Imperial College London
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DrGiordanoScarciotti

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6268g.scarciotti Website

 
 
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Location

 

1118Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mellone:2021:10.1038/s41598-021-95163-8,
author = {Mellone, A and Gong, Z and Scarciotti, G},
doi = {10.1038/s41598-021-95163-8},
journal = {Scientific Reports},
pages = {1--13},
title = {Modelling, prediction and design of national COVID-19 lockdowns by stringency and duration},
url = {http://dx.doi.org/10.1038/s41598-021-95163-8},
volume = {11},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Mellone,A
AU  - Gong,Z
AU  - Scarciotti,G
DO  - 10.1038/s41598-021-95163-8
EP  - 13
PY  - 2021///
SN  - 2045-2322
SP  - 1
TI  - Modelling, prediction and design of national COVID-19 lockdowns by stringency and duration
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-021-95163-8
UR  - https://www.medrxiv.org/content/10.1101/2021.03.12.21253454v1
UR  - https://www.nature.com/articles/s41598-021-95163-8
UR  - http://hdl.handle.net/10044/1/90444
VL  - 11
ER  -
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