya Mannar Mullativu Batticaloa Amapra Trincomalee

10737

12034

8684

2478

1248

2315

4886

3319

7787

6639

9240

5406

4593

1915

2694

5049

6347

2171

4534

3843

9939

12422

8381

938

1981

4934

4352

4584

5295

6608

106433

89252

71115

45081

4947

8655

5964

5500

6234

11863

Puttalam

49391

43326

36548

23225

8238

21373

14795

9433

10609

10656

Anuradhapura

47857

66912

95377

75499

27209

29203

24202

13427

17440

13218

Polonnaruwa

20462

15082

17862

6790

5754

5834

4862

3895

4001

4052

Kurunegala

Badulla

3715

3607

4965

2434

1768

1800

3336

1939

3924

5757

Moneragala

25652

42216

35255

23984

15690

13754

15451

24048

42673

40885

Ratnapura

24396

19203

9737

7993

7037

9217

10464

10371

12877

6982

Kegalle

12751

16479

8207

2115

964

1803

835

533

529

483

Total

400263 399349 363197 273502 142294 184319 218550 211691 264549 210039


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……87 Stations average Value of Rainfall in Sri Lanka on district base. RAIN_ DISTRICT RAIN_91 RAIN_92 RAIN_93 RAIN_94 RAIN_95 RAIN_96 RAIN_97 RAIN_98 RAIN_99 00 Vavuniya 1120 660 1534 1170 942 1264 1254 1275 1421 1616 Killinochchi 1332 926 1801 1111 975 1304 1112 1358 1235 991 Anuradhapura 1294 960 1646 1362 1028 1244 1407 1060 1257 1298 Polonnaruwa 1359 1304 1981 1933 1250 1302 1885 1226 1547 1536 Matale 1679 1423 2138 2249 1851 1486 2063 1769 1932 1730 Badulla 1946 1728 1851 2298 1635 1943 2360 1637 2122 2139 Monaragala 1523 1143 1355 1473 1386 1441 1935 1406 1250 1357 Hambantota 1063 748 1056 1067 996 1024 1584 939 976 1152 Ratnapura 3251 3409 3857 3039 3672 2944 3827 3776 3891 3173 Kandy 2059 1904 2331 2605 2644 2179 2689 2204 2347 2271 Kurunegala 1765 1492 1848 1444 1742 1525 2190 1668 1608 1395 Kegalle 2958 3026 3688 3140 3493 2439 3529 3619 3135 2509 Nuwara Eliya 2760 3544 3793 3140 3785 3164 3377 3405 3490 3024 Kalutara 3273 3174 4045 3636 3829 2859 3503 3933 4132 3351 Matara 2719 2004 2828 2083 2349 1856 2367 2116 2662 2330 Galle 2393 2575 2940 2613 2330 2282 2475 2396 3147 2703 Puttalam 1264 1426 1354 1309 1529 1165 1459 1394 1385 1229 Colombo 2608 2994 3297 2840 3330 2529 3457 3428 3497 2863 Batticaloa 1482 1188 1680 2888 1046 1516 1296 993 1686 1834 Trincomalee 1288 1493 1870 2012 1127 1248 1545 1391 1938 1639 Gampaha 1925 2022 2205 2112 2510 1807 2517 2367 2646 2183 Ampara 1647 1468 1601 2189 1182 1629 1333 1253 1911 1833 Jaffna 935 721 1491 858 853 1177 617 1235 1280 721 Mullativu 813 605 1744 942 985 1202 998 1047 1226 1043 Mannar 682 858 1677 1033 947 1233 938 1072 705 1065


