Risk assessment and vulnerability analyses are common practices in epidemiology (Avanzi et al., 2018;Gullón et al., 2017; WHO, 2014). Evidence from around the world confirms that climate change can affect distribution and occurrence of diseases, a major concern for policy making and healthcare facilities (UN, 2007). The health of human populations is sensitive to shifts in weather patterns and other aspects of climate change (Smith et al., 2015). Weather events and climate change are important drivers of the Abstract 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. Municipalitylevel data were obtained for the state of Pará, Brazil. Generalized linear and nonlinear models were evaluated as alternative predictors for hepatitis-A transmission in Pará. The Histogram Gradient Boost (HGB) regression model was deemed the best choice (RMSE= 2.36, and higher 2 R = 0.95) among the tested models. Partial dependence analysis and permutation feature importance analysis 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 Pará 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.