Using spatial tools for high impact zoonotic agents surveillance design in backyard production systems in central Chile R Alegria-‐M oran1,2 , F Di Pillo1,2 , N Bravo1,2 , C Hamilton-‐West1*
1 Department of Preventive Veterinary Medicine, Faculty of Veterinary Science, University of Chile, Chile. 2 PhD P rogram in
Agriculture, Forestry and Veterinary Science, University of Chile, Chile. *
[email protected]
Abstract
Figure 2. Random sampling points by province, assigned using ArcGIS 10.
Specific loc ation o f b ackyard p roduc tion sys tems (BP S) in Chile rem ains uncle ar; this becomes a risk and a difficul ty to desi gn a s urveillance sys tem fo r earl y detec tion of high impac t zoonotic agen ts. BPS species are considered as the main c arriers o f a number of strains or sub types of p rio rity zoonotic agen ts like avi an influenza and Salmonella spp [1,2], becoming a ris k in differen t aspec t. Considerin g a s trati fied and propo rtion al rando m sampling amon g 15 provinces of the regions of Valparaiso, Libertado r General Bern ardo O’ Higgins and Me tropoli tan a de San ti ago, assumin g a 50% of prevalence of a high impact zoonotic agent (e.g. avian influenza, Salmonella spp.) and adjustin g fo r the popul ation size, resul t in 329 BPS to s ample. Using Arc Gis 10® 329 random sample points we re allocated within the study are a. Over 70% of rando m sampling points we re allocated correctl y on agricultu ral lands, afte r a post check using Goo gle Earth. Almos t 90% of the s ampling poin ts ( BPS) satisf y the 5 kilometers radius c riteri a self-‐i mposed. This app ro ach allo ws incre asing the efficiency o f samplin g and field acti vities, bu t would be muc h useful to generate maps with the locations of all BPS from Chile.
Materials and methods Target population V region of Valp araíso, VI region of Libertador Gen eral Bernardo O’Hi ggins (L GB O’ Hig gins) and XIII region Metropolitana de Santiago (Figure 1). Study design and sample size Stratified and propo rtion al random sampling bas ed on 15 pro vinces includ ed in th e stud y area., using equation 1 adjusted by equation 2, extracted from Dohoo, et al (2010) [3]:
Random allocation of sampling points was perform ed using ArcGis 10® acco rding to the s ample siz e established fo r each province ( Figure 2). Sampl e points wh ere check ed for feasibility using Goo gle Earth and Goo gle Maps tools. A radius of five kilom eters w as considered from the random sampling point allowing the possibility of sampling around points allocated on hills or places with no livestock activity.
Results After the allocation of random sampling points by pro vince and the ch ec king proc ess, 251 (76%) of th em w ere d efined as w ell positioned in relation with the f easibility of finding BPS giv en th e p roximity to land o f ag ricultural use and 78 (24% ) as b ad positioned (Tabl e 2). 89% of the random points presen ts BPS within a radius of 5 kilometers and only 11% b eing initially d efin ed as w ell positioned show a difficult to fulfill with the establish radius (Table 3). Table 2. Check process of random sampling points according to each Province.
Sampling points Province
Cardenal Caro
Where n = sample size, Zα = value of Zα for a confidence lev el (1 -‐ α), P = expected prevalence of the pathogen, q = (1 – P) and L = precision of the estimation. n ’ = 1/((1/n) + (1/N))
[eq. 2]
Where n’ = adjusted sample size, n = simple size (eq. 1), N = population size. Assuming a 50% prev alenc e, a confidenc e lev el of 95% and error of 5%. Giving a final sample size of 329 BPS distributed according each province, as detailed in Table 1. For th e es timation of the number of sampl e to tak e on each BPS equation 3 was used, extracted from Dohoo, et al (2010) [3]: n = (1 – (α)1/D) (N – (D – 1)/2)
[eq. 3]
Where n = requi red sampl e size, N = population size, D = minimum estimated number o f sick animal son the g roup and α = 1 – confidenc e lev el. Considering th e detection of at leas t a 30% of positiv es animals, th e giv en m aximum sampl e size intra-‐BP S is equal to nin e birds and 8 pigs. Figure 1. Study area and provinces: (1) Petorca; (2) Valparaíso, (3) Quillota; (4) San Felipe; (5) Los Andes; (6) San Antonio; (7) Melipilla; (8) Chacabuco; (9) Santiago; (10) Cordillera; (11) Talagante; (12) Maipo; (13) Cardenal Caro; (14) Cachapoal; (15) Colchagua.
