TY - JOUR
T1 - Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010–2014
AU - Lauer, Stephen A.
AU - Sakrejda, Krzysztof
AU - Ray, Evan L.
AU - Keegan, Lindsay T.
AU - Bi, Qifang
AU - Suangtho, Paphanij
AU - Hinjoy, Soawapak
AU - Iamsirithaworn, Sopon
AU - Suthachana, Suthanun
AU - Laosiritaworn, Yongjua
AU - Cummings, Derek A.T.
AU - Lessler, Justin
AU - Reich, Nicholas G.
N1 - Funding Information:
ACKNOWLEDGMENTS. This project was funded by NIH National Institute of Allergy and Infectious Diseases Grant 1R01AI102939 and National Institute of General Medical Sciences (NIGMS) Grant R35GM119582. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the NIH or the NIGMS. The funders had no role in study design, data collection and analysis, decision to present, or preparation of the presentation.
Funding Information:
This project was funded by NIH National Institute of Allergy and Infectious Diseases Grant 1R01AI102939 and National Institute of General Medical Sciences (NIGMS) Grant R35GM119582. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the NIH or the NIGMS. The funders had no role in study design, data collection and analysis, decision to present, or preparation of the presentation.
Publisher Copyright:
© 2018 National Academy of Sciences. All Rights Reserved.
PY - 2018/3/6
Y1 - 2018/3/6
N2 - Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000–2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010–2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.
AB - Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000–2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010–2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.
KW - Dengue
KW - Forecasting
KW - Infectious disease
KW - Statistics
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U2 - 10.1073/pnas.1714457115
DO - 10.1073/pnas.1714457115
M3 - Review article
C2 - 29463757
AN - SCOPUS:85042922887
SN - 0027-8424
VL - 115
SP - E2175-E2182
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 10
ER -