Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions

Abstract During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectivel...

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Autores principales:	Andres Babino, Marcelo O. Magnasco
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dca5a86c9e1247a0907c4e7041130089
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spelling	oai:doaj.org-article:dca5a86c9e1247a0907c4e70411300892021-12-02T13:19:21ZMasks and distancing during COVID-19: a causal framework for imputing value to public-health interventions10.1038/s41598-021-84679-82045-2322https://doaj.org/article/dca5a86c9e1247a0907c4e70411300892021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84679-8https://doaj.org/toc/2045-2322Abstract During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the $$R_t$$ R t parameter. Then, we postulate each jump in $$R_t$$ R t as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing $$R_t$$ R t to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 200,000 ( $$95\%$$ 95 % CI 190,000–210,000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time.Andres BabinoMarcelo O. MagnascoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andres Babino Marcelo O. Magnasco Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions
description	Abstract During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the $$R_t$$ R t parameter. Then, we postulate each jump in $$R_t$$ R t as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing $$R_t$$ R t to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 200,000 ( $$95\%$$ 95 % CI 190,000–210,000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time.
format	article
author	Andres Babino Marcelo O. Magnasco
author_facet	Andres Babino Marcelo O. Magnasco
author_sort	Andres Babino
title	Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions
title_short	Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions
title_full	Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions
title_fullStr	Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions
title_full_unstemmed	Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions
title_sort	masks and distancing during covid-19: a causal framework for imputing value to public-health interventions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dca5a86c9e1247a0907c4e7041130089
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Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions

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