Region Valparaíso
Subtotal Metropolitana
Subtotal LGB O'Higgins
Table 1. Demographic distribution of BPS and sample size by province and region.
Subtotal Total
Province Code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Nº of BPS breeding birds 956 500 781 770 270 384 3,661 1,910 426 244 237 387 632 3,836 1,974 3,981 2,837 8,792 16,289
Nº of BPS breeding pigs 105 35 64 136 15 72 427 202 78 61 29 36 92 498 622 323 412 1,357 2,282
-
Total 47
Table 3. Number of random sampled points located within the 5 kilometer radium.
90
Sampling points
4 (36%)
7 (64%)
11
Province
Sampled
< 5km
> 5 km
42 (71%)
17 (29%)
59
Cachapoal
47
30 (64%)
17 (26%)
-
4 (100%)
4
Cardenal Caro
90
84 (93%)
6 (7%)
-
2 (100%)
2
Chacabuco
11
9 (82%)
2 (18%)
Colchagua
59
59 (100%)
0 (0%)
Cordillera
4
2 (50%)
2 (50%)
Los Andes
2
2 (100%)
0 (0%)
Maipo
13
11 (85%)
2 (15%)
Melipilla
30
25 (83%)
5 (17%)
15
Maipo
13 (100%)
-
13
Melipilla
30 (100%)
-
30
Petorca
7 (47%)
Quillota
9 (100%)
8 (53%)
15
-
9
-
10
Petorca
San Felipe
8 (40%)
12 (60%)
20
Quillota
Santiago
5 (56%)
4 (44%)
9
Talagante
5 (100%)
-
5
Valparaiso
5 (100%)
-
5
San Antonio
Total
10 (100%)
251 (76%)
78 (24%)
329
15 (100%)
0 (0%)
9
9 (100%)
0 (0%)
San Antonio
10
10 (100%)
0 (0%)
San Felipe
20
20 (100%)
Santiago
9
9 (100%)
0 (0%)
Talagante
5
4 (80%)
1 (20%)
Valparaiso
5
3 (60%)
2 (40%)
329
292 (89%)
37 (11%)
Total
0 (0%)
Discussion The lac k of knowled g e about th e specific location of each BP S increas e time and logistic just on th e d etec tion of f easibl e point inc reasing du ration of s ampling acti vities. Land us e could increas e th e tim e spend in finding a BPS to sampl e. Global tend ency l eads to th e incorporation of sp ati al tools fo r th e d esign and impl emen tation of su rv eillanc e pro grams and the sto rag e of geo -‐coding data fo r animal health res earch [4]. And this may help to establish risk b as ed surveillanc e p rog rams th at will help to optimize tim e, human and budget resourc es impro ving th e qu ality of the p rog ram. Th e ch ecking p roc ess becom es very impo rtant at th e time o f planning field acti vities inc reasing th e ch anc e of visit plac es with BPS that are feasible of sampling.
Sample Size 15 5 9 20 2 10 61 30 11 9 4 5 13 72 90 47 59 196 329
90 (100%)
24 (51%)
Colchagua Los Andes
[eq. 1]
23 (49%)
Chacabuco Cordillera
n = Z2 α Pq/L2
Well positioned (%) Bad positioned (%)
Cachapoal
Acknowledgements and funding Funded by: -‐ FONDECYT 11121389 -‐ DCSAV
-‐ -‐
CONICYT Nº21130159 CONICYT Nº81150003
References 1. 2. 3. 4.
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