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Spatial characterization of colonies of the flying fox bat, a carrier of Nipah virus in Thailand.

Thanapongtharm W, Linard C, Wiriyarat W, Chinsorn P, Kanchanasaka B, Xiao X, Biradar C, Wallace RG, Gilbert M - BMC Vet. Res. (2015)

Bottom Line: While no evidence of infection in domestic pigs or people has been found to date, pig farming is an active agricultural sector in Thailand and therefore could be a potential pathway for zoonotic disease transmission from the bat reservoirs.Flying fox colonies are found mainly on Thailand's Central Plain, particularly in locations surrounded by bodies of water, vegetation, and safe havens such as Buddhist temples.Such proactive planning would help conserve flying fox colonies and should help prevent zoonotic transmission of Nipah and other pathogens.

View Article: PubMed Central - PubMed

Affiliation: Department of Livestock Development (DLD), Bangkok, Thailand. weeraden@yahoo.com.

ABSTRACT

Background: A major reservoir of Nipah virus is believed to be the flying fox genus Pteropus, a fruit bat distributed across many of the world's tropical and sub-tropical areas. The emergence of the virus and its zoonotic transmission to livestock and humans have been linked to losses in the bat's habitat. Nipah has been identified in a number of indigenous flying fox populations in Thailand. While no evidence of infection in domestic pigs or people has been found to date, pig farming is an active agricultural sector in Thailand and therefore could be a potential pathway for zoonotic disease transmission from the bat reservoirs. The disease, then, represents a potential zoonotic risk. To characterize the spatial habitat of flying fox populations along Thailand's Central Plain, and to map potential contact zones between flying fox habitats, pig farms and human settlements, we conducted field observation, remote sensing, and ecological niche modeling to characterize flying fox colonies and their ecological neighborhoods. A Potential Surface Analysis was applied to map contact zones among local epizootic actors.

Results: Flying fox colonies are found mainly on Thailand's Central Plain, particularly in locations surrounded by bodies of water, vegetation, and safe havens such as Buddhist temples. High-risk areas for Nipah zoonosis in pigs include the agricultural ring around the Bangkok metropolitan region where the density of pig farms is high.

Conclusions: Passive and active surveillance programs should be prioritized around Bangkok, particularly on farms with low biosecurity, close to water, and/or on which orchards are concomitantly grown. Integration of human and animal health surveillance should be pursued in these same areas. Such proactive planning would help conserve flying fox colonies and should help prevent zoonotic transmission of Nipah and other pathogens.

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Predicted suitability maps for flying fox colonies on the central plain of Thailand. The maps explained by Bioclim (BC), Domain (DM), Generalized Linear Model (GLM), Generalized Additive Model (GAM), Maximum Entropy Model (MAX), Boosted Regression Tree (BRT), and Random Forest (RF). The large map shows the Ensemble model (EM) output obtained by combining the 7 SDMs weighted by their respective predictive performance.
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Fig3: Predicted suitability maps for flying fox colonies on the central plain of Thailand. The maps explained by Bioclim (BC), Domain (DM), Generalized Linear Model (GLM), Generalized Additive Model (GAM), Maximum Entropy Model (MAX), Boosted Regression Tree (BRT), and Random Forest (RF). The large map shows the Ensemble model (EM) output obtained by combining the 7 SDMs weighted by their respective predictive performance.

Mentions: The predicted values obtained from the seven SDMs were combined as an ensemble model (EM) weighted by the predictive performance of each source model (Figure 3). All models captured the strong structuring effects of distance to rivers. The presence-only models (BC and DM) showed higher predicted values (more than 0.6) than that of the others in high-suitability areas. The model with the greatest AUC for evaluation was RF followed by BRT, Maxent, GAM, GLM, Domain, and Bioclim, respectively (Figure 4). The mean AUC of EM was 0.980 for model sets (ranged from 0.969-0.989) and 0.981 for test sets (ranged from 0.971-0.991). The effect of the predictive variable on predicted response (the fitted function) of the BRT model showed that the distance to temple, the distance to water, and elevation had a negative association and the area of vegetation within 10 km had a positive association with the presence of a colony (Figure 5). The human population density showed a positive association with the fitted function when human density was greater than 100 people per square kilometer and turned negative when the density was higher than 500 people per a square kilometer. The association remained steady when the density was higher than 800 people per square kilometer. The average relative contributions were 46% (35-62%) for the distance to a temple, 43% (31-55%) for the distance to the nearest body of water, 5.0% (2.6-8.7%) for the human density, 3.3% (0.6-6.5%) for vegetation area within 10 km of radius, and 3.2% (1.4-5.0%) for elevation.Figure 3


Spatial characterization of colonies of the flying fox bat, a carrier of Nipah virus in Thailand.

