RT Journal Article
SR Electronic
T1 Hydrology-informed metapopulation modeling of liver fluke transmission in the Lawa Lake complex of northeast Thailand
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 569913
DO 10.1101/569913
A1 Tomás M. León
A1 Vichian Plermkamon
A1 Kittiwet Kuntiyawichai
A1 Banchob Sripa
A1 Robert C. Spear
YR 2020
UL http://biorxiv.org/content/early/2020/11/23/569913.abstract
AB While hydrologic processes are intuitively understood to influence transmission dynamics of water-related diseases, limited research exists that explicitly links hydrologic and infectious disease data. In the case of the life cycle of liver flukes, hydrology influences several transmission processes that mediate infection risk for multiple hosts. Northeast Thailand is a hotspot for liver fluke transmission and has strong seasonal flooding patterns. A metapopulation model linking local hydrologic processes with transmission of the liver fluke Opisthorchis viverrini in a lake system in northeast Thailand was developed and parameterized using infection data from 2008-2016. A rainfall-runoff model and other hydrologic data were used to assess level of connectivity between villages and the influence of upstream communities on parasite distribution in the study area. Disease transmission was modeled with metapopulations representing six village clusters around the lake using known prevalence data from humans, cats and dogs, snails, and fish. The metapopulation model improved upon the single-village model in its match to historical data patterns for the six village clusters with the introduction of the new time-variable parameters. Results suggest there are three unique hydrologic-epidemiologic regimes within the Lawa Lake system in response to upstream watersheds and risk of overland flooding that contribute to risk for O. viverrini infection. While available data may be insufficient to specifically characterize exact transmission dynamics, the practical implications of such findings are the importance of addressing connectivity for any intermediate host-based intervention. Similar approaches using hydrologic data to assess the impacts of water on pathogen transmission dynamics and inform mechanistic disease transmission models could be applied across other water-related disease systems.Competing Interest StatementThe authors have declared no competing interest.