Superspreaders provide essential clues for mitigation of COVID-19

Nielsen, Bjarke Frost; Sneppen, Kim

doi:10.1101/2020.09.15.20195008

Cited by 8 publications

(8 citation statements)

References 29 publications

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“…However, even though these interventions may not decrease k , they may decrease superspreading – because R 0 and k are dependent on each other, descriptors of the offspring distribution, a reduction in R 0 even without a reduction in k corresponds to smaller superspreading events. Indeed, superspreading pathogens are sensitive to mitigation strategies that preferentially prevent superspreading events, with reductions in behavioral variability providing superior reduction in R 0 [41, 42, 27]. Research on the impact of superspreading diseases to public health interventions is still an emerging field, with substantial uncertainty remaining in how to best implement interventions.…”

Section: Discussionmentioning

confidence: 99%

Characterizing superspreading of SARS-CoV-2 : from mechanism to measurement

Susswein

Bansal

2020

Preprint

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Superspreading is a ubiquitous feature of SARS-CoV-2 transmission dynamics, with a few primary infectors leading to a large proportion of secondary infections. Despite the superspreading events observed in previous coronavirus outbreaks, the mechanisms behind the phenomenon are still poorly understood. Here, we show that superspreading is largely driven by heterogeneity in contact behavior rather than heterogeneity in susceptibility or infectivity caused by biological factors. We find that highly heterogeneous contact behavior is required to produce the extreme superspreading estimated from recent COVID-19 outbreaks. However, we show that superspreading estimates are noisy and subject to biases in data collection and public health capacity, potentially leading to an overestimation of superspreading. These results suggest that superspreading for COVID-19 is substantial, but less than previously estimated. Our findings highlight the complexity inherent to quantitative measurement of epidemic dynamics and the necessity of robust theory to guide public health intervention.

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Section: Discussionmentioning

confidence: 99%

Characterizing superspreading of SARS-CoV-2 : from mechanism to measurement

Susswein

Bansal

2020

Preprint

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“…[ α in our model]. In contrast, in network models [17] the number of connections between individuals can be lowered for a similar effect.…”

Section: Resultsmentioning

confidence: 95%

Airborne Pathogens in a Heterogeneous World: Superspreading & Mitigation

Kirkegaard

Mathiesen

Sneppen

2020

Preprint

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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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“…Contact networks are being used to build realistic simulations of a society, for instance by creating household-structures with various types of inter-household connections [70–73]. The flexibility to control networks modeling NPIs easy [71, 72, 74, 75]. Moreover, [76–78] use a network-based approach for spatial properties (e.g., flow between geographical regions).…”

Section: Related Workmentioning

confidence: 99%

Why ODE models for COVID-19 fail: Heterogeneity shapes epidemic dynamics

Großmann

Backenkoehler

Wolf

2021

Preprint

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In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.

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Superspreaders provide essential clues for mitigation of COVID-19

Cited by 8 publications

References 29 publications

Characterizing superspreading of SARS-CoV-2 : from mechanism to measurement

Characterizing superspreading of SARS-CoV-2 : from mechanism to measurement

Airborne Pathogens in a Heterogeneous World: Superspreading & Mitigation

Why ODE models for COVID-19 fail: Heterogeneity shapes epidemic dynamics

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