Covid-19 superspreading. lessons from simulations on an empirical contact network
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Hilfiker, Lorenz
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2021
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06 - Presentation
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Madrid
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Abstract
Infectious individuals who cause an extraordinarily large number of secondary infections are colloquially referred to as superspreaders. Their pivotal role for the transmission of Covid-19 has been exemplified by now infamous cases such as the Washington choir practice, where one infectious individual caused 52 secondary infections [1]. In order to formally analyse superspreading, we denote by Z the individual reproduction number. In a fully susceptible population, the mean mZ is known as the basic reproduction number R0. Based on branching arguments and assuming a well-mixed population, the distribution of Z is typically modelled by a negative binomial distribution whose variance mZ(1+mZ=kZ) is characterised by the dispersion parameter kZ [2]. Empirical evidence suggests that Covid-19 exhibits a particularly wide distribution of Z, with the right tail representing superspreading events. In situations without interventions, the dispersion parameter kZ was estimated in the range 0.04 - 0.2 [3, 4]. Some studies even found evidence for a fat tailed Z-distribution, possibly a power law with the exponent close to 1 [5, 6]. The underlying mechanisms for the emergence of this level of heterogeneity are difficult to establish. A priori, network effects could play a role, as suggested in [5]. A more frequent line of reasoning focuses on physiological or biological factors: wet pronunciation, loud speech, frequent coughing or higher viral loads could result in some infected individuals being inherently more prone to spread the disease than others during an encounter with a susceptible individual [7]. Combining both lines of thought, the study in [8] shows that individual variation in infectiousness indeed leads to higher variance of Z on some standard static network models. However, no previous study has investigated heterogeneities of the Z-distribution on empirical contact networks. Therefore, we provide preliminary simulation results based on one realistic temporal social contact network and gather further evidence that the key to finding Z-distributions in alignment with empirical data is to allow for individual variation in infectiousness.
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330 - Wirtschaft
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10th International Conference on Complex Networks and their Applications
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30.11.2021
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02.12.2021
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English
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HILFIKER, Lorenz und Martin STERCHI, 2021. Covid-19 superspreading. lessons from simulations on an empirical contact network. 10th International Conference on Complex Networks and their Applications. Madrid. 2021. Verfügbar unter: https://irf.fhnw.ch/handle/11654/43156