Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China

Zhang, Yunjun; Li, Yuying; Wang, Lu; Li, Mingyuan; Zhou, Xiao‐Hua

doi:10.3390/ijerph17103705

Cited by 109 publications

(88 citation statements)

References 19 publications

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“…If asymptomatic individuals are unlikely to transmit and do so with low probability, then the small fraction of cases that are symptomatic are transmitting at a high rate, in line with recently reported "super-spreading" events (47,48). Super-spreading events are instances in which a single infected individual infects a large number of people.…”

supporting

confidence: 74%

Quantifying Asymptomatic Infection and Transmission of COVID-19 in New York City using Observed Cases, Serology and Testing Capacity

Subramanian¹,

He²,

Pascual³

2020

Preprint

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The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13% to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with pre-symptomatic ones. If asymptomatic infections transmit at similar rates than symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, pre-symptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection sub-populations have reproductive numbers lower than 3. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of SARS-Cov-2 should be reconsidered.

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supporting

confidence: 74%

Quantifying Asymptomatic Infection and Transmission of COVID-19 in New York City using Observed Cases, Serology and Testing Capacity

Subramanian¹,

He²,

Pascual³

2020

Preprint

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show abstract

“…These include the fraction of infections that are asymptomatic, how infectious asymptomatic infections typically are, and the duration of the infectious period before symptoms appear. Also of importance is the probability distribution of the generation of secondary cases, 17 , 18 , 19 which is overdispersed such that most infected individuals transmit none or a few infections, and a few individuals transmit many—the so-called super-spreading events. 20 Contact tracing data provide crucial insights on this distribution, which has important consequences for COVID-19 control.…”

mentioning

confidence: 99%

COVID-19 spread in the UK: the end of the beginning?

et al. 2020

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“…Epidemiological studies on SARS-CoV-2 have emphasized the importance of heterogeneity in transmission and the need for measuring transmission events and variations at the individual levels [7][8][9][10]. These studies have emphasized the importance of heterogeneity information in addition to estimating basic reproduction number.…”

Section: Discussionmentioning

confidence: 99%

“…Mathematical modelling methods are mostly adopted for assessing population-level heterogeneity of SARS-CoV-2 infections. Modelling studies have estimated the heterogeneity in terms of over-dispersion levels and proportional variations in transmission based on simulations and assumptions [7][8][9][10]. In this background, attempts to measure individual patient-level heterogeneity in SARS-CoV-2 context using real world data could be of direct public health relevance.…”

Section: Introductionmentioning

confidence: 99%

Social network analysis methods for exploring SARS-CoV-2 contact tracing data

Nagarajan

Muniyandi

Palani

et al. 2020

BMC Med Res Methodol

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Background Contact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels. Methods Standard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as “nodes” and their epidemiological contact as “edges” was created. Directed network perspective was utilized in which directionality of infection emanated from a “source patient” towards a “target patient”. Network measures of “ degree centrality” and “betweenness centrality” were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers. Results Out-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (> = 10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient’s which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network. Conclusions Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.

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Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China

Cited by 109 publications

References 19 publications

Quantifying Asymptomatic Infection and Transmission of COVID-19 in New York City using Observed Cases, Serology and Testing Capacity

Quantifying Asymptomatic Infection and Transmission of COVID-19 in New York City using Observed Cases, Serology and Testing Capacity

COVID-19 spread in the UK: the end of the beginning?

Social network analysis methods for exploring SARS-CoV-2 contact tracing data

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