About: Modeling latent infection transmissions through biosocial stochastic dynamics

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Modeling latent infection transmissions through biosocial stochastic dynamics
Creator	Melnik, Roderick Tadić, Bosiljka
source	MedRxiv
abstract	The events of the recent SARS-CoV-02 epidemics have shown the importance of social factors, especially given the large number of asymptomatic cases that effectively spread the virus, which can cause a medical emergency to very susceptible individuals. Besides, the SARS-CoV-02 virus survives for several hours on different surfaces, where a new host can contract it with a delay. These passive modes of infection transmission remain an unexplored area for traditional mean-field epidemic models. Here, we design an agent-based model for simulations of infection transmission in an open system driven by the dynamics of social activity; the model takes into account the personal characteristics of individuals, as well as the survival time of the virus and its potential mutations. A growing bipartite graph embodies this biosocial process, consisting of active carriers (host) nodes that produce viral nodes during their infectious period. With its directed edges passing through viral nodes between two successive hosts, this graph contains complete information about the routes leading to each infected individual. We determine temporal fluctuations of the number of exposed and the number of infected individuals, the number of active carriers and active viruses at hourly resolution. The simulated processes underpin the latent infection transmissions, contributing significantly to the spread of the virus within a large time window. More precisely, being brought by social dynamics and exposed to the currently existing infection, an individual passes through the infectious state until eventually spontaneously recovers or otherwise is moves to a controlled hospital environment. Our results reveal complex feedback mechanisms that shape the dependence of the infection curve on the intensity of social dynamics and other sociobiological factors. In particular, the results show how the lockdown effectively reduces the spread of infection and how it