About: Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies
Creator	Wang, Qinxia Wang, Yuanjia Xie, Shanghong Zeng, Donglin
source	Medline; PMC
abstract	Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (R(t)) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2–3 weeks after the outbreak). A fast rate of decline in R(t) was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, R(t) significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.
has issue date	2020-07-03(xsd:dateTime)
bibo:doi	10.3389/fpubh.2020.00325
bibo:pmid	32719764
has license	cc-by
sha1sum (hex)	846895c08ebc94cf7187415f4b964fd6277fc91c
schema:url	https://doi.org/10.3389/fpubh.2020.00325
resource representing a document's title	Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies
has PubMed Central identifier	PMC7347904
has PubMed identifier	32719764
schema:publication	Front Public Health
resource representing a document's body	covid:846895c08ebc94cf7187415f4b964fd6277fc91c#body_text
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