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 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.

COVID-19 Research: Challenges to Interpret Numbers and Propose Solutions

“…On the other hand, epidemiologic studies reported in this journal have progressed our understanding of the risk factors associated with SARS-CoV-2 transmission and COVID-19 fatal outcomes (13)(14)(15)(16)(17)(18)(19). Likewise, modeling studies have shed light on effective control measures and likely trajectory of the pandemic in various settings (20)(21)(22)(23).…”

“…They estimated that the lockdown effectively reduced the transmission of the COVID-19 by a factor 7, based on an effective reproduction number R e = 0.47 during lockdown compared to the basic reproduction number R 0 = 3.2 in the early stage of the epidemic (22). With yet another approach, Wang et al developed a survival convolution model to fit the dynamics of national epidemics and estimated the effect of nationwide control interventions in selected countries through a natural quasi-experimental design (23). Their forecasting results predicted better COVID-19 transmission control in China and Korea than in Italy and the USA after relaxing restriction measures in the spring 2020 (23).…”

COVID‐19: Current knowledge in clinical features, immunological responses, and vaccine development

“…29,30 To this point, several restrictions levied have proven effective in slowing down the spread of COVID-19, but the greatest effect is obtained by applying a combination of measures. 27,31 Interestingly, during the 1918-19 Spanish Flu pandemic similar travel restrictions and quarantine measures were implemented in many countries. It is estimated that implementation of these measures during the 1918-19 pandemic resulted in a reduction of death rates by 50%.…”

“…In early stages of the pandemic there was quite some confusion, particularly in western countries, about the protective effects of facial masks and physical distancing until the established evidence surfaced 29,30 . To this point, several restrictions levied have proven effective in slowing down the spread of COVID‐19, but the greatest effect is obtained by applying a combination of measures 27,31 . Interestingly, during the 1918‐19 Spanish Flu pandemic similar travel restrictions and quarantine measures were implemented in many countries.…”

Estimation and optimal control of the multi-scale dynamics of the Covid-19

“…The first group addressed the problem of forecasting the disease in order to help decision makers to better evaluate the logistic challenges they will face [1, 3, 4, 16, 15, 24, 25, 28, 29, 37, 38, 39, 41, 45, 47, 48, 51, 61, 62]. The second category of papers focused on evaluating the effectiveness of mitigations measures prescribed by the WHO and different governments in order to define better fighting strategies [5, 9, 12, 13, 14, 29, 34, 37, 41, 44, 45, 46, 50, 53, 55, 54, 60, 62]. The last set of papers studied the social and the economical impacts of the pandemic [35, 59].…”