Dataset Information

Real-time estimation of the reproduction number of the novel coronavirus disease (COVID-19) in China in 2020 based on incidence data.

ABSTRACT: Background:Since the first appearance in Wuhan, China in December 2019, the novel coronavirus disease (COVID-19) has posed serious threats to the public health in many Chinese places and overseas. It is essential to quantify the transmissibility on real-time basis for designing public health responses. Methods:We estimated the time-varying reproduction numbers in China, Hubei province and Wuhan city by using the renewable equation determined by the serial interval (SI) of COVID-19. We compare the average reproduction numbers in different periods of time to explore the effectiveness of the public health control measures against the COVID-19 epidemic. Results:We estimated the reproduction numbers at 2.61 (95% CI: 2.47-2.75), 2.76 (95% CI: 2.54-2.95) and 2.71 (95% CI: 2.43-3.01) for China, Hubei province and Wuhan respectively. We found that the reproduction number largely dropped after the city lockdown. As of February 16, the three reproduction numbers further reduced to 0.98, 1.14 and 1.41 respectively. Conclusions:The control of COVID-19 epidemic was effective in substantially reducing the disease transmissibility in terms of the reproduction number in China reduced to 0.98 as of February 16. At the same time, the reproduction number in Wuhan was probably still larger than 1, and thus the enhancement in the public health control was recommended to maintain.

SUBMITTER: Wang K

PROVIDER: S-EPMC7327374 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Real-time estimation of the reproduction number of the novel coronavirus disease (COVID-19) in China in 2020 based on incidence data.

Wang Kai K Zhao Shi S Li Huling H Song Yateng Y Wang Lei L Wang Maggie H MH Peng Zhihang Z Li Hui H He Daihai D

Annals of translational medicine 20200601 11

<h4>Background</h4>Since the first appearance in Wuhan, China in December 2019, the novel coronavirus disease (COVID-19) has posed serious threats to the public health in many Chinese places and overseas. It is essential to quantify the transmissibility on real-time basis for designing public health responses.<h4>Methods</h4>We estimated the time-varying reproduction numbers in China, Hubei province and Wuhan city by using the renewable equation determined by the serial interval (SI) of COVID-19 ...[more]

PMID: 32617309

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Project description:The reproduction number, R, defined as the average number of secondary cases generated by a primary case, is a crucial quantity for identifying the intensity of interventions required to control an epidemic. Current estimates of the reproduction number for seasonal influenza show wide variation and, in particular, uncertainty bounds for R for the pandemic strain from 1918 to 1919 have been obtained only in a few recent studies and are yet to be fully clarified. Here, we estimate R using daily case notifications during the autumn wave of the influenza pandemic (Spanish flu) in the city of San Francisco, California, from 1918 to 1919. In order to elucidate the effects from adopting different estimation approaches, four different methods are used: estimation of R using the early exponential-growth rate (Method 1), a simple susceptible-exposed-infectious-recovered (SEIR) model (Method 2), a more complex SEIR-type model that accounts for asymptomatic and hospitalized cases (Method 3), and a stochastic susceptible-infectious-removed (SIR) with Bayesian estimation (Method 4) that determines the effective reproduction number Rt at a given time t. The first three methods fit the initial exponential-growth phase of the epidemic, which was explicitly determined by the goodness-of-fit test. Moreover, Method 3 was also fitted to the whole epidemic curve. Whereas the values of R obtained using the first three methods based on the initial growth phase were estimated to be 2.98 (95% confidence interval (CI): 2.73, 3.25), 2.38 (2.16, 2.60) and 2.20 (1.55, 2.84), the third method with the entire epidemic curve yielded a value of 3.53 (3.45, 3.62). This larger value could be an overestimate since the goodness-of-fit to the initial exponential phase worsened when we fitted the model to the entire epidemic curve, and because the model is established as an autonomous system without time-varying assumptions. These estimates were shown to be robust to parameter uncertainties, but the theoretical exponential-growth approximation (Method 1) shows wide uncertainty. Method 4 provided a maximum-likelihood effective reproduction number 2.10 (1.21, 2.95) using the first 17 epidemic days, which is consistent with estimates obtained from the other methods and an estimate of 2.36 (2.07, 2.65) for the entire autumn wave. We conclude that the reproduction number for pandemic influenza (Spanish flu) at the city level can be robustly assessed to lie in the range of 2.0-3.0, in broad agreement with previous estimates using distinct data.

Dataset Information

Real-time estimation of the reproduction number of the novel coronavirus disease (COVID-19) in China in 2020 based on incidence data.

Publications

Real-time estimation of the reproduction number of the novel coronavirus disease (COVID-19) in China in 2020 based on incidence data.

Similar Datasets

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