Table 2 Characteristics of included studies (by author in alphabetical order).
Nr | Author, Journal, Publication Year | Title | Type of dataset | Region |
|---|---|---|---|---|
1 | Adam et al. [11] Nature Medicine 2020 | Clustering and superspreading potential of SARS-CoV-2 infections in Hong Kong | Case line lists and contact tracing data | Hong Kong |
2 | Bi et al. [20] Lancet Infectious Diseases 2020 | Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study | Surveillance data and contact tracing data | Shenzhen, China |
3 | Endo et al. [7] Wellcome Open Research 2020 | Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China | WHO situation report No 38 (extraction of the number of imported and local cases) | 36 countries |
4 | Guo et al. [21] Journal of travel medicine 2022 | Superspreading potential of COVID-19 outbreak seeded by Omicron variants of SARS-CoV-2 in Hong Kong | Contact tracing data | Hong Kong |
5 | Gupta et al. [22] PLoS ONE 2022 | Contact tracing of COVID-19 in Karnataka, India: Superspreading and determinants of infectiousness and symptomatic infection | Two datasets intersect: daily COVID-19 bulletins and line list of contact tracing data | Karnataka State, India |
6 | Hasan et al. [23] Scientific Report 2020 | Superspreading in early transmissions of COVID-19 in Indonesia | Contact tracing data | Jakarta-Depok and Batam, Indonesia |
7 | He et al. [24] BMC Public Health 2020 | Low dispersion in the infectiousness of COVID-19 cases implies difficulty in control | Dataset was obtained from Xu et al. (2020) [25]: line list of confirmed case reports. Transmission pair and cluster reconstruction by inferring associations among cases [24] | Mainland China |
8 | James et al. [26] Plos ONE 2021 | Model-free estimation of COVID-19 transmission dynamics from a complete outbreak | Anonymised epidemiological data, contact tracing interviews | New Zealand |
9 | Kirkegaard et al. [6] Scientific Reports 2021 | Superspreading quantified from bursty epidemic trajectories | Epidemic aggregate data (national surveillance) from the 98 districts in Denmark; total counts of the number of infected (and tested) per day. | Denmark |
10 | Ko et al. [27] International Journal of Infectious Diseases 2022 | Secondary transmission of SARS-CoV-2 during the first two waves in Japan: Demographic characteristics and overdispersion | Epidemiological data collected by interviews of confirmed cases (demographic data, clinical information, history of high-risk activities or visit to high-risk venues, contact history) | Japan |
11 | Kremer et al. [28] Scientific Reports 2021 | Quantifying superspreading for COVID-19 using Poisson mixture distributions | 3 sets of surveillance and contact tracing data of the respective region (Hong Kong: Dataset of Adam et al.; India: dataset of Laxminarayan et al.) | Hong Kong; India; Rwanda |
12 | Kwok et al. [19] J Hosp Infect 2020 | Inferring super-spreading from transmission clusters of COVID-19 in Hong Kong, Japan, and Singapore | Public data on confirmed cases, subsequent clustering by epidemiological links (temporal and geographical grouping of one or more index and secondary cases) [19] | Hong Kong; Japan; Singapore |
13 | Lau et al. [18] PNAS 2020 | Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA | Surveillance data (including demographic information and geolocation of residence of cases), aggregate mobility data of county inhabitants | Georgia, USA |
14 | Laxminarayan et al. [14] Science 2020 | Epidemiology and transmission dynamics of COVID-19 in two Indian states | Surveillance data Contact tracing data | Tamil Nadu and Anda Pradesh State, India |
15 | Lee et al. [29] Int. Journal of Environmental Research and Public Health 2021 | Analysis of Superspreading Potential from Transmission Clusters of COVID-19 in South Korea | Epidemiological data; cluster-induced transmissions (“group of cases wherein each case can be associated with the others” [29]) | Seoul, South Korea |
16 | Miller et al. [30] Nature Communications 2020 | Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel | 213 RNA samples from nasopharyngeal swabs (from six major hospitals across Israel) | Israel |
17 | Paireau et al. [8] Eurosurveillance 2022 | Early chains of transmission of COVID-19 in France, January to March 2020 | Contact tracing data and retrospective epidemiological investigations | France |
18 | Riou et al. [17] BMC medical research methodology 2020 | Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020 | indirect estimate of epidemic size (on 18/01/2020), “based on cases identified in foreign countries before quarantine measures were implemented” [17] | Global |
19 | Ryu et al. [31] Emerg Infect Dis 2022 | Serial interval and transmission dynamics during the SARS-CoV-2 Delta variant predominance in South Korea | Contact tracing data | South Korea |
20 | Shi et al. [32] Nat Med 2021 | Effective control of SARS-CoV-2 transmission in Wanzhou, China | Epidemiological data and contact tracing data over 4 generations of an outbreak | Wanzhou, China |
21 | Sun et al. [33] Science 2020 | Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2 | Contact-tracing data | Hunan Province, China |
22 | Tariq et al. [34] BMC Med 2020 | Real-time monitoring the transmission potential of COVID-19 in Singapore, March 2020 | Surveillance and contact tracing data | Singapore |
23 | Toth et al. [10] PLOS ONE 2021 | High variability in transmission of SARS-CoV-2 within households and implications for control | “Serological SARS-CoV-2 antibody test data and prior SARS-CoV-2 test reporting” [10] of households; data paired with maximum likelihood estimate model of importation and transmission | Utah, USA |
24 | Tsang et al. [35] Epidemics 2022 | Variability in transmission risk of SARS-CoV-2 in close contact settings: A contact tracing study in Shandong Province, China | Aggregate data of cases and their contacts (contact tracing) Additional surveillance for data of clusters | Shandong Province, China |
25 | Wang et al. [36] Nature Communications 2020 | Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase | 208 SARS-CoV-2 genomic sequences with high coverage from China (obtained from GISAID) | China |
26 | Zhang et al. [37] Int. Journal of Environmental Research and Public Health 2020 | Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China | Surveillance and contact tracing data | Tianjin, China |
27 | Zhao et al. [9] BMC medical research methodology 2021 | Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19 | Dataset 1-3: Surveillance and contact tracing data of the respective region | Dataset 1: Mainland, China Dataset 2: Hong Kong Dataset 3: Tianjin, China |
28 | Zhao et al. [38] Journal of Travel Medicine 2022 | Superspreading potential of SARS-CoV-2 Delta variants under intensive disease control measures in China | Contact tracing data | Guangdong, China |