Hepatitis?A is a waterborne infectious disease transmitted by the eponymous hepatitis?A virus (HAV). Due to the disease's sociodemographic and environmental characteristics, this study applied public census and remote sensing data to assess risk factors for hepatitis?A transmission. Municipality?level data were obtained for the state of Par?, Brazil. Generalized linear and non?linear models were evaluated as alternative predictors for hepatitis?A transmission in Par?. The Histogram Gradient Boost (HGB) regression model was deemed the best choice (urn:x-wiley:24711403:media:gh2229:gh2229-math-0001= 2.36, and higherurn:x-wiley:24711403:media:gh2229:gh2229-math-0002 = 0.95) among the tested models. Partial dependence analysis (PDA) and permutation feature importance analysis (PFI) were used to investigate the partial dependence and the relative importance values of the independent variables in the disease transmission prediction model. Results indicated a complex relationship between the disease transmission and the sociodemographic and environmental characteristics of the study area. Population size, lack of sanitation, urban clustering, year of notification, insufficient public vaccination programs, household proximity to open?air dumpsites and storm?drains, and lack of access to healthcare facilities and hospitals were sociodemographic parameters related to HAV transmission. Turbidity and precipitation were the environmental parameters closest related to disease transmission. Based on HGB model, a hepatitis?A risk map was built for Par? state. The obtained risk map can be thought of as an auxiliary tool for public health strategies. This study reinforces the need to incorporate remote sensing data in epidemiological modelling and surveillance plans for the development of early prevention strategies for hepatitis?A.
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