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 go...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7aaefc37417b4e48a3ea31e2b3172190
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spelling	oai:doaj.org-article:7aaefc37417b4e48a3ea31e2b31721902021-12-02T14:12:46ZIncorporating human mobility data improves forecasts of Dengue fever in Thailand10.1038/s41598-020-79438-02045-2322https://doaj.org/article/7aaefc37417b4e48a3ea31e2b31721902021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79438-0https://doaj.org/toc/2045-2322Abstract 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.Mathew V. KiangMauricio SantillanaJarvis T. ChenJukka-Pekka OnnelaNancy KriegerKenth Engø-MonsenNattwut EkapiratDarin AreechokchaiPreecha PrempreeRichard J. MaudeCaroline O. BuckeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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
description	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.
format	article
author	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
author_facet	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
author_sort	Mathew V. Kiang
title	Incorporating human mobility data improves forecasts of Dengue fever in Thailand
title_short	Incorporating human mobility data improves forecasts of Dengue fever in Thailand
title_full	Incorporating human mobility data improves forecasts of Dengue fever in Thailand
title_fullStr	Incorporating human mobility data improves forecasts of Dengue fever in Thailand
title_full_unstemmed	Incorporating human mobility data improves forecasts of Dengue fever in Thailand
title_sort	incorporating human mobility data improves forecasts of dengue fever in thailand
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7aaefc37417b4e48a3ea31e2b3172190
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Incorporating human mobility data improves forecasts of Dengue fever in Thailand

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