Thanapongtharm W, Linard C, Wiriyarat W, Chinsorn P, Kanchanasaka B, Xiao X, Biradar C, Wallace RG, Gilbert M - BMC Vet. Res. (2015)

Predicted suitability maps for flying fox colonies on the central plain of Thailand. The maps explained by Bioclim (BC), Domain (DM), Generalized Linear Model (GLM), Generalized Additive Model (GAM), Maximum Entropy Model (MAX), Boosted Regression Tree (BRT), and Random Forest (RF). The large map shows the Ensemble model (EM) output obtained by combining the 7 SDMs weighted by their respective predictive performance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4389713&req=5

Fig3: Predicted suitability maps for flying fox colonies on the central plain of Thailand. The maps explained by Bioclim (BC), Domain (DM), Generalized Linear Model (GLM), Generalized Additive Model (GAM), Maximum Entropy Model (MAX), Boosted Regression Tree (BRT), and Random Forest (RF). The large map shows the Ensemble model (EM) output obtained by combining the 7 SDMs weighted by their respective predictive performance.
Mentions: The predicted values obtained from the seven SDMs were combined as an ensemble model (EM) weighted by the predictive performance of each source model (Figure 3). All models captured the strong structuring effects of distance to rivers. The presence-only models (BC and DM) showed higher predicted values (more than 0.6) than that of the others in high-suitability areas. The model with the greatest AUC for evaluation was RF followed by BRT, Maxent, GAM, GLM, Domain, and Bioclim, respectively (Figure 4). The mean AUC of EM was 0.980 for model sets (ranged from 0.969-0.989) and 0.981 for test sets (ranged from 0.971-0.991). The effect of the predictive variable on predicted response (the fitted function) of the BRT model showed that the distance to temple, the distance to water, and elevation had a negative association and the area of vegetation within 10 km had a positive association with the presence of a colony (Figure 5). The human population density showed a positive association with the fitted function when human density was greater than 100 people per square kilometer and turned negative when the density was higher than 500 people per a square kilometer. The association remained steady when the density was higher than 800 people per square kilometer. The average relative contributions were 46% (35-62%) for the distance to a temple, 43% (31-55%) for the distance to the nearest body of water, 5.0% (2.6-8.7%) for the human density, 3.3% (0.6-6.5%) for vegetation area within 10 km of radius, and 3.2% (1.4-5.0%) for elevation.Figure 3

Bottom Line: While no evidence of infection in domestic pigs or people has been found to date, pig farming is an active agricultural sector in Thailand and therefore could be a potential pathway for zoonotic disease transmission from the bat reservoirs.Flying fox colonies are found mainly on Thailand's Central Plain, particularly in locations surrounded by bodies of water, vegetation, and safe havens such as Buddhist temples.Such proactive planning would help conserve flying fox colonies and should help prevent zoonotic transmission of Nipah and other pathogens.

View Article: PubMed Central - PubMed

Affiliation: Department of Livestock Development (DLD), Bangkok, Thailand. weeraden@yahoo.com.

ABSTRACT

Background: A major reservoir of Nipah virus is believed to be the flying fox genus Pteropus, a fruit bat distributed across many of the world's tropical and sub-tropical areas. The emergence of the virus and its zoonotic transmission to livestock and humans have been linked to losses in the bat's habitat. Nipah has been identified in a number of indigenous flying fox populations in Thailand. While no evidence of infection in domestic pigs or people has been found to date, pig farming is an active agricultural sector in Thailand and therefore could be a potential pathway for zoonotic disease transmission from the bat reservoirs. The disease, then, represents a potential zoonotic risk. To characterize the spatial habitat of flying fox populations along Thailand's Central Plain, and to map potential contact zones between flying fox habitats, pig farms and human settlements, we conducted field observation, remote sensing, and ecological niche modeling to characterize flying fox colonies and their ecological neighborhoods. A Potential Surface Analysis was applied to map contact zones among local epizootic actors.

Results: Flying fox colonies are found mainly on Thailand's Central Plain, particularly in locations surrounded by bodies of water, vegetation, and safe havens such as Buddhist temples. High-risk areas for Nipah zoonosis in pigs include the agricultural ring around the Bangkok metropolitan region where the density of pig farms is high.

Conclusions: Passive and active surveillance programs should be prioritized around Bangkok, particularly on farms with low biosecurity, close to water, and/or on which orchards are concomitantly grown. Integration of human and animal health surveillance should be pursued in these same areas. Such proactive planning would help conserve flying fox colonies and should help prevent zoonotic transmission of Nipah and other pathogens.

Show MeSH
Related in: MedlinePlus