@Article{LealGuimKamp:2021:AsEnSo,
               author = "Leal, Philipe Riskalla and Guimar{\~a}es, Ricardo Jos{\'e} de 
                         Paula Souza e and Kampel, Milton",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Evandro Chagas} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Associations Between Environmental and Sociodemographic Data and 
                         Hepatitis-A Transmission in Par{\'a} State (Brazil)",
              journal = "GeoHealth",
                 year = "2021",
               volume = "5",
               number = "5",
                pages = "e2020GH00327",
                month = "May",
             keywords = "geoprocessing, hepatitis-A transmission modelling, remote sensing, 
                         time-space epidemiology analyses.",
             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. Municipality-level data were 
                         obtained for the state of Par{\'a}, Brazil. Generalized linear 
                         and nonlinear models were evaluated as alternative predictors for 
                         hepatitis-A transmission in Par{\'a}. The Histogram Gradient 
                         Boost (HGB) regression model was deemed the best choice ((Formula 
                         presented.) = 2.36, and higher (Formula presented.) = 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 Par{\'a} 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.",
                  doi = "10.1029/2020GH000327",
                  url = "http://dx.doi.org/10.1029/2020GH000327",
                 issn = "2471-1403",
             language = "en",
           targetfile = "leal_associations.pdf",
        urlaccessdate = "09 maio 2024"
}