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Incorporating human mobility data improves forecasts of Dengue fever in Thailand
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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806770/ https://www.ncbi.nlm.nih.gov/pubmed/33441598 http://dx.doi.org/10.1038/s41598-020-79438-0 |
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author | Kiang, Mathew V. Santillana, Mauricio Chen, Jarvis T. Onnela, Jukka-Pekka Krieger, Nancy Engø-Monsen, Kenth Ekapirat, Nattwut Areechokchai, Darin Prempree, Preecha Maude, Richard J. Buckee, Caroline O. |
author_facet | Kiang, Mathew V. Santillana, Mauricio Chen, Jarvis T. Onnela, Jukka-Pekka Krieger, Nancy Engø-Monsen, Kenth Ekapirat, Nattwut Areechokchai, Darin Prempree, Preecha Maude, Richard J. Buckee, Caroline O. |
author_sort | Kiang, Mathew V. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-7806770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78067702021-01-14 Incorporating human mobility data improves forecasts of Dengue fever in Thailand Kiang, Mathew V. Santillana, Mauricio Chen, Jarvis T. Onnela, Jukka-Pekka Krieger, Nancy Engø-Monsen, Kenth Ekapirat, Nattwut Areechokchai, Darin Prempree, Preecha Maude, Richard J. Buckee, Caroline O. Sci Rep Article 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. Nature Publishing Group UK 2021-01-13 /pmc/articles/PMC7806770/ /pubmed/33441598 http://dx.doi.org/10.1038/s41598-020-79438-0 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kiang, Mathew V. Santillana, Mauricio Chen, Jarvis T. Onnela, Jukka-Pekka Krieger, Nancy Engø-Monsen, Kenth Ekapirat, Nattwut Areechokchai, Darin Prempree, Preecha Maude, Richard J. Buckee, Caroline O. Incorporating human mobility data improves forecasts of Dengue fever in Thailand |
title | 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_short | Incorporating human mobility data improves forecasts of Dengue fever in Thailand |
title_sort | incorporating human mobility data improves forecasts of dengue fever in thailand |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806770/ https://www.ncbi.nlm.nih.gov/pubmed/33441598 http://dx.doi.org/10.1038/s41598-020-79438-0 |
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