Monitoring Disease Transmissibility of 2019 Novel Coronavirus Disease in Zhejiang, China

Chong, Ka Chun; Cheng, Wei; Zhao, Shi; Li, Feng; Wang, Maggie Haitian; Zee, Benny; Wei, Lesley; Xiong, Xi; Liu, Hengyan; Wang, Jingxuan

doi:10.1101/2020.03.02.20028704

Cited by 6 publications

(7 citation statements)

References 8 publications

(7 reference statements)

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“…By multiplying the value of R 0 prior to the interventions by the ratio of the dominant eigenvalues from the POLYMOD and CoMix contact matrices, we were able to calculate R 0 under the physical distancing interventions. Prior to interventions, we assumed R 0 followed a normal distribution with mean 2.6 and standard deviation of 0.54 based on a meta-analysis of the literature presented in Additional file 3 [22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Discussionmentioning

confidence: 99%

Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK

Jarvis

Zandvoort

Gimma

et al. 2020

BMC Med

612

419

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Background: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R 0 , the average number of secondary cases generated per case). Methods: We asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire was conducted online via email recruitment and documents the age and location of contacts and a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday, 24 March, 1 day after a "lockdown" was implemented across the UK. We compared measured contact patterns during the "lockdown" to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place. Results: We found a 74% reduction in the average daily number of contacts observed per participant (from 10.8 to 2.8). This would be sufficient to reduce R 0 from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37-0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22-0.53) for physical (skin to skin) contacts only. Conclusions: The physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease, and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

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Section: Discussionmentioning

confidence: 99%

Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK

Jarvis

Zandvoort

Gimma

et al. 2020

BMC Med

612

419

View full text Add to dashboard Cite

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“…Early on in the epidemic, China implemented massive lockdowns and electronic surveillance [ 15 ]. Zhejiang province declared the highest provincial level public health emergency on January 23 rd , 2020, started to suspend all inter-province land and water passenger transportation on January 27 th , and restricted city-level gatherings and travels on February 1 st , 2020 [ 14 ]. Lockdowns may severely interrupt social activities and/or are less enforceable in many other regions [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, they implemented a comprehensive set of restrictions [ 18 ] on February 1 st . The core interventions [ 14 ] may be summarized as: 1) using mass symptom-based screening to find cases; 2) isolating all detected cases; 3) quarantining persons exposed to another individual with diagnosed Covid-19 for at least 14 days; 4) ordering mass masking and social distancing to all residents. Trained health professionals investigated each confirmed case with a predefined questionnaire by which basic health and demographic information were collected.…”

Section: Methodsmentioning

confidence: 99%

The impact of social distancing, contact tracing, and case isolation interventions to suppress the COVID-19 epidemic: A modeling study

Chen

Handel

et al. 2021

Epidemics

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Introduction Most countries are dependent on nonpharmaceutical public health interventions such as social distancing, contact tracing, and case isolation to mitigate COVID-19 spread until medicines or vaccines widely available. Minimal research has been performed on the independent and combined impact of each of these interventions based on empirical case data. Methods We obtained data from all confirmed COVID-19 cases from January 7 th to February 22 nd 2020 in Zhejiang Province, China, to fit an age-stratified compartmental model using human contact information before and during the outbreak. The effectiveness of social distancing, contact tracing, and case isolation was studied and compared in simulation. We also simulated a two-phase reopening scenario to assess whether various strategies combining nonpharmaceutical interventions are likely to achieve population-level control of a second-wave epidemic. Results Our study sample included 1,218 symptomatic cases with COVID-19, of which 664 had no inter-province travel history. Results suggest that 36.5% (95% CI, 12.8–57.1) of contacts were quarantined, and approximately five days (95% CI, 2.2–11.0) were needed to detect and isolate a case. As contact networks would increase after societal and economic reopening, avoiding a second wave without strengthening nonpharmaceutical interventions compared to the first wave it would be exceedingly difficult. Conclusions Continuous attention and further improvement of nonpharmaceutical interventions are needed in second-wave prevention. Specifically, contact tracing merits further attention.

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“…Since the COVID-19 was seeded by the imported cases in many cities outside Hubei, the estimation equation from Thompson et al [21], an alternative to Wallinga and Teunis [22], was used, accounting for the risk of imported cases [23].…”

Section: Methodsmentioning

confidence: 99%

Transmissibility of coronavirus disease 2019 (COVID-19) in Chinese cities with different transmission dynamics of imported cases

Chong

Cheng

Zhao

et al. 2020

Preprint

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Background: Monitoring the time-varying reproduction number (Rt) of the disease is useful in determining whether there is sustained transmission in a population. In this study, we examined Rt of COVID-19 and compared its transmissibility between different intervention periods in Hangzhou and Shenzhen. Methods: Daily aggregated counts of confirmed imported and local cases between January 1, 2020 and March 13, 2020 were analysed. A likelihood function was constructed to estimate Rt, accounting for imported cases. Results: Although Hangzhou had fewer number of cases than Shenzhen, Shenzhen had higher proportion of imported cases than Hangzhou (83% vs 29%). Since the epidemic of COVID-19 in Shenzhen was dominated by imported cases, Rt was kept below unity through time. On the contrary, Rt was greater than unity in Hangzhou from 16 January to 7 February due to the surge in local cases. Credits to the Wuhan lockdown and outbreak response measures following the local lockdown, Rt decreased steadily and dropped below unity in mid-February. Conclusion: The lockdown measures and local outbreak responses helped reduce the potential of local transmission in Hangzhou and Shenzhen. Meanwhile, cities with similar epidemic trend could have different transmission dynamics given the variation in imported cases.

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Monitoring Disease Transmissibility of 2019 Novel Coronavirus Disease in Zhejiang, China

Abstract: 48 words 1 8

Cited by 6 publications

References 8 publications

Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK

Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK

The impact of social distancing, contact tracing, and case isolation interventions to suppress the COVID-19 epidemic: A modeling study

Transmissibility of coronavirus disease 2019 (COVID-19) in Chinese cities with different transmission dynamics of imported cases

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