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Epidemics of communicable diseases place a huge burden on public healthinfrastructures across the world. Producing accurate and actionable forecastsof infectious disease incidence at short and long time scales will improvepublic health response to outbreaks. However, scientists and public healthofficials face many obstacles in trying to create accurate and actionablereal-time forecasts of infectious disease incidence. Dengue is a mosquito-bornevirus that annually infects over 400 million people worldwide. We developed areal-time forecasting model for dengue hemorrhagic fever in the 77 provinces ofThailand. We created an operational and computational infrastructure thatgenerated multi-step predictions of dengue incidence in Thai provinces everytwo weeks throughout 2014. These predictions show mixed performance acrossprovinces, out-performing na\"ive seasonal models in over half of provinces ata 1.5 month horizon. Additionally, to assess the degree to which delays in casereporting make long-range prediction a challenging task, we compared theperformance of our real-time predictions with predictions made with fullyreported data. This paper provides valuable lessons for the implementation ofreal-time predictions in the context of public health decision making.

Infrastructure and methods for real-time predictions of the 2014 dengue fever season in Thailand