Hydrology-informed metapopulation modeling of liver fluke transmission in the Lawa Lake complex of northeast Thailand

Abstract

A metapopulation model linking local hydrology with transmission of the liver flukeOpisthorchis viverriniin 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, while the disease transmission model was expanded from a single-village model forO. viverrinitransmission. Connectivity between villages and hydrologic variables was assessed monthly and showed strong seasonality trends. The metapopulation model improved upon the single-village model in its fit 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 forO. viverriniinfection. Similar approaches using a hydrologic submodel to inform a mechanistic disease transmission model could be applied across many water-related disease systems.Author SummaryWhile hydrology is intuitively understood to influence transmission dynamics of water-related diseases, limited research exists that explicitly links hydrologic and infectious disease data. In this work, we use an approach that leverages a rainfall-runoff model to better understand water movement into, out of, and around Lawa Lake in northeast Thailand and how that affects fate and transport of the multiple waterborne life stages ofOpisthorchis viverrini. To model disease transmission, we represent six village clusters around the lake using known infection prevalence data of humans, cats and dogs, snails, and fish to parameterize and fit a metapopulation model. The connectivity between village clusters and external inputs of parasites are derived from the hydrology data and the rainfall-runoff model. Results suggest three unique hydrologic regimes that also reflect unique patterns in disease prevalence among the different hosts. Other water-related disease systems can use similar approaches to assess the impacts of water on pathogen transmission dynamics.