Modelling, prediction and design of COVID-19 lockdowns by stringency and duration

Abstract The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficu...

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Autores principales:	Alberto Mellone, Zilong Gong, Giordano Scarciotti
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/43d25edfe1ca4921949a64d7c0403640
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spelling	oai:doaj.org-article:43d25edfe1ca4921949a64d7c04036402021-12-02T16:35:42ZModelling, prediction and design of COVID-19 lockdowns by stringency and duration10.1038/s41598-021-95163-82045-2322https://doaj.org/article/43d25edfe1ca4921949a64d7c04036402021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95163-8https://doaj.org/toc/2045-2322Abstract The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical “hybrid” approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.Alberto MelloneZilong GongGiordano ScarciottiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alberto Mellone Zilong Gong Giordano Scarciotti Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
description	Abstract The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical “hybrid” approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.
format	article
author	Alberto Mellone Zilong Gong Giordano Scarciotti
author_facet	Alberto Mellone Zilong Gong Giordano Scarciotti
author_sort	Alberto Mellone
title	Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
title_short	Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
title_full	Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
title_fullStr	Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
title_full_unstemmed	Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
title_sort	modelling, prediction and design of covid-19 lockdowns by stringency and duration
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/43d25edfe1ca4921949a64d7c0403640
work_keys_str_mv	AT albertomellone modellingpredictionanddesignofcovid19lockdownsbystringencyandduration AT zilonggong modellingpredictionanddesignofcovid19lockdownsbystringencyandduration AT giordanoscarciotti modellingpredictionanddesignofcovid19lockdownsbystringencyandduration
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Modelling, prediction and design of COVID-19 lockdowns by stringency and duration

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