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Predicting an infectious disease can help reduce its impact by advisingpublic health interventions and personal preventive measures. Novel datastreams, such as Internet and social media data, have recently been reported tobenefit infectious disease prediction. As a case study of dengue in Brazil, wehave combined multiple traditional and non-traditional, heterogeneous datastreams (satellite imagery, Internet, weather, and clinical surveillance data)across its 27 states on a weekly basis over seven years. For each state, wenowcast dengue based on several time series models, which vary in complexityand inclusion of exogenous data. The top-performing model varies by state,motivating our consideration of ensemble approaches to automatically combinethese models for better outcomes at the state level. Model comparisons suggestthat predictions often improve with the addition of exogenous data, althoughsimilar performance can be attained by including only one exogenous data stream(either weather data or the novel satellite data) rather than combining all ofthem. Our results demonstrate that Brazil can be nowcasted at the state levelwith high accuracy and confidence, inform the utility of each individual datastream, and reveal potential geographic contributors to predictive performance.Our work can be extended to other spatial levels of Brazil, vector-bornediseases, and countries, so that the spread of infectious disease can be moreeffectively curbed.

Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil