Micro-level social structures and the success of COVID-19 national policies
Nature Computational Science
; 2(9):595-604, 2022.
Article
Dans Anglais
| EuropePMC | ID: covidwho-2062280
ABSTRACT
Similar policies in response to the COVID-19 pandemic have resulted in different success rates. Although many factors are responsible for the variances in policy success, our study shows that the micro-level structure of person-to-person interactions—measured by the average household size and in-person social contact rate—can be an important explanatory factor. To create an explainable model, we propose a network transformation algorithm to create a simple and computationally efficient scaled network based on these micro-level parameters, as well as incorporate national-level policy data in the network dynamic for SEIR simulations. The model was validated during the early stages of the COVID-19 pandemic, which demonstrated that it can reproduce the dynamic ordinal ranking and trend of infected cases of various European countries that are sufficiently similar in terms of some socio-cultural factors. We also performed several counterfactual analyses to illustrate how policy-based scenario analysis can be performed rapidly and easily with these explainable models. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
EuropePMC
langue:
Anglais
Revue:
Nature Computational Science
Année:
2022
Type de document:
Article
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