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……88 Dengue confirmed cases in Sri Lanka : 1991-2000 DISTRICT DEN_91 DEN_92 DEN_93 DEN_94 DEN_95 DEN_96 DEN_97 DEN_98 DEN_99 DEN_00 Vavuniya 0 0 0 0 0 0 3 2 0 0 Killinochchi 0 0 0 0 0 0 1 1 0 0 Anuradhapura 2 2 9 3 8 4 19 20 18 32 Polonnaruwa 2 5 2 1 1 3 9 0 3 4 Matale 5 1 1 0 1 5 0 7 1 8 Badulla 7 7 3 1 1 2 0 5 7 12 Monaragala 2 0 0 0 0 6 1 1 1 0 Hambantota 5 2 0 0 0 3 0 0 6 37 Ratnapura 18 17 18 2 11 5 12 13 8 102 Kandy 7 13 46 7 24 83 119 80 45 141 Kurunegala 6 4 5 4 6 304 45 24 35 248 Kegalle 2 6 0 1 2 26 7 27 27 74 Nuwara Eliya 2 7 5 0 1 1 5 2 1 0 Kalutara 29 24 39 9 17 32 26 40 67 97 Matara 2 0 3 0 1 3 58 31 14 205 Galle 28 12 22 3 14 27 29 57 37 146 Puttalam 10 15 37 12 3 23 15 13 13 49 Colombo 621 465 478 140 294 525 543 781 972 1550 Batticaloa 4 2 1 0 1 58 1 0 2 2 Trincomalee 1 0 1 0 0 1 1 0 3 12 Gampaha 113 69 76 23 47 179 85 166 419 619 Ampara 1 4 2 0 0 4 0 1 5 0 Jaffna 0 0 1 0 3 0 2 0 2 2 Mullativu 0 0 0 0 0 0 6 0 0 0 Mannar 0 0 0 0 0 0 1 0 0 1 Total 867 655 749 206 435 1294 988 1271 1686 3341



1995-2000 Puthalam District Malaria cases by Monthly.

1995 1996 1997 1998 1999 2000 Av

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 1464 879 687 588 648 578 506 409 313 391 703 1072 8238 2519 2306 1472 887 1008 1222 1702 1378 1266 1881 2593 3139 21373 2844 1767 956 552 614 1013 1041 1087 718 1005 1452 1746 14795 1928 1215 743 345 315 577 681 698 643 614 946 728 9433 984 1538 982 532 567 549 563 422 408 555 1145 2359 10604 2596 1841 1416 631 650 679 676 454 457 410 475 371 10656 2056 1591 1043 589.2 633.67 769.7 861.5 741.3 634.2 809.3 1219 1569 12517

1995-2000 Puthalam District Rainfall by Monthly Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 1994 205 20.79 1995 40.3 43.7 74.48 304.2 253 72.95 15.6 14.58 8.58 179.5 499 23.95 1529 1996 51.33 93.2 5.9 174.5 44.76 103.2 19.88 87.56 151.5 194.4 154 83.78 1164 1997 1.8 18.15 10.85 88.49 154.9 65.25 69.31 7.89 172 329.6 442 98.9 1459 1998 23.49 9.6 53.33 97.64 191.76 60.26 116.73 58.4 92.48 196.5 253 240 1393 1999 63.2 80.43 8.8 170.2 159.2 41.85 30.93 11.46 89.1 453 226 51.4 1385 2000 124.1 99.1 86.3 176.5 66.23 54.7 5.6 111.6 114.5 86.3 1229 Avg. 50.7 57.363 39.94 168.6 144.98 66.36 43.008 48.58 104.7 239.9 296 86.5 1336

1995-2000 Average Malaria cases & Average Rainfall data by Monthly . Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Cases 2056 1591 1043 589 634 770 861 741 634 809 1219 1569 Rainfall 50.7 57.36 39.94 168.6 144.98 66.36 43 48.58 104.7 239.9 296 86.5

1995 Average Malaria cases & Two Month before Average Rainfall data.

Cases Rainfall

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2056 1591 1043 589 634 770 861 741 634 809 1219 1569 296 86.5 50.7 57.36 39.94 168.6 144.98 66.36 43 48.58 105 239.9



