Publication: Incorporating human mobility data improves forecasts of Dengue fever in Thailand
Issued Date
2021-12-01
Resource Type
ISSN
20452322
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2-s2.0-85099342923
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Mahidol University
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SCOPUS
Bibliographic Citation
Scientific Reports. Vol.11, No.1 (2021)
Suggested Citation
Mathew V. Kiang, Mauricio Santillana, Jarvis T. Chen, Jukka Pekka Onnela, Nancy Krieger, Kenth Engø-Monsen, Nattwut Ekapirat, Darin Areechokchai, Preecha Prempree, Richard J. Maude, Caroline O. Buckee Incorporating human mobility data improves forecasts of Dengue fever in Thailand. Scientific Reports. Vol.11, No.1 (2021). doi:10.1038/s41598-020-79438-0 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/79278
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Title
Incorporating human mobility data improves forecasts of Dengue fever in Thailand
Abstract
Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.