Predicting snakebites in Sri Lanka
Research Background
Snakebite is neglected tropical disease (NTD) particularly affecting rural farmers and communities living in tropical areas, with up to 1.8 million bites and up to 94,000 deaths annually. Rural farmers and communities in the tropics are also on the frontline of climate change impacts, and are increasingly adopting strategies to reduce the impacts of a changing climate on their livelihoods, health and well-being. Accordingly, snakebite risk represents the interaction between snake and human factors, but their quantification has been limited by data availability. Models of infectious disease transmission are instrumental for the mitigation of NTDs and zoonoses. Here, we represented snake-human interactions with disease transmission models to approximate geospatial estimates of snakebite incidence in Sri Lanka, a global hotspot with an estimated >30,000 envenomings and 400 deaths by snakebite annually.
In this project, we developed a framework to explore how such climate change adaptation strategies could affect snakebite patterns. Our findings show that modelling snakebite as zoonosis provides a mechanistic eco-epidemiological basis to understand snakebites, and the possible implications of global environmental and demographic change for the burden of snakebite.
This study is funded by Global Challenges Research Fundgrant administered by the UK Medical Research Council (MP/P024513/1).
Research Objective
We have tried to reveal eco-epidemiology of snakebite, which refers to the role that societal, demographic, and economic drivers of have on the heterogeneity of human-snake interactions. We tacked this challenges by using 1) statistics, 2) mathematics and 3) computer simulations.
First we applied statistical methods to evaluate national scale snakebite biting and envenoming cases (Ediriweera et al., 2021, Martin et al., 2021). We first applied spatiotemporal statistics with montly nation-wide snakebite dataset (Ediriweera et al., 2021). We also used point process models (PPMs) to explain the snakebites and envenoming with snake distributions, abundances, and behaviors (Martin et al, 2021).
Second, we have developed mechanistic model to explain snakebite incidents by applying mathmatical infectious disease transmission models (Martin et al., 2022, 2024). In this model, we explored if the snakebites occurrences can be considered as a) frequency-, b) density-dependent or c) a mixture of both using nation-wide snakebite database. When frequency-dependent, the per-capita rate at which susceptible individuals become infected depends on pathogen prevalence in the population. When density-dependent, transmission increases with infected host density.
Third, we developed a computational simulation which explicitly simulate snakes and famers chance encounters based on snake biology, farmers behaviors and environmental conditions using spatially explicit Agent based modeling (ABM) (Goldstein et al., 2021, 2023). ABM is a bottom up approach that simulates behavioural traits of individual agents, and their interactions with one another and the environment. Our model includes information on estimated snake abundance, behavioral traits relevant to snakebite, landcover preferences for different biting species, and farmer seasonal and daily activity patterns. In addition, a landcover classification derived from remote sensing data provides data on the different landcover categories present in the region, which include rice, rubber, tea, forest, and water bodies.
