The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China
Hu, F.-C.; Wen, F.-Y.
Show abstract
BackgroundHow 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. MethodsWe 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. ResultsThe 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. ConclusionSeeing 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.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China 95%
- What is required to prevent a second major outbreak of the novel coronavirus SARS-CoV-2 upon lifting the metropolitan-wide quarantine of Wuhan city, China 95%
- Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections 89%
Similar papers in this journal
- Modeling and Prediction of the 2019 Coronavirus Disease Spreading in China Incorporating Human Migration Data 96%
- Survival analysis of hospital length of stay of novel coronavirus (COVID-19) pneumonia patients in Sichuan, China 93%
- Prediction of Daily New COVID-19 Cases - Difficulties and Possible Solutions 92%
Similar papers in this journal
- A Novel Triage Tool of Artificial Intelligence-Assisted Diagnosis Aid System for Suspected COVID-19 Pneumonia in Fever Clinics 92%
- Application of Telemedicine During the Coronavirus Disease Epidemics: A Rapid Review and Meta-Analysis 92%
- A follow-up study of children infected with SARS-CoV-2 from Western China 91%
Similar papers in this journal
- Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis 93%
- Serial interval and generation interval for respectively the imported and local infectors estimated using reported contact-tracing data of COVID-19 in China 93%
- Uncovering COVID-19 Transmission Tree: Identifying Traced and Untraced Infections in an Infection Network 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.