Rainfall Putalam MOH area 1995-2000

Jan Rain 94 Rain 95 Rain 96 Rain 97 Rain 98 Rain 99 Rain 00

Feb

106.3 65.7 0.1 29.55 174.2 115.2 81.82

Mar Apr

52.1 57.55 8.7 20.05 100.5 68.75 51.28

May

Jun

Jul

Aug

Sep

60.1 210.5 230.2 5.7 16.8 0.7 0.2 174.5 40.3 98.55 1 36 3.7 44.85 126.7 65.2 15.85 0 20.8 41.45 205.1 9.85 129.7 21.75 3.1 135.45 54.6 7.85 0.65 2.15 52.8 171.75 5.45 6.6 0 61.95 23.4 129.75 110.4 32.29 27.33 20.425

Oct

Nov Dec 204.5 119.55 0.205 169.3 471.2 121.8 139 250.75 81.55 126.8 84.25 236.55 467.2 94.45 54.65 124.8 265.75 197.15 65.7 207.15 176 160.45 103.25 53.9 170.55 180.8 74.509 173.74 262.39 143

Dengue Malaria Cases Puthalam MOH area 1995-2000 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

95

442

265

230

354

385

269

155

122

85

96

1162

1290

840

388

420

524

679

486

413

553 1114 1303

97

1045

878

413

233

238

377

408

321

291

366

602

612

98

734

567

361

121

92

200

204

155

171

168

258

178

99

417

652

420

253

172

189

147

130

161

211

427

873

00

1080

833

600

316

274

210

199

155

155

125

185

169

277.5 263.5 294.83 298.7 228.17 212.667 249.17 452.67

572

813.333 747.5 477.33

72

Dec

130

297

Malaria Cases & Rainfall average 1995- 2000 Putalam MOH area Jan Malaria Rainfall

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 813 748 477 278 264 295 299 228 213 249 453 572 81.8 51.28 23.4 129.75 110.38 32.29 27.33 20.425 74.5 173.74 262.39 143

Malaria cases & Two Month before rainfall for Putalam MOH area Jan

Feb

Mar

Apr

May

Malaria

813

748

477

278

Rainfall

262.39

143

81.8

51.28

264

Jun 295

Jul

Aug 299

228

23.4 129.75 110.4 32.29


Sep

Oct 213

Nov 249

27.33 20.425

453

Dec 572

74.5 173.74

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……91 Karuwalagasweawa MOH area Rainfall 1995-2000 YEAR

