Main content area

A spatial assessment of Nipah virus transmission in Thailand pig farms using multi-criteria decision analysis

Thanapongtharm, Weerapong, Paul, Mathilde C., Wiratsudakul, Anuwat, Wongphruksasoong, Vilaiporn, Kalpravidh, Wantanee, Wongsathapornchai, Kachen, Damrongwatanapokin, Sudarat, Schar, Daniel, Gilbert, Marius
BMC veterinary research 2019 v.15 no.1 pp. 73
Nipah virus, Pteropodidae, biosecurity, disease surveillance, environmental factors, epizootic diseases, experts, farms, forests, human population, humans, intermediate hosts, livestock and meat industry, models, multi-criteria decision making, orchards, pathogens, population density, risk factors, surface water, swine, virus transmission, Thailand
BACKGROUND: Thailand’s Central Plain is identified as a contact zone between pigs and flying foxes, representing a potential zoonotic risk. Nipah virus (NiV) has been reported in flying foxes in Thailand, but it has never been found in pigs or humans. An assessment of the suitability of NiV transmission at the spatial and farm level would be useful for disease surveillance and prevention. Multi-criteria decision analysis (MCDA), a knowledge-driven model, was used to map contact zones between local epizootic risk factors as well as to quantify the suitability of NiV transmission at the pixel and farm level. RESULTS: Spatial risk factors of NiV transmission in pigs were identified by experts as being of three types, including i) natural host factors (bat preferred areas and distance to the nearest bat colony), ii) intermediate host factors (pig population density), and iii) environmental factors (distance to the nearest forest, distance to the nearest orchard, distance to the nearest water body, and human population density). The resulting high suitable areas were concentrated around the bat colonies in three provinces in the East of Thailand, including Chacheongsao, Chonburi, and Nakhonnayok. The suitability of NiV transmission in pig farms in the study area was quantified as ranging from very low to medium suitability. CONCLUSIONS: We believe that risk-based surveillance in the identified priority areas may increase the chances of finding out NiV and other bat-borne pathogens and thereby optimize the allocation of financial resources for disease surveillance. In the long run, improvements of biosecurity in those priority areas may also contribute to preventing the spread of potential emergence of NiV and other bat-borne pathogens.