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

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  • Reich, N.G
    • Other Affiliation: Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts—Amherst, Amherst, MA, United States
  • Lauer, S.A
    • Other Affiliation: Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts—Amherst, Amherst, MA, United States
  • Sakrejda, K
    • Other Affiliation: Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts—Amherst, Amherst, MA, United States
  • Iamsirithaworn, S
    • Other Affiliation: Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
  • Hinjoy, S
    • Other Affiliation: Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
  • Suangtho, P
    • Other Affiliation: Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
  • Suthachana, S
    • Other Affiliation: Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
  • Clapham, H.E
    • Other Affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Salje, H
    • Other Affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Cummings, D.A.T
    • Other Affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
  • Lessler, J
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
    • Other Affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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.
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  • Article
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  • Attribution 4.0 International
Journal title
  • PLoS Neglected Tropical Diseases
Journal volume
  • 10
Journal issue
  • 6
Language
  • English
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  • Publisher
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  • National Institute of Allergy and Infectious Diseases, NIAID: R01AI102939, R21AI115173
Publisher
  • Public Library of Science

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