Identification of a super-spreading chain of transmission associated with COVID-19

Hu, Ke; Zhao, Yang; Wang, Mengmei; Zeng, Qiqi; Wang, Xiaorui; Wang, Ming; Zheng, Zhong; Li, Xiaochen; Zhang, Yunting; Wang, Tao; Zeng, Shaolin; Yan, Jiang; Liú, Dan; Yu, Wenzhen; Hu, Fang; Qin, Hongyu; Hao, Jingcan; Yuan, Jian; Shang, Rui; Jiang, Meng; Ding, Xi; Zhang, Binghong; Shi, Bingyin; Zhang, Chengsheng

doi:10.1101/2020.03.19.20026245

Cited by 22 publications

(21 citation statements)

References 23 publications

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“…Greater levels of individual heterogeneity in transmission lead to a less frequent occurrence of outbreaks, however outbreaks that do occur, have a tendency to occur explosively [62,63]. Several cases of COVID-19 SSEs have been identified [64][65][66]. While SSEs often remain rare events, they can have important implications for the epidemic trajectory and control efforts [30].…”

Section: Individual Variation In Transmissionmentioning

confidence: 99%

The basic reproduction number of SARS-CoV-2: a scoping review of available evidence

Barber

Griffin

Casey

et al. 2020

Preprint

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Background: The transmissibility of SARS-CoV-2 determines both the ability of the virus to invade a population and the strength of intervention that would be required to contain or eliminate the spread of infection. The basic reproduction number, R0, provides a quantitative measure of the transmission potential of a pathogen. Objective: Conduct a scoping review of the available literature providing estimates of R0 for SARS-CoV-2, provide an overview of the drivers of variation in R0 estimates and the considerations taken in the calculation of the parameter. Design: Scoping review of available literature between the 01 December 2019 and 07 May 2020. Data sources: Both peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv. Selection criteria: Studies were selected for review if (i) the estimation of R0 represented either the initial stages of the outbreak or the initial stages of the outbreak prior to the onset of widespread population restriction (lockdown), (ii) the exact dates of the study period were provided and (iii) the study provided primary estimates of R0. Results: A total of 20 R0 estimates were extracted from 15 studies. There was substantial variation in the estimates reported. Estimates derived from mathematical models fell within a wider range of 1.94-6.94 than statistical models which fell between the range of 2.2 to 4.4. Several studies made assumptions about the length of the infectious period which ranged from 5.8-20 days and the serial interval which ranged from 4.41-14 days. For a given set of parameters a longer duration of infectiousness or a longer serial interval equates to a higher R0. Several studies took measures to minimise bias in early case reporting, to account for the potential occurrence of super-spreading events, and to account for early sub-exponential epidemic growth. Conclusions: The variation in reported estimates of R0 reflects the complex nature of the parameter itself, including the context (i.e. social/spatial structure), the methodology used to estimate the parameter, and model assumptions. R0 is a fundamental parameter in the study of infectious disease dynamics however it provides limited practical applicability outside of the context in which it was estimated, and should be calculated and interpreted with this in mind.

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Section: Individual Variation In Transmissionmentioning

confidence: 99%

The basic reproduction number of SARS-CoV-2: a scoping review of available evidence

Barber

Griffin

Casey

et al. 2020

Preprint

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“…Several risk factors have been implicated with the fast spread of the virus, including super spread events 33,34 . Its further spread to different countries has been attributed to air travellers [35][36][37]6,[38][39][40][41][42] .…”

Section: Introductionmentioning

confidence: 99%

COVID-19 higher induced mortality in Chinese regions with lower air quality

Pansini

Fornacca

2020

Preprint

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COVID-19 has spread in all continents in a span of just over three months, escalating into a pandemic that poses several humanitarian as well as scientific challenges. We here investigated the geographical expansion of the infection and correlate it with the annual indexes of air quality observed from the Sentinel-5 satellite orbiting around China, Italy and the U.S.A. Controlling for population size, we find more viral infections in those areas afflicted by Carbon Monoxide (CO) and Nitrogen Dioxide (NO2). Higher mortality was also correlated with poor air quality, namely with high PM2.5, CO and NO2 values. In Italy, the correspondence between poor air quality and SARS-CoV-2 appearance and induced mortality was the starkest. Similar to smoking, people living in polluted areas are more vulnerable to SARS-CoV-2 infections and induced mortality. This further suggests the detrimental impact climate change will have on the trajectory of future epidemics.Significance -We found a significant correlation between levels of air quality and COVID-19 spread and mortality in China, Italy and the United States. Despite the infection being still ongoing at a global level, these correlations are relatively robust not being influenced by varying population densities. Living in an area with low air quality seems to be a risk factor for becoming infected and dying from this new form of coronavirus.

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“…Thus, such reports need to be integrated using contact tracing analysis to construct transmission graphs, which provide crucial clues for the identification of "patient zero" and super-spreaders. 20,21 For example, by contact tracing and constructing disease transmission chains, researchers found that the epidemic of COVID-19 in Italy had spread much earlier than February 20, 2020, when the first case was confirmed. 8 In our study, we found that one COVID-19 patient generally directly produces 0 to 4 infections in Tianjin, while in Chengdu, the number is up to 3.…”

Section: Discussionmentioning

confidence: 99%

The construction and visualization of the transmission networks for COVID-19: A potential solution for contact tracing and assessments of epidemics

Luo

Pei-hua

et al. 2020

Preprint

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The WHO described coronavirus disease 2019 (COVID-19) as a pandemic due to the speed and scale of its transmission. Without effective interventions, the rapidly increasing number of COVID-19 cases would greatly increase the burden of clinical treatments. Identifying the transmission sources and pathways is of vital importance to block transmission and allocate limited public health resources. According to the relationships among cases, we constructed disease transmission network graphs for the COVID-19 epidemic through a visualization technique based on individual reports of epidemiological data. We proposed an analysis strategy of the transmission network with the epidemiological data in Tianjin and Chengdu. The transmission networks showed different transmission characteristics. In Tianjin, an imported case can produce an average of 2.9 secondary infections and ultimately produce up to 4 generations of infections, with a maximum of 6 cases generated before being identified. In Chengdu, 45 noninformative cases and 24 cases with vague exposure information made it difficult to provide accurate information by the transmission network. The proposed analysis framework of visualized transmission networks can trace the transmission source and contacts, assess the current situation of transmission and prevention, and provide evidence for the global response and control of the COVID-19 pandemic.

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Identification of a super-spreading chain of transmission associated with COVID-19

Abstract: 341 words; main text: 2964 words

Cited by 22 publications

References 23 publications

The basic reproduction number of SARS-CoV-2: a scoping review of available evidence

The basic reproduction number of SARS-CoV-2: a scoping review of available evidence

COVID-19 higher induced mortality in Chinese regions with lower air quality

The construction and visualization of the transmission networks for COVID-19: A potential solution for contact tracing and assessments of epidemics

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