Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes

Goldstein, Eyal, Erinjery, Joseph J, Martin, Gerardo, Kasturiratne, Anuradhani, Ediriweera, Dileepa Senajith, de Silva, Hithanadura Janaka, Diggle, Peter, Lalloo, David Griffith ORCID: 0000-0001-7680-2200, Murray, Kris A and Iwamura, Takuya (2021) Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes. PLOS NEGLECTED TROPICAL DISEASES, 15 (1). e0009047-.

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Official URL: http://dx.doi.org/10.1371/journal.pntd.0009047

Abstract

Snakebite causes more than 1.8 million envenoming cases annually and is a major cause of death in the tropics especially for poor farmers. While both social and ecological factors influence the chance encounter between snakes and people, the spatio-temporal processes underlying snakebites remain poorly explored. Previous research has focused on statistical correlates between snakebites and ecological, sociological, or environmental factors, but the human and snake behavioral patterns that drive the spatio-temporal process have not yet been integrated into a single model. Here we use a bottom-up simulation approach using agent-based modelling (ABM) parameterized with datasets from Sri Lanka, a snakebite hotspot, to characterise the mechanisms of snakebite and identify risk factors. Spatio-temporal dynamics of snakebite risks are examined through the model incorporating six snake species and three farmer types (rice, tea, and rubber). We find that snakebites are mainly climatically driven, but the risks also depend on farmer types due to working schedules as well as species present in landscapes. Snake species are differentiated by both distribution and by habitat preference, and farmers are differentiated by working patterns that are climatically driven, and the combination of these factors leads to unique encounter rates for different landcover types as well as locations. Validation using epidemiological studies demonstrated that our model can explain observed patterns, including temporal patterns of snakebite incidence, and relative contribution of bites by each snake species. Our predictions can be used to generate hypotheses and inform future studies and decision makers. Additionally, our model is transferable to other locations with high snakebite burden as well.

Item Type:	Article
Uncontrolled Keywords:	Animals, Humans, Snakes, Snake Bites, Incidence, Decision Making, Ecology, Ecosystem, Systems Analysis, Sri Lanka
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User:	Symplectic Admin
Date Deposited:	12 Oct 2022 09:21
Last Modified:	18 Jan 2023 20:36
DOI:	10.1371/journal.pntd.0009047
Open Access URL:	https://doi.org/10.1371/journal.pntd.0009047
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3165418