JAN

FEB

MAR

APR

MAY

JUN JUL AUG SEP

OCT

NOV

1994 1995

38.8

53.9

79.2 247.7 209.4

1996

12.6

64.8

2.1

1997

0

15.7

3.6 129.3 118.8

1998

26.7

0

1999

88.9

84.5

0 124.9

44.5

13

2000 140.2

33.8

49.8 126.2

0

0

91.7

0

232.2

79

18

5.2

6.1

0 200.5

401.8

25.5

11.7 96.6

9.1

21.3

110.4 223.1

146.9

52.7

0

0

44.2 386.3

260.3

84.6

5.9 82.9

23.6

64.3 212.6

51.2 42.117 22.45 130.7

DEC

0

0

0

0 100.9

176.3

83.3

265.5 286.8

49.3 235.4

235.4 124.1

92.5

263.1

82.1

260

99.5 22.3 16.2 25.32 78.783 201.8 257.89 130.39

Karuealagaswewa MOH area Malaria cases 1995-2000 YEAR

JAN

FEB

MAR APR MAY JUN JUL AUG SEP

1995

380

201

129

65

1996

582

377

233

1997

657

335

1998

307

1999 2000

84

Total

80

68

61

72

301

395

1922

123

125 280 441

252

108

243

394

562

3720

193

97

103 183 176

307

165

171

299

234

2920

159

93

56

63

76

57

76

233

181

1439

268

315

246

127

174 209 182

113

80

75

161

320

2270

452

287

238

62

71

69

81

102

64

1623

441

279 188.67 88.33 104.3 151 165 147.8

53 87

86

OCT NOV DEC

85 63

47

90 119.7 248.33 292.67

Malaria & Rainfall YEAR

JAN

FEB 279

MAR

APR

189

MAY

Cases

441

88

Rainfall

51.2 42.12 22.45 130.7

104

JUN JUL AUG SEP 151

165

148

99.5 22.3 16.2 25.32

OCT 90

120

NOV 248

DEC 293

78.78 201.8 257.89 130.39

Two Month before Rainfall YEAR Cases Rainfall

JAN 441

FEB 279

257.89 130.39

MAR 189

APR

MAY 88

104

51.2 42.12 22.45

JUN JUL AUG SEP 151

165

148

131 99.5 22.25

OCT 90

120

16.2 25.32

NOV 248

DEC 293

78.78 201.78

Anamaduwa MOH area for Malaria & Rainfall Relationship YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1994 149.4 7 1995 70.2 46.2 80.6 241.2 244.5 0 0 0 0 269.3 384.4 8.3 1996 31.6 68.2 21 202.6 21 92 0 98.5 136.2 216.9 143.2 80.7 1997 0 25.2 0 119.3 239.9 44.1 34.2 0 179.8 147.5 337.4 60.3 Ideal International E- Publication www.isca.co.in

GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……92 1998 0 16 73.2 1999 66.5 60.7 33 2000 112.3 96.6 -9.9M 46.77 52.15 41.6

42.5 126.8 103.3 139.3

85.6 76.6 40.1 118

50.3 22.1 1.4 35

87.8 44.1 100.1 226.6 182.4 0 0 49.5 315.5 192 14 82.8 51.2 54.7 22.7 37.57 86.13 205.1 231.5

254.9 12.5 70.62

Malaria Cases YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Total 95 287 137 81 50 63 80 72 60 47 128 66 236 1307 96 373 190 105 48 65 104 144 166 141 267 434 645 2682 97 514 221 129 60 84 163 148 114 66 117 256 549 2421 98 509 227 97 46 43 89 103 129 121 113 140 139 1756 99 148 181 102 37 56 38 58 29 33 51 227 757 1717 2000 686 377 253 99 132 115 151 161 181 155 121 83 2514 419.5 222.2 128 56.67 73.83 98.2 113 109.8 98.17 138.5 207.3 401.5

YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Cases 419 222 128 57 74 98 113 110 98 138 207 401 Rainfall 46.77 52.15 41.6 139.3 118 35 22.7 37.57 86.13 205.1 231.5 70.62

YEAR

JAN

Cases

419

Rainfall

FEB

MAR APR

222

128

231.5 70.62

46.8

MAY 57

JUN

74

JUL

98

52.15 41.56 139.3

112

AUG

SEP

110

OCT 98

118 34.98 22.67

138

NOV

DEC

207

401

37.57 86.13

205.1

Dengue cases of Colombo District 1996-2000 Jan 96 97 98 99 2000 Avg.

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 19 19 15 23 9 22 115 129 41 49 45 39 525 42 8 14 11 17 43 47 52 50 46 130 83 543 73 51 29 61 35 53 53 106 98 79 62 81 781 59 43 45 36 8 97 103 167 89 93 135 97 972 145 88 118 146 124 165 204 153 80 137 135 55 1550 67.6 41.8 44.2 55.4 38.6 76 104.4 121.4 71.6 80.8 101.4 71

Average Rainfall of Colombo District 1996-2000


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……93 Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep

Oct

Nov

Dec

1995

19.5

1996

101.8 154.4

17.5

326

1997

20.76 71.24 78.63 223.5

345 237.5 298.89 116.77 467.5 717.5 562.2 317.7 3457

1998

134.2

17.3 63.38 207.3

336 309.8 504.51 259.48 382.4 428.9 350.7 434.8 3428

1999

104.9 238.5 92.97 249.6

475 255.7 151.23 191.71 272.3 641.7 346.5 176.2 3497

2000 200.29 184.68 171.7 217.4 112.39 133.22 84.83 244.8

106 215.4 223.57

325 223.4

240 445.5 297.1 260.6 141.4 2529

601

306.1 482.8 306.8 258.2 126.8 2863

317 248.4 355.84 222.81 410.1 478.4 355.6 217.9

Average Value of Dengue cases & Rainfall for Colombo District :1996-2000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Cases 68 42 44 55 37 76 104 121 72 81 101 71 Rianfall 112.39 133.22 84.83 244.8 317 248.4 355.84 222.81 410.1 478.4 355.6 217.9

