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Timely and accurate knowledge of Dengue incidence is of value to public health professionals because it helps to enable the precise communication of risk, improved allocation of resources to potential interventions, and improved planning for the provision of clinical care of severe cases. Therefore, many national public health organizations make local Dengue incidence data publicly available for individuals and organizations to use to manage current risk. The availability of these data has also resulted in active research into the forecasting of Dengue incidence as a way to increase the public health value of incidence data. Here, we robustly assess timeseries- based forecasting approaches against a null model (historical average incidence) for the forecasting of incidence up to four months ahead. We used publicly available data from multiple countries: Brazil, Mexico, Singapore, Sri Lanka, and Thailand; and found that our time series methods are more accurate than the null model across all populations, especially for 1- and 2-month ahead forecasts. We tested whether the inclusion of climatic data improved forecast accuracy and found only modest, if any improvements. We also tested whether national timeseries forecasts are more accurate if made from aggregate sub-national forecasts, and found mixed results. We used our forecasting results to illustrate the high value of increased reporting speed. This framework and test data are available as an R package. The nonmechanistic approaches described here motivates further research into the use of disease-dynamic models to increase the accuracy of medium-term Dengue forecasting across multiple populations.
medrxiv Subject Collection: Infectious Diseases
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