Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data

Abstract We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social dist...

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Autores principales:	Santi García-Cremades, Juan Morales-García, Rocío Hernández-Sanjaime, Raquel Martínez-España, Andrés Bueno-Crespo, Enrique Hernández-Orallo, José J. López-Espín, José M. Cecilia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cec90a6817e846359a39ba6ce552d51c
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spelling	oai:doaj.org-article:cec90a6817e846359a39ba6ce552d51c2021-12-02T16:24:52ZImproving prediction of COVID-19 evolution by fusing epidemiological and mobility data10.1038/s41598-021-94696-22045-2322https://doaj.org/article/cec90a6817e846359a39ba6ce552d51c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94696-2https://doaj.org/toc/2045-2322Abstract We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 $$R^2$$ R 2 , 4.16 RMSE and 1.08 MAE.Santi García-CremadesJuan Morales-GarcíaRocío Hernández-SanjaimeRaquel Martínez-EspañaAndrés Bueno-CrespoEnrique Hernández-OralloJosé J. López-EspínJosé M. CeciliaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Santi García-Cremades Juan Morales-García Rocío Hernández-Sanjaime Raquel Martínez-España Andrés Bueno-Crespo Enrique Hernández-Orallo José J. López-Espín José M. Cecilia Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data
description	Abstract We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 $$R^2$$ R 2 , 4.16 RMSE and 1.08 MAE.
format	article
author	Santi García-Cremades Juan Morales-García Rocío Hernández-Sanjaime Raquel Martínez-España Andrés Bueno-Crespo Enrique Hernández-Orallo José J. López-Espín José M. Cecilia
author_facet	Santi García-Cremades Juan Morales-García Rocío Hernández-Sanjaime Raquel Martínez-España Andrés Bueno-Crespo Enrique Hernández-Orallo José J. López-Espín José M. Cecilia
author_sort	Santi García-Cremades
title	Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data
title_short	Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data
title_full	Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data
title_fullStr	Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data
title_full_unstemmed	Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data
title_sort	improving prediction of covid-19 evolution by fusing epidemiological and mobility data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cec90a6817e846359a39ba6ce552d51c
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_version_	1718384138957881344

Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data

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