Average Value of Dengue cases & One month before rainfall data for Colombo District : 1995-2000 by Monthly Jan

Feb

Cases Rianfall

68

Mar 42

Apr 44

May Jun 55

37

Jul 76

Aug 104

Sep 121

Oct 72

Nov 81

101

Dec 71

218 112.39 133.2 84.83 244.8 317.3 248.35 355.84 222.8 410.1 478.4 355.6

Dengue cases in Colombo MOH area 1995-2000 Jan Cases 96 97 98 99 2000

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 15 11 8 18 7 13 39 62 22 32 24 28 279 23 2 7 7 10 24 21 46 41 39 111 79 410 38 32 15 48 19 32 34 65 61 48 34 54 480 36 32 30 19 5 68 60 60 46 40 72 44 512 63 51 65 52 68 72 73 50 27 77 62 35 695 35 25.6 25 28.8 21.8 41.8 45.4 56.6 39.4 47.2 60.6 48

Rainfall in Colombo MOH area 1995-2000 Jan

Feb

1995 1996 97.9 102.1 1997 0 70 1998 57.9 0 1999 165.1 132.2 2000 102.4 177.9 84.66 96.44

Mar Apr 0.2 25.9 36.4 62.6 79.3 40.9

240 92.2 111.3 301.2 154.2 179.8

May 126.5 274.2 199.3 326.6 207.5 226.8

Jun

Jul

129.6 209.7 197 187 256.3 482.2 102.4 96.2 229.8 316 183.02 258.22

Aug

Sep

206.6 69.2 127.8 106.5 249 151.82


282.8 417.3 209 278.1 312.3 299.9

Oct 305.9 618.7 304.1 530.2 282.6 408.3

Nov

Dec 19.2 239.2 169 440.3 138 267.3 328 297.4 173 52 120 259.24 158


Dengue cases & Rainfall in Colombo MOH area 1995-2000 by Monthly Jan Cases D cases

Feb 35

Mar Apr 26

25

May 29

Jun

Jul

22

42

Aug

Sep

45

57

84.66 96.44 40.9 179.8 226.8 183.02 258.22

151.82

Oct 39

Nov 47

Dec 61

48

299.9 408.3 259.24

158

Dengue cases & One month before Rainfall data 1995-2000 by Monthly Jan Cases D cases

Feb

Mar Apr May Jun Jul Aug Sep Oct Nov Dec 35 26 25 29 22 42 45 57 39 47 61 48 158 84.66 96.4 40.9 179.8 226.8 183.02 258.22 151.82 299.9 408.3 259

YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1996 2 1 1 1 0 6 18 24 9 8 15 7 1997 4 0 2 0 0 8 12 3 2 3 14 3 1998 5 3 5 4 5 11 6 18 15 18 16 18 1999 12 5 8 10 0 12 14 29 4 6 22 15 2000 20 16 9 20 21 27 48 24 13 13 29 8 8.6 5 5 7 5.2 12.8 19.6 19.6 8.6 9.6 19.2 10.2

92 51 124 137 248

Rainfall Dehewalla MOH area YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1995 14.5 1996 71.9 60.3 3.5 266.2 97.5 104.8 261.6 181 208 224.9 236.7 139.3 1997 1 35 30.6 141.3 267.8 188.6 225.9 107 375.6 562.3 333.6 270.8 1998 86.9 0.1 77.3 189.3 202 227.3 440.4 173 274.9 272.3 380.7 543.3 1999 106.6 143.6 81.7 722.3 382.7 88.9 109.5 125 297.2 599.7 242.7 203.4 2000 129.7 169.6 203.7 379.3 193.6 233.7 65.4 166 308.2 345.9 331.5 178.1 79.22 81.72 79.36 339.7 228.72 168.7 220.56 150 292.8 401 305 224.9

