Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand.
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.
Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts-Amherst, Amherst, Massachusetts, United States of America.
Nicholas G
0000-0003-3503-9899
NG
Reich
Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts-Amherst, Amherst, Massachusetts, United States of America.
Stephen A
SA
Lauer
Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts-Amherst, Amherst, Massachusetts, United States of America.
Krzysztof
K
Sakrejda
Department of Disease Control, Ministry of Public Health, Bangkok, Thailand.
Sopon
S
Iamsirithaworn
Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand.
Soawapak
S
Hinjoy
Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand.
Paphanij
P
Suangtho
Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand.
Suthanun
S
Suthachana
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Hannah E
HE
Clapham
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Henrik
H
Salje
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.|Emerging Pathogens Institute, Department of Biology, University of Florida, Gainesville, Florida, United States of America.
Derek A T
DA
Cummings
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Justin
J
Lessler
D003715:Dengue / Q000453:epidemiology*; D005544:Forecasting; D008954:Models, Biological; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D013785:Thailand / Q000453:epidemiology; D013997:Time Factors