Causal inference unveils how forest coverage mitigates excess snakebite cases during rainfall seasons in Colombia

Snakebite envenoming is a neglected tropical disease that affects mainly rural populations, where antivenom is scarce. Understanding environmental drivers of snakebite incidence is critical for public health preparedness. This study employs causal inference to assess the impact of rainfall on snakebite surges in Colombia, with broader implications for tropical regions. Using a spatiotemporal database of monthly snakebite case data (2007-2021), we applied machine learning models to estimate the causal effect of rainfall, considering nine atmospheric and oceanic indices, forest coverage, and rural GDP. High rainfall significantly causes excess snakebite cases (i.e., increasing the likelihood that the number of cases exceeds what is expected based on the standardized incidence ratio): a one-standard-deviation increase in rainfall (134.65 mm) led to a 2.1% rise in excess snakebite cases (95% CI 1.3-2.9). Forest coverage exhibited an inverse relationship with the impact of rainfall on excess cases, which is positive in regions with < 50% forest cover. These findings highlight the need for climate-adaptive public health strategies. Deforested regions face heightened snakebite risk during heavy rainfall, emphasizing the role of deforestation in shaping disease dynamics. As climate change alters precipitation patterns, integrating ecological and epidemiological data is crucial for forecasting and mitigating snakebite burden globally.