Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Kucharski, Adam J.; Russell, Timothy; Diamond, Charlie; Liu, Yang; Edmunds, John; Funk, Sebastian; Eggo, Rosalind M

doi:10.1101/2020.01.31.20019901

Cited by 385 publications

(104 citation statements)

References 24 publications

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“…After accounting for the population size, the estimates of prevalence were indeed highly consistent (0.40% versus 0.37%). Another study, 10 which was also based on international exported cases but used a different model with an assumed population . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

Section: Discussionmentioning

confidence: 99%

“…1 Although some studies with varying sample sizes have described the clinical characteristics of patients with Covid-19, [2][3][4][5][6][7] and a previous study has reported the early transmission dynamics of the first 425 confirmed cases in Wuhan, 8 Several modelling studies have used the international cases exported from Wuhan to extrapolate the severity of epidemic in Wuhan, which estimated much larger numbers of infected cases than those officially reported, implying a substantial amount of unascertained cases. 9,10 While the huge discrepancy remained unexplained, these early models can no longer be applied since January 23 due to the intensive intra-city and inter-city traffic restriction, social distancing measures, and improvement of medical . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

Section: Introductionmentioning

confidence: 99%

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Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

Wang

Liu

Hao

et al. 2020

Preprint

209

215

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2-18. We compared epidemiological characteristics across periods and different demographic groups. We developed a susceptible-exposed-infectious-recovered model to study the epidemic and evaluate the impact of interventions. RESULTSThe median age of the cases was 57 years and 50.3% were women. The attack rate peaked in the third period and substantially declined afterwards across geographic regions, sex and age groups, except for children (age <20) whose attack rate continued to increase. Healthcare workers and elderly people had higher attack rates and severity risk increased with age. The effective reproductive number dropped from 3.86 (95% credible interval 3.74 to 3.97) before interventions to 0.32 (0.28 to 0.37) post interventions. The interventions were estimated to prevent 94.5% (93.7 to 95.2%) infections till February 18. We found that at least 59% of infected cases were unascertained in Wuhan, potentially including asymptomatic and mild-symptomatic . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.is the (which was not peer-reviewed) The copyright holder for this preprint .

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

Wang

Liu

Hao

et al. 2020

Preprint

209

215

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“…Since the 2019-nCoV (COVID-19) outbreaks in Wuhan, China, the cumulative number of confirmed cases is increasing every day, and a large number of populations all over the world are at risk. The recent study [1][2][3][4] on the efficacy of traffic blockage for the COVID-19 indicated that the the population flows will certainly increase the cumulative number of cases and the quarantine and traffic blockage will lower this risk.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Efficacy of Quarantine and Traffic Blockage for the Epidemic Caused by 2019-nCoV (COVID-19):A Simulation Analysis

Liu

et al. 2020

Preprint

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Background: Since the 2019-nCoV outbreaks in Wuhan, China, the cumulative number of confirmed cases is increasing every day, and a large number of populations all over the world are at risk. The quarantine and traffic blockage can alleviate the risk of the epidemic and the infections, henceforth evaluating the efficacy of such actions is essential to inform policy makers and raise the public awareness of the importance of self-isolation and quarantine. Method:We collected confirmed case data and the migration data, and introduced the quarantine factor and traffic blockage factor to the Flow-SEIR model. By varying the quarantine factor and traffic blockage factor, we simulated the change of the peak number and arrival time of infections, then the efficacy of these two intervation measures can be analyzed in our simulation. In our study, the self-protection at home is also included in quarantine. Results:In the simulated results, the quarantine and traffic blockage are effective for epidemic control. For Hubei province, the current quarantine factor is estimaed to be 0.405, which means around 40.5% of suceptibles who are close contacting with are in quarantine, and the current traffic blockage factor is estimaed to be 0.66, which indicates around 34% of suceptibles who had flowed out from Hubei. For the other provinces outside Hubei, the current quarantine factor is estimated to be 0.285, and the current traffic blockage factor is estimated to be 0.26. With the quarantine and traffic blockage factor increasing, the number of infections decrease dramatically. We also simulated the start dates of quarantine and traffic blockage at four time points, the simulated results show that the early of warning is also effective for epidemic containing.However, provincial level traffic blockage can only alleviate 21.06% -22.38% of the peak number of infections. In general, the quarantine is much more effective than the traffic blockage control.

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“…Due to Wuhan lock-down, Kucharski et al (2020) estimated that the median daily reproduction number had declined from 2.35 of January 16 to 1.05 of January 30, Tian et al (2020) estimated that the dispersal of infection to other cities was deferred 2.91 days (CI: 2.54-3.29). However, Read et al (2020) suggested that travel restrictions from and to Wuhan city are unlikely to be effective in halting transmission across China; with a 99% effective reduction in travel, the size of the epidemic outside of Wuhan may only be reduced by 24.9% on February 4.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

A simple model to assess Wuhan lock-down effect and region efforts during COVID-19 epidemic in China Mainland

Yuan

Chen

2020

Preprint

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Since COVID-19 emerged in early December, 2019 in Wuhan and swept across China Mainland, a series of large-scale public health interventions, especially Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement, have been taken by the government to control the epidemic. Based onBaidu Migration data and the confirmed cases data, we identified two key factors affecting the later (e.g February 27, 2020) cumulative confirmed cases in non-Wuhan region (y). One is the sum travelers from Wuhan during January 20 to January 26 (x1), which had higher infected probability but lower transmission ability because the human-to-human transmission risk of COVID-19 was confirmed and announced on January 20. The other is the "seed cases" from Wuhan before January 19, which had higher transmission ability and could be represented with the confirmed cases before January 29 (x2) due to a mean 10-day delay between infection and detection. A simple yet effective regression model then was established as follow: y= 70.0916+0.0054×x1+2.3455×x2 (n = 44, R 2 = 0.9330, P<10 -7 ). Even the lock-down date only delay or in advance 3 days, the estimated confirmed cases by February 27 in non-Wuhan region will increase 35.21% or reduce 30.74% -48.59%. Although the above interventions greatly reduced the human mobility, Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement do have a determining effect on the ongoing spread of COVID-19 across China Mainland. The strategy adopted by China has changed the fast-rising curve of newly diagnosed cases, the international community should learn from lessons of Wuhan and experience from China. Efforts of 29 Provinces and 44 prefecture-level cities against COVID-19 were also assessed preliminarily according to the interpretive model. Big data has played and will continue playing an important role in public health.

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Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Cited by 385 publications

References 24 publications

Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

Estimating the Efficacy of Quarantine and Traffic Blockage for the Epidemic Caused by 2019-nCoV (COVID-19):A Simulation Analysis

A simple model to assess Wuhan lock-down effect and region efforts during COVID-19 epidemic in China Mainland

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