ENVIRONMENTAL SUITABILITY FOR LYMPHATIC FILARIASIS IN NIGERIA 1 Eneanya ,
Obiora Donnelly1 1MRC
Jorge
2 Cano ,
Ilaria
1 Dorigatti ,
Rachel
2 Pullan ,
Tini
1 Garske ,
and Christl
Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, United Kingdom 2Faculty
of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, United Kingdom
Background
Results A continuous risk map of environmental suitability of LF was projected on a geographic space at a spatial resolution of 1km x 1km (Figure 4). Median ROC for the ensemble model was 0.972 (95%CI: 0.798-0.984), and median TSS was 0.815 (95%CI: 0.8120.824).
• Lymphatic filariasis (LF) is a mosquito-borne Neglected Tropical Disease (NTD), which in its advanced stages can manifest as severe lymphedema, and/or hydrocele (Figure 1). • Prevention and treatment are by the use of bed nets and treating entire endemic communities with ivermectin + albendazole + diethylcarbamizine (DEC). • In this work we: • Describe and map the ecological niche of LF in Nigeria. • Estimate population living in areas that are environmentally suitable for disease transmission.
Figure 1 – Obiora Eneanya and a severe lymphedema patient in rural Nigeria
The data •
Data were collected during mapping surveys conducted by the Federal Ministry of Health Nigeria from 2000-2013.
•
We had 1378 site-level data points covering all 36 States and the Federal Capital Territory (Figure 2).
•
For analysis, ‘Presence’ was considered when at least 1 LF case was recorded, while ‘Absence’ was considered if no LF cases were recorded in survey locations.
•
All survey sites tested for the presence of filarial antigenemia using immunochromatographic card test.
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Environmental covariates with known biological plausibility for LF occurrence were considered as predictors (Figure 3).
Figure 4 – Continuous risk map (median, lower and upper bounds) of environmental suitability
A suitability threshold of 0.711 with sensitivity of 95% and specificity of 96.2% provided the best discrimination between presence and absence values. This was then used to generate binary maps, delineating areas into ‘suitable’ or ‘unsuitable’ (Figure 5).
Figure 2 – Locations of study sites in Nigeria
Figure 3 – Climate and environmental covariates used as infection predictors
The Model • Presence/absence data were fit to 7 different ecological niche modelling algorithms.
Figure 5 – Binary risk maps (median, lower and upper bounds) of environmental suitability
Estimating population living in LF risk areas
• Data was partitioned into 2, with a random sample of 30% retained for By overlaying a gridded map of population density for Nigeria, we evaluation and 70% used to calibrate the model. used the binary maps generated to estimate that 110 (95%CI: 106• An iteration of 100 model runs was performed for each of 7 model algorithms 127) million people live in areas that are environmental suitable for LF transmission. This was calculated on a 100m x 100m scale. and the evaluation values for each run was stored and then averaged. • The area under the receiver operator curve (ROC) and True Skills Statistics Conclusions (TSS) were used as measures for model performance. • Machine-learning and ensemble modelling are powerful tools to map disease risk and are known to yield more accurate • An ensemble of two best performing modelling algorithms was used for final predictive models. projection (here, machine-learning algorithms, generalised boosted models + • The resulting maps provides a geographical framework to target random forest). control efforts and assess its potential impacts. References - Thuiller Wilfred. BIOMOD – optimizing predictions of species distribution and projecting potential future shifts under global change. Global Change Biology 2003; 9(10):1353-62. - Araújo Miguel and New Mark. Ensemble forecasting of species distributions. Trends in Ecology & Evolution. 2007; 22(1):42-7.
Funding: OE thanks the Commonwealth Scholarship Commission, United Kingdom for doctoral studentship funding. We also thank MRC for Centre funding. Contacts:
[email protected] @obi_eneanya
