Determination of critical decision points for COVID-19 measures in Japan

Abstract Coronavirus disease 2019 (COVID-19) has spread throughout the world. The prediction of the number of cases has become essential to governments’ ability to define policies and take countermeasures in advance. The numbers of cases have been estimated using compartment models of infectious dis...

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Autores principales:	Junu Kim, Kensaku Matsunami, Kozue Okamura, Sara Badr, Hirokazu Sugiyama
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aad82a5e3ea94a709be7279eafc7e642
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spelling	oai:doaj.org-article:aad82a5e3ea94a709be7279eafc7e6422021-12-02T15:08:11ZDetermination of critical decision points for COVID-19 measures in Japan10.1038/s41598-021-95617-z2045-2322https://doaj.org/article/aad82a5e3ea94a709be7279eafc7e6422021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95617-zhttps://doaj.org/toc/2045-2322Abstract Coronavirus disease 2019 (COVID-19) has spread throughout the world. The prediction of the number of cases has become essential to governments’ ability to define policies and take countermeasures in advance. The numbers of cases have been estimated using compartment models of infectious diseases such as the susceptible-infected-removed (SIR) model and its derived models. However, the required use of hypothetical future values for parameters, such as the effective reproduction number or infection rate, increases the uncertainty of the prediction results. Here, we describe our model for forecasting future COVID-19 cases based on observed data by considering the time delay (t delay). We used machine learning to estimate the future infection rate based on real-time mobility, temperature, and relative humidity. We then used this calculation with the susceptible-exposed-infectious-removed (SEIR) model to forecast future cases with less uncertainty. The results suggest that changes in mobility affect observed infection rates with 5–10 days of time delay. This window should be accounted for in the decision-making phase especially during periods with predicted infection surges. Our prediction model helps governments and medical institutions to take targeted early countermeasures at critical decision points regarding mobility to avoid significant levels of infection rise.Junu KimKensaku MatsunamiKozue OkamuraSara BadrHirokazu SugiyamaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Junu Kim Kensaku Matsunami Kozue Okamura Sara Badr Hirokazu Sugiyama Determination of critical decision points for COVID-19 measures in Japan
description	Abstract Coronavirus disease 2019 (COVID-19) has spread throughout the world. The prediction of the number of cases has become essential to governments’ ability to define policies and take countermeasures in advance. The numbers of cases have been estimated using compartment models of infectious diseases such as the susceptible-infected-removed (SIR) model and its derived models. However, the required use of hypothetical future values for parameters, such as the effective reproduction number or infection rate, increases the uncertainty of the prediction results. Here, we describe our model for forecasting future COVID-19 cases based on observed data by considering the time delay (t delay). We used machine learning to estimate the future infection rate based on real-time mobility, temperature, and relative humidity. We then used this calculation with the susceptible-exposed-infectious-removed (SEIR) model to forecast future cases with less uncertainty. The results suggest that changes in mobility affect observed infection rates with 5–10 days of time delay. This window should be accounted for in the decision-making phase especially during periods with predicted infection surges. Our prediction model helps governments and medical institutions to take targeted early countermeasures at critical decision points regarding mobility to avoid significant levels of infection rise.
format	article
author	Junu Kim Kensaku Matsunami Kozue Okamura Sara Badr Hirokazu Sugiyama
author_facet	Junu Kim Kensaku Matsunami Kozue Okamura Sara Badr Hirokazu Sugiyama
author_sort	Junu Kim
title	Determination of critical decision points for COVID-19 measures in Japan
title_short	Determination of critical decision points for COVID-19 measures in Japan
title_full	Determination of critical decision points for COVID-19 measures in Japan
title_fullStr	Determination of critical decision points for COVID-19 measures in Japan
title_full_unstemmed	Determination of critical decision points for COVID-19 measures in Japan
title_sort	determination of critical decision points for covid-19 measures in japan
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aad82a5e3ea94a709be7279eafc7e642
work_keys_str_mv	AT junukim determinationofcriticaldecisionpointsforcovid19measuresinjapan AT kensakumatsunami determinationofcriticaldecisionpointsforcovid19measuresinjapan AT kozueokamura determinationofcriticaldecisionpointsforcovid19measuresinjapan AT sarabadr determinationofcriticaldecisionpointsforcovid19measuresinjapan AT hirokazusugiyama determinationofcriticaldecisionpointsforcovid19measuresinjapan
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Determination of critical decision points for COVID-19 measures in Japan

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