02123nas a2200313   4500000000100000008004100001653001300042653002400055653003300079653001600112653001700128100001200145700001200157700001500169700002100184700001300205700001500218700001700233700001500250700001200265700001500277700001400292245009800306856009000404300001300494490000700507520128100514022001401795       2016                            d10aThailand10aSurveillance system10aNegelected Tropical Diseases10aForecasting10aDengue fever1 aReich N1 aLauer S1 aSakrejda K1 aIamsirithaworn S1 aHinjoy S1 aSuangtho P1 aSuthachana S1 aClapham HE1 aSalje H1 aCummings D1 aLessler J00aChallenges in real-time prediction of infectious disease: A case study of dengue in Thailand.  uhttp://journals.plos.org/plosntds/article/asset?id=10.1371%2Fjournal.pntd.0004761.PDF  ae00047610 v103 a<p>Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.</p>
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