Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature

Abstract

Background: A recurrent feature of infectious diseases is the observation that different individuals show different levels of secondary transmission. This inter-individual variation in transmission potential is often quantified by the dispersion parameter k. Low values of k indicate a high degree of variability and a greater probability of superspreading events. Understanding k for COVID-19 across contexts can assist policy makers prepare for future pandemics.

Methods: A literature search following a systematic approach was carried out in PubMed, Embase, Web of Science, Cochrane Library, medRxiv, bioRxiv and arXiv to identify publications containing epidemiological findings on superspreading in COVID-19. Study characteristics, epidemiological data, including estimates for k and R0, and public health recommendations were extracted from relevant records.

Results: The literature search yielded 28 peer-reviewed studies. The mean k estimates ranged from 0.04 to 2.97. Among the 28 studies, 93% reported mean k estimates lower than one, which is considered as marked heterogeneity in inter-individual transmission potential. Recommended control measures were specifically aimed at preventing superspreading events. The combination of forward and backward contact tracing, timely confirmation of cases, rapid case isolation, vaccination and preventive measures were suggested as important components to suppress superspreading.

Conclusions: Superspreading events were a major feature in the pandemic of SARS-CoV-2. On the one hand, this made outbreaks potentially more explosive but on the other hand also more responsive to public health interventions. Going forward, understanding k is critical for tailoring public health measures to high-risk groups and settings where superspreading events occur.

Keywords: COVID-19; Heterogeneity; Overdispersion; SARS-CoV-2; Secondary transmission; Superspreading; Transmission pattern.

Fig. 1

Conceptual framework. Homogeneous and heterogeneous patterns of disease transmission require different control measures. A: Every infected individual passes on the disease to two other people on average, R0 equals two, k approaches infinity, secondary cases show a Poisson distribution with mean and variance equal to R0. As a consequence, Public Health aims at population-wide control measures. B: Infected individuals show different levels of secondary transmission, R0 equals two as in scenario A, but contrastingly k is smaller than one. Secondary cases show a Negative Binomial distribution. Public health measures can target high risk groups or settings where superspreading is likely to occur. Figure created with BioRender.com

Fig. 2

Study selection process.

Fig. 3

Geographical mapping of reported k estimates. Shown are all-group point estimates of k. Colour-coding based on the countries’ values in the range of k. Created with mapchart.net

Fig. 4

All-group point estimates of k and proportion of primary cases accounting for 80% of onward transmission. (A) All-group point estimates of k with 95% CI arranged in alphabetical order. Dashed line indicates mean, area in grey indicates 95% CI of global pooled estimate. Arrows indicate that upper 95% confidence interval reaches infinity. Point estimates in grey are not included in meta-analysis for global pooled estimate. B Proportion of most infectious primary cases that generate 80% of secondary casesarranged in alphabetical order. Dashed line indicates empirical ‘20/80 rule’.

Fig. 5

R0 and corresponding k values. Shown are extracted R0 and corresponding k values (with 95% CI). Upper CI limit not depicted if reaching infinity.

Fig. 6

k and R0 estimates for different subgroups. A Analysis by cluster type/ setting (by setting). B Analysis by cluster type/ setting (by publication). C Analysis by age group. D Analysis by symptoms. E Analysis by public health interventions.