Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand

Nicholas G. Reich, Stephen A. Lauer, Krzysztof Sakrejda, Sopon Iamsirithaworn, Soawapak Hinjoy, Paphanij Suangtho, Suthanun Suthachana, Hannah E. Clapham, Henrik Salje, Derek A.T. Cummings, Justin Lessler

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere0004761
JournalPLoS neglected tropical diseases
Volume10
Issue number6
DOIs
StatePublished - Jun 15 2016

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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