The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China
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Abstract
Background
How could we anticipate the progression of the ongoing epidemic of the coronavirus disease 2019 (COVID-19) in China? As a measure of transmissibility, we aimed to estimate concurrently the time-varying reproduction number over time during the COVID-19 epidemic in China.
Methods
We extracted the epidemic data from the “Tracking the Epidemic” website of the Chinese Center for Disease Control and Prevention for the duration of January 19, 2020 and March 14, 2020. Then, we specified two plausible distributions of serial interval to apply the novel estimation method implemented in the incidence and EpiEstim packages to the data of daily new confirmed cases for robustly estimating the time-varying reproduction number in the R software.
Results
The epidemic curve of daily new confirmed cases in China peaked around February 4–6, 2020, and then declined gradually, except the very high peak on February 12, 2020 owing to the added clinically diagnosed cases of the Hubei Province. Under two specified plausible scenarios for the distribution of serial interval, both curves of the estimated time-varying reproduction numbers fell below 1.0 around February 17–18, 2020. Finally, the COVID-19 epidemic in China abated around March 7–8, 2020, indicating that the prompt and aggressive control measures of China were effective.
Conclusion
Seeing the estimated time-varying reproduction number going downhill speedily was more informative than looking for the drops in the daily number of new confirmed cases during an ongoing epidemic of infectious disease. We urged public health authorities and scientists to estimate time-varying reproduction numbers routinely during an epidemic of infectious diseases and to report them daily to the public until the end of the epidemic.
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SciScore for 10.1101/2020.04.11.20061838: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources As listed on the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/) https://cran.r-project.org/suggested: (CRAN, RRID:SCR_003005)The estimate_R function of the EpiEstim package assumes a Gamma distribution for SI by default to approximate the infectivity profile. EpiEstimsuggested: (EpiEstim, RRID:SCR_018538)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study had several limitations because we relied on some assumptions to make a rapid analysis of this …
SciScore for 10.1101/2020.04.11.20061838: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources As listed on the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/) https://cran.r-project.org/suggested: (CRAN, RRID:SCR_003005)The estimate_R function of the EpiEstim package assumes a Gamma distribution for SI by default to approximate the infectivity profile. EpiEstimsuggested: (EpiEstim, RRID:SCR_018538)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study had several limitations because we relied on some assumptions to make a rapid analysis of this ongoing epidemic feasible. First, we assumed that all new cases of COVID-19 in China were detected and put into the counts of daily new confirmed cases correctly. However, asymptomatic or mild cases of COVID-19 were likely undetected, and thus under-reported, especially in the early phase of this epidemic.6,11 In some countries, a lack of diagnostic test kits for the SARS-CoV-2 and a shortage of qualified manpower for fast testing could also cause under-reporting or delay in reporting. Second, we admitted the time delay in our estimates of R0(t) for the COVID-19 epidemic in China due to the following two time lags: (1) the duration between the time of infection and the time of symptom onset (i.e., the incubation period of infection) if infectiousness began around the time of symptom onset5 and (2) the duration between the time of symptom onset and the time of diagnosis.2 Nevertheless, if asymptomatic carriers could transmit the COVID-19,12 the first time lag would be shorter and it became the duration between the time of infection and the time of becoming infectious (i.e., the latent period of infection). Moreover, the time interval from symptom onset to diagnosis was shorter and shorter due to the full alert of society and the faster diagnostic tests.2 Third, we assumed that the distribution of SI did not change considerably over time as the epidemic progressed. However,...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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