COVID-19 Superspreading Suggests Mitigation by Social Network Modulation

Nielsen, Bjarke Frost; Simonsen, Lone; Sneppen, Kim

doi:10.1103/physrevlett.126.118301

Cited by 93 publications

(97 citation statements)

References 31 publications

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“…Also, it was recently shown that a strategic social network-based reduction of contact strongly enhances the effectiveness of social distancing measures while keeping risks lower (Block 2020 ), and that overdispersed diseases such as COVID-19 are very sensitive to social network size and clustering (Nielsen et al. 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Kinetic models for epidemic dynamics with social heterogeneity

et al. 2021

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We introduce a mathematical description of the impact of the number of daily contacts in the spread of infectious diseases by integrating an epidemiological dynamics with a kinetic modeling of population-based contacts. The kinetic description leads to study the evolution over time of Boltzmann-type equations describing the number densities of social contacts of susceptible, infected and recovered individuals, whose proportions are driven by a classical SIR-type compartmental model in epidemiology. Explicit calculations show that the spread of the disease is closely related to moments of the contact distribution. Furthermore, the kinetic model allows to clarify how a selective control can be assumed to achieve a minimal lockdown strategy by only reducing individuals undergoing a very large number of daily contacts. We conduct numerical simulations which confirm the ability of the model to describe different phenomena characteristic of the rapid spread of an epidemic. Motivated by the COVID-19 pandemic, a last part is dedicated to fit numerical solutions of the proposed model with infection data coming from different European countries.

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Section: Introductionmentioning

confidence: 99%

Kinetic models for epidemic dynamics with social heterogeneity

et al. 2021

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“…[ in our model]. In contrast, in network models 21 the number of connections between individuals can be lowered for a similar effect.…”

Section: Resultsmentioning

confidence: 92%

“…The direct generalisation of the SIR model to a network model is one in which individuals interact pairwise. This is the typical approach and has the property that individuals can only infect one at a time, even in the case of modelling spread on heterogeneous network 16 . To account for one-to-many interactions one may consider agent-based models on bipartite networks (of e.g.…”

Section: Introductionmentioning

confidence: 99%

Superspreading of airborne pathogens in a heterogeneous world

Kirkegaard

Mathiesen

Sneppen

2021

Sci Rep

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Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20% infecting more than 80%, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.

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“…Our recent studies have shown that the presence of overdispersion makes a pandemic far more controllable than influenza pandemics when mitigating by limiting non-repetitive contacts (17) and personal contact network size (18). We therefore speculate that restrictions which alter social contact structure may, conversely, provide a fitness advantage to variants with more homogeneous transmission, and may thus play a role in viral evolution.…”

Section: Significancementioning

confidence: 87%

Lockdowns exert selection pressure on overdispersion of SARS-CoV-2 variants

Nielsen¹,

Eilersen²,

Simonsen

et al. 2021

Preprint

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The SARS-CoV-2 ancestral strain has caused pronounced superspreading events, reflecting a disease characterized by overdispersion, where about 10% of infected people causes 80% of infections. New variants of the disease have different person-to-person variations in viral load, suggesting for example that the Alpha (B.1.1.7) variant is more infectious but relatively less prone to superspreading. Meanwhile, mitigation of the pandemic has focused on limiting social contacts (lockdowns, regulations on gatherings) and decreasing transmission risk through mask wearing and social distancing. Using a mathematical model, we show that the competitive advantage of disease variants may heavily depend on the restrictions imposed. In particular, we find that lockdowns exert an evolutionary pressure which favours variants with lower levels of overdispersion. We find that overdispersion is an evolutionarily unstable trait, with a tendency for more homogeneously spreading variants to eventually dominate.

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COVID-19 Superspreading Suggests Mitigation by Social Network Modulation

Cited by 93 publications

References 31 publications

Kinetic models for epidemic dynamics with social heterogeneity

Kinetic models for epidemic dynamics with social heterogeneity

Superspreading of airborne pathogens in a heterogeneous world

Lockdowns exert selection pressure on overdispersion of SARS-CoV-2 variants

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