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Different combinations of human and snake activity, including species and farming practice differences, are likely to generate differences in snakebite patterns across locations.

This is revealed in new research published in , showing results from a simulation model for predicting snakebite. The model includes data for agricultural activity across the days and seasons, as well as snake behavioral patterns, and the times and locations humans and snakes meet.

The innovative simulation model was developed by a team of researchers led by  Associate Professor  Kris Murray of the MRC Unit The Gambia at the London School of Hygiene & Tropical Medicine and Imperial College London, and Dr Takuya Iwamura at Tel Aviv University (now positioned at Oregon State University). This was done in collaboration with the Liverpool School of Tropical Medicine, Lancaster University and the University of Kelaniya.

The model is based on better understanding snake-human interactions at fine spatial scales and aimed to predict the likelihood of snakebite in various places (e.g., tea vs rubber plantations) and times (i.e., different times of the day, or months of the year).

Snakebite is a major cause of death in tropical areas, mainly in Southeast Asia and Sub-Saharan Africa. Each year, more than 1.8 million snakebites occur, leading to some 94,000 deaths, especially among agricultural workers in socioeconomically poor regions. The World Health Organization has launched a strategic programme to reduce venomous snakebite by 50%, by 2030.

Prof Murray remarks, “This is a really exciting study, in which we provide the first fine-scaled model of snakebite risk by simulating interactions between snakes and farmers. Both snakes and people go about their business at different times of the day, in different seasons and in different types of habitats - the model captures all of this to predict encounters between people and snakes. Then, we estimate the likelihood of these interactions resulting in a bite by factoring in the aggressiveness of different snake species. It’s a completely different approach to decomposing the epidemiology of snakebite; we are focussing on the ecology of snakes and their interactions with people rather than the socio-economic risk factors”.

Speaking on the novelty of the model, lead author Mr. Eyal Goldstein at Tel Aviv University says, “Many studies have assessed the statistical ties between snakebite and an assortment of environmental and sociological factors, but we aimed to create a comprehensive interdisciplinary model which includes behavioral patterns on both sides – snakes and humans -- enabling us to identify risk factors for different times and places, and to warn about these”.

The proposed model shows that climate and precipitation are major factors influencing snakebite, as they control both snake and agricultural activities, but also indicate which types of agricultural activities contribute to bite risk. It is based on data collected by the team and from other research studies in Sri Lanka, where each year 30,000 venomous snakebites cause around 400 deaths.

The researchers are working on the snakebite problem in Sri Lanka, a high-risk area for snakebite, and focus on six species of snake and different types of agricultural activity that farmers in the region (tea, rubber and rice) are engaged in.

A test of the model against existing data from Sri Lanka showed it could broadly predict the patterns of snakebite among different regions and in different seasons, as well as the relative contribution of each of the different species of snakes to the overall burden of snakebite.

Senior author Dr Iwamura says, “Our approach to connect snake-human interactions in mathematical terms reflects the underlying ecology of the snakebite system. One of our goals is to inform on the spatial and temporal dynamics of human health risks arising from encounters with wildlife, in this case with snakes.”

The predictions from the study, funded by the Medical Research Council through a Global Challenge Research Fund award, can be used to generate new hypotheses, inform future studies and guide decision making on snakebite both in Sri Lanka and in other high-risk areas.