Quantifying superspreading for COVID-19 using Poisson mixture distributions

Kremer, Cécile; Torneri, Andrea; Boesmans, Sien; Meuwissen, Hanne; Verdonschot, Selina; Driessche, Koen Vanden; Althaus, Christian L.; Faes, Christel; Hens, Niel

doi:10.1038/s41598-021-93578-x

Cited by 24 publications

(28 citation statements)

References 41 publications

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“…To represent heterogeneity in infectiousness and contact behavior, we used a right-truncated and untruncated Gamma distribution respectively. However, other distributions could be used to obtain similar levels of heterogeneity, which we did not test in the current study [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, we calculated P 80 : the minimal proportion of infected individuals that is responsible for 80% of cases—a measure commonly used in the superspreading literature [ 22 , 28 , 29 ]. We calculated P 80 for each simulation by ordering individuals that were infectious during the simulation according to the number of secondary cases they caused, in decreasing order.…”

Section: Methodsmentioning

confidence: 99%

“…To compare the effect of infectiousness-related heterogeneity to the effect of contact-related heterogeneity, we varied the distribution of the individual transmission probability and the individual contact factor as described in Table 1 . The values for α i and α c were chosen as follows: the value 10 represents a distribution in line with the baseline scenario (i.e., a distribution which approaches a degenerate one), with low levels of heterogeneity for either the individual transmission probability or the individual contact factor, a value of 1 was chosen as an intermediate scenario, and values 0.6, 0.4 and 0.2 were chosen to represent a high level of heterogeneity, in line with what we would expect to see in the transmission of SARS-CoV-2 [ 29 ]. The mean of the truncated Gamma distribution considered for the individual transmission probability (i.e., 0.08) was chosen so that it corresponds to an R 0 value of about 3 in the baseline scenario.…”

Section: Methodsmentioning

confidence: 99%

“…follows: the value 10 represents a distribution in line with the baseline scenario (i.e., a distribution which approaches a degenerate one), with low levels of heterogeneity for either the individual transmission probability or the individual contact factor, a value of 1 was chosen as an intermediate scenario, and values 0.6, 0.4 and 0.2 were chosen to represent a high level of heterogeneity, in line with what we would expect to see in the transmission of SARS-CoV-2 [29].…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…In 2005, Lloyd-Smith et al [ 28 ] proposed a framework to study superspreading dynamics in which the expected number of secondary cases caused by an infected individual, i.e., the individual reproduction number, is represented by a random variable, following a distribution—Lloyd-Smith et al use a Negative Binomial distribution—on the positive real axis [ 28 , 29 ]. As such, superspreading events can be characterized as occurrences in the right-hand tail of the aforementioned distribution.…”

Section: Introductionmentioning

confidence: 99%

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Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2

et al. 2022

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Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes—with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.

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Section: Discussionmentioning

confidence: 99%