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

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.

SUBMITTER: Wang Q

PROVIDER: S-EPMC7273280 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

Wang Qinxia Q Xie Shanghong S Wang Yuanjia Y Zeng Donglin D

medRxiv : the preprint server for health sciences 20200513

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 ...[more]

PMID: 32511512

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Project description:IntroductionCommunity mitigation strategies (CMS) have demonstrated to be effective in the reduction of transmission and incidence of COVID-19, especially in the population with symptoms associated with the disease. The aim of this study was to evaluate the association between the presence of COVID-19 symptoms and adherence to CMS in Latin American adults.MethodsWe carried out a secondary analysis of a database developed by the University of Maryland and Facebook social network during the COVID-19 pandemic. We included Latin American adults that used the Facebook platform and participated in a survey conducted from April 23 to May 23, 2020. The principal outcome variable was reported compliance with the three main CMS (physical distancing, use of face masks, and hand washing). The exposure variable included symptoms suspicious for COVID-19 defined as the presence of three or more symptoms of an acute clinical case of COVID-19. We performed generalized linear models of the Poisson family with a logarithmic link function to evaluate the association between the presence of COVID-19 symptoms and reported compliance with CMS. We calculated crude and adjusted prevalence ratios (PR) with their 95% confidence intervals (95%CI).ResultsWe analyzed 1,310,690 adults from Latin America; 48.1% were male and 42.9% were under 35 years of age. The prevalence of suspicious symptoms of COVID-19 was 18.5% and reported compliance with the three CMS was 45.3%. The countries with the highest proportion of reported compliance with the three CMS were Peru, Bolivia and Panama, while those with the lowest reported compliance were Costa Rica, Nicaragua and Honduras. In the adjusted model, people with suspicious symptoms for COVID-19 had a 14% lower compliance with the three CMS (aPR = 0.86; 95%CI: 0.85-0.87; p < 0.001).ConclusionsLess than half of the participants complied with the CMS, and those presenting suspicious symptoms for COVID-19 had lower reported compliance with the three CMS.

Dataset Information

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

Publications

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

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