Dengue Cases in Dehewalla MOH area Dengue Cases & Rainfall in Dehewalla JAN Cases Rainfall

FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 9 5 5 7 5 12 20 20 9 10 20 10 79.22 81.72 79.36 339.7 228.72 168.7 220.56 150 292.8 401 305 224.9

Dengue Cases & One month before rainfall YEAR Cases Rainfall

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG SEP OCT NOV DEC 9 5 5 7 15 12 15 18 9 10 20 10 224.9 79.22 81.72 79.36 339.68 228.7 188.66 261 150.5 292.8 401 305.04 Ideal International E- Publication www.isca.co.in


Acronyms Acronym ADEOS II ALOS ARIES CBERS ENVISAT EOS ERS-2 IRS NOAA SPOT

Mission Advanced Earth Observation Satellite Advanced Land Observing Satellite Australian Resource Information & Environment Satellite China-Brazil Earth Resources Satellite Environmental Satellite Earth Observation System ESA (European Space Agency) Remote Sensing Indian Remote Sensing Satellite National Oceanographic & Atmospheric Administration Système Pour l'Observation de la Terre

Acronym

Instrument

AATSR Advanced Along Track Scanning Radiometer AMI-SAR Active Microwave Instrumentation Synthetic Aperture Radar ASAR Advanced Synthetic Aperture Radar ASTER Advanced Spaceborne Thermal Emission & Reflection Radiometer AVHRR Advanced Very High Resolution Radiometer AVNIR Advanced Visible & Near Infrared Radiometer CCD Charged Couple Device Camera ETM+ Enhanced Thematic Mapper Plus GLI Global Land Imager HRV High Resolution Visible HRVIR High Resolution Visible & Infrared IR-MSS Infrared-Multispectral Scanner LISS III Linear Imaging Self-Scanning System MODIS Moderate Resolution Imaging Spectro Radiometer MOMS-2P Modular Optoelectronic Multispectral Scanner MSU-E2 Multizone High-Resolution Electronic Scanner MSU-SK Multizone Middle-Resolution Optomechanical Scanner

PAN PAN SAR-70 SeaWiFS SROSM TM

Panchromatic Panchromatic Synthetic Aperture Radar (70 cm) Sea-Viewing Wide Field-of-View Sensor Spectroradiometer for Ocean Satellite Monitoring Thematic Mapper

Acronym

Instruments

Country

GLI AVNIR ARIES CCD, IR/MSS AATSR, ASAR ASTER, MODIS AMI-SAR PAN, LISS AVHRR HRV, HRVIR

Japan Japan Australia China/Brazil Europe USA Europe India USA France

Mission

Country

ENVISAT 1 ERS-1, 2 ENVISAT 1 Terra NOAA ALOS CBERS Landsat-7 ADEOS II SPOT 1, 2 SPOT 4, 5 CBERS IRS-1C, D Terra, EOS PM 1-3 Priroda/Mir Almaz-1B Almaz-1B Priroda

ESA ESA ESA USA USA Japan China/Brazil USA Japan France France China/Brazil India USA Russia Russia Russia Russia

Resurs-O1, O2

Russia

IRS-1C, D Ikonos-2 Almaz-1B TOPEX/Poseidon Almaz-1B Landsat

India SpaceImaging Russia France/USA Russia USA

Miscellaneous

ESA

European Space Agency

TIR

Thermal Infrared

GIS:

Geographic information system

RS:

Remote Sensing

IRS:

Indian Remote Sensing

Avg:

Average


GEOGRAPHIC INFORMATION SYSTEM & REMOTE SENSING BASED ……96 ACRoRS

Asian Center for Research on Remote Sensing

MOH area

Medical officer for Health area

CLET

Center for Language and Education Training
