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

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies
Creator	Mphil, Qinxia Wang Phd, Yuanjia Xie Phd, Shanghong Zeng Phd, Donglin
Source	MedRxiv; Medline; PMC
abstract	Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making. We propose a robust and parsimonious survival-convolution model for predicting key statistics of COVID-19 epidemics (daily new cases). 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 infection rate and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only data in the early phase (two to three 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 infection rate in these two countries. The lockdown in Italy did not further accelerate the speed at which the infection rate decreases. The effective reproduction number has staggered around R(t) = 1.0 for more than 2 weeks before decreasing to below 1.0, and the epidemic in Italy is currently under control. In the US, R(t) significantly decreased during a 2-week period after the declaration of national emergency, but afterwards the rate of decrease is substantially slower. If the trend continues after May 1, the first wave of COVID-19 may be controlled by July 26 (CI: July 9 to August 27). However, a loss of temporal effect on infection rate (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (November 19 with less than 100 daily cases) and a total of more than 2 million cases.
has issue date	2020-05-13(xsd:dateTime)
bibo:doi	10.1101/2020.04.16.20067306
bibo:pmid	32511512
has license	cc-by-nd
sha1sum (hex)	d1f12d4a9b3159579f8a4e5a97c316c4e7edb7bd
schema:url	https://doi.org/10.1101/2020.04.16.20067306
resource representing a document's title	Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies
has PubMed Central identifier	PMC7273280
has PubMed identifier	32511512
schema:publication	medRxiv
resource representing a document's body	covid:d1f12d4a9b3159579f8a4e5a97c316c4e7edb7bd#body_text
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