The role of super-spreaders in modeling of SARS-CoV-2

Rousse, François; Carlsson, Marcus; Ögren, Magnus; Wellander, Benjamin Kalischer

doi:10.1016/j.idm.2022.10.003

Cited by 1 publication

(3 citation statements)

References 30 publications

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“…The main point of this research is that extensions to both SIR and SEIR of the type mentioned above yield overall curves that are only marginally different from basic SIR, given that a level of Artificial Sterilizing Immunity (ASI) is included. First of all, after setting up the details in Section 1 of S1 File , we prove in Proposition 1.1 that the division of I into various sub-compartments have no effect whatsoever, further supporting the conclusions in [ 8 , 9 , 11 ]. In other terms, the existence of “super-spreaders” do not in any notable way affect the dynamics of disease spread.…”

Section: Resultssupporting

confidence: 72%

“…The SIR-model, and our extensions thereof, are deterministic in the sense that if we run it twice, the output is the same. Such models are believed to work well during major outbreaks, where the law of large numbers applies [ 5 , 11 ]. All our findings pertain to this situation; for modeling of e.g.…”

Section: The Mathematics Of Infectious Disease Spread Dynamicsmentioning

confidence: 99%

“…Similar results have also been established numerically for other heterogeneities, such as age and activity [ 10 ]. Curiously, variable infectivity (super-spreaders) do not have any damping effect on the spread during a major outbreak [ 11 ]. In any case, such conclusions are derived using heuristic arguments or by simply testing relevant models, and the mechanisms behind these phenomena remain poorly understood.…”

Section: Introductionmentioning

confidence: 99%

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A note on variable susceptibility, the herd-immunity threshold and modeling of infectious diseases

2023

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The unfolding of the COVID-19 pandemic has been very difficult to predict using mathematical models for infectious diseases. While it has been demonstrated that variations in susceptibility have a damping effect on key quantities such as the incidence peak, the herd-immunity threshold and the final size of the pandemic, this complex phenomenon is almost impossible to measure or quantify, and it remains unclear how to incorporate it for modeling and prediction. In this work we show that, from a modeling perspective, variability in susceptibility on an individual level is equivalent with a fraction θ of the population having an “artificial” sterilizing immunity. We also derive novel formulas for the herd-immunity threshold and the final size of the pandemic, and show that these values are substantially lower than predicted by the classical formulas, in the presence of variable susceptibility. In the particular case of SARS-CoV-2, there is by now undoubtedly variable susceptibility due to waning immunity from both vaccines and previous infections, and our findings may be used to greatly simplify models. If such variations were also present prior to the first wave, as indicated by a number of studies, these findings can help explain why the magnitude of the initial waves of SARS-CoV-2 was relatively low, compared to what one may have expected based on standard models.

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Section: Resultssupporting

confidence: 72%

Section: The Mathematics Of Infectious Disease Spread Dynamicsmentioning

confidence: 99%