Modelling, prediction and design of COVID-19 lockdowns by stringency and duration
File(s)
Published version
Author(s)
Mellone, Alberto
Gong, Zilong
Scarciotti, Giordano
Type
Journal Article
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.
Date Issued
2021-12
Date Acceptance
2021-07-20
Citation
Scientific Reports, 2021, 11 (1), pp.1-13
ISSN
2045-2322
Publisher
Nature Publishing Group
Start Page
1
End Page
13
Journal / Book Title
Scientific Reports
Volume
11
Issue
1
Copyright Statement
© 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/.
License URL
Identifier
https://www.medrxiv.org/content/10.1101/2021.03.12.21253454v1
Publication Status
Published
Article Number
15708
Date Publish Online
2021-08-03