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BackgroundThe incubation period of SARS-CoV-2 remains uncertain, which has important implications for estimating transmission potential, forecasting epidemic trends, and decision-making in prevention and control. PurposeTo estimate the central tendency and dispersion for incubation period of COVID-19 and, in turn, assess the effect of a certain length of quarantine for close contacts in active monitoring. Data SourcesPubMed, Embase, medRxiv, bioRxiv, and arXiv, searched up to April 26, 2020 Study SelectionCOVID-19 studies that described either individual-level incubation period data or summarized statistics for central tendency and dispersion measures of incubation period were recruited. Data ExtractionFrom each recruited study, either individual-level incubation period data or summarized statistics for central tendency and dispersion measures were extracted, as well as population characteristics including sample size, average age, and male proportion. Data SynthesisFifty-six studies encompassing 4 095 cases were included in this meta-analysis. The estimated median incubation period for general transmissions was 5.8 days [95% confidence interval (95%CI), 5.3 to 6.2 d]. Median and dispersion were higher for SARS-CoV-2 incubation compared to other viral respiratory infections. Furthermore, about 20 in 10 000 contacts in active monitoring would develop symptoms after 14 days, or below 1 in 10 000 for young-age infections or asymptomatic transmissions. LimitationSmall sample sizes for subgroups; some data were possibly used repeatedly in different studies; limited studies for outside mainland China; non-negligible intra-study heterogeneity. ConclusionThe long, dispersive incubation period of SARS-CoV-2 contributes to the global spread of COVID-19. Yet, a 14-day quarantine period is sufficient to trace and identify symptomatic infections, which while could be justified according to a better understanding of the crucial parameters.
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ABSTRATCThe rapid emergence of clinical trials on COVID-19 stimulated a wave of discussion in scientific community. We reviewed the characteristics of interventional trials from Chinese Clinical Trial Registration (ChiCTR) and ClinicalTrials.gov. A total of 171 COVID-19-related interventional trials were identified on Feb 22nd, 2020. These trials are classified into 4 categories based on treatment modalities, including chemical drugs, biological therapies, traditional Chinese medicine treatments and other therapies. Our analysis focused on the issues of stage, design, randomization, blinding, primary endpoints definition and sample size of these trials. We found some studies with potential defects including unreasonable design, inappropriate primary endpoint definition, insufficient sample size and ethical issue. Clinical trials on COVID-19 should be designed based on scientific rules, ethics and benefits for patients.
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Objectives@#Fitting and forecasting the trend of COVID-19 epidemics.@*Methods@#Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR+ CAQ dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting.@*Results@#According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR+ CAQ model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory- confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively.@*Conclusions@#The proposed SEIR+ CAQ dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.
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Objective@#To evaluate the current status of the prevention and control of coronavirus disease (COVID-19) outbreak in China, establish a predictive model to evaluate the effects of the current prevention and control strategies, and provide scientific information for decision- making departments.@*Methods@#Based on the epidemic data of COVID-19 openly accessed from national health authorities, we estimated the dynamic basic reproduction number R0(t) to evaluate the effects of the current COVID-19 prevention and control strategies in all the provinces (municipalities and autonomous regions) as well as in Wuhan and the changes in infectivity of COVID-19 over time.@*Results@#For the stability of the results, 24 provinces (municipality) with more than 100 confirmed COVID-19 cases were included in the analysis. At the beginning of the outbreak, the R0(t) showed unstable trend with big variances. As the strengthening of the prevention and control strategies, R0(t) began to show a downward trend in late January, and became stable in February. By the time of data analysis, 18 provinces (municipality) (75%) had the R0(t)s less than 1. The results could be used for the decision making to free population floating conditionally.@* Conclusions@#Dynamic R0(t) is useful in the evaluation of the change in infectivity of COVID-19, the prevention and control strategies for the COVID-19 outbreak have shown preliminary effects, if continues, it is expected to control the COVID-19 outbreak in China in near future.