Estimation of the Time-Varying Reproduction Number of COVID-19 Outbreak in China

You, Chong; Deng, Yuping; Hu, Wenjie; Sun, Jiarui; Lin, Qiushi; Zhou, Feng; Pang, Cheng Heng; Zhang, Yuan; Chen, Zhengchao; Zhou, Xiao‐Hua

doi:10.2139/ssrn.3539694

Cited by 120 publications

(168 citation statements)

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“…exponential growth are 2.44, 4.2, and 2.67, respectively [16]. Moreover, simulation methods predict that with continuing efforts Wuhan would achieve a reproduction number of less than one soon [17]. Here, we frame immunogenetic explanations for the epidemiological dynamics of COVID-19.…”

Section: The 2019-ncov: a Microparasite That Poses A New Problem Formentioning

confidence: 99%

Immune-epidemiological parameters of the novel coronavirus – a perspective

Saghazadeh

Rezaei

2020

Expert Review of Clinical Immunology

203

183

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Section: The 2019-ncov: a Microparasite That Poses A New Problem Formentioning

confidence: 99%

Immune-epidemiological parameters of the novel coronavirus – a perspective

Saghazadeh

Rezaei

2020

Expert Review of Clinical Immunology

203

183

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“…In the ongoing COVID-19 epidemic, China has introduced drastic control measures. These appear to have significantly reduced transmission, apparently reducing the effective reproduction number (the number of new infections per infected individual) very close or below one [6,10], although there is still a lot of uncertainty about the effectiveness of control measures [5].…”

Section: Introductionmentioning

confidence: 99%

The timing of one-shot interventions for epidemic control

Lauro

Kiss

Miller

2020

Preprint

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The apparent early success in China's large-scale intervention to control the COVID-19 epidemic has led to interest in whether other countries can replicate it as well as concerns about a resurgence of the epidemic if or when China relaxes the interventions. In this paper we look at the impact of a single short-term intervention on an epidemic. We see that if an intervention cannot be sustained long-term, it has the greatest impact if it is imposed once infection levels have become large enough that there is an appreciable number of infections present. For minimising the total number infected it should start close to the peak so that there is no rebound once the intervention is stopped, while to minimise the peak prevalence, it should start earlier, allowing two peaks of comparable size rather than one very large peak. In populations with distinct subgroups, synchronized interventions are less effective than targeting the interventions in each sub-population separately.We do not attempt to clearly determine what makes an intervention sustainable or not. We believe that is a policy question. If an intervention is sustainable, it should be kept in place. Our intent is to offer insight into how best to time an intervention whose impact on society is too great to maintain.

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“…According to the simple approximated formula that R 0 = exp(γ•SI), where γ was the exponential growth rate calculated from the incidence data directly, a longer SI would lead to a higher R 0 estimate theoretically. With a shorter SI at 4.6 days, which was supported by richer datasets in [3,4], the R 0 of COVID-19 could be lower than previous estimates based on longer SI. By using the growth rate (γ) at 0.15 per day, the R 0 was found at 2.0 with SI at 4.6 days, whereas 3.3 with SI at 8 days.…”

Section: Main Text To the Editormentioning

confidence: 50%

“…We adopted the growing process proposed in [2] deterministically with a population of 11 million in Wuhan, 1 case initially onset on 5 December 2019 and a fixed step at 1 day. We consider two values of the mean serial interval (SI) that are  SI at 4.6 days estimated based on 28 records of transmission chains [3], which was largely consistent with the SI estimate at 4.4 days based on 71 records [4]; and  SI at 8 days, which was closer to the SI of the severe acute respiratory syndrome (SARS, 8.4 days), SI of the Middle East respiratory syndrome (MERS, 7.6 days).…”

Section: Main Text To the Editormentioning

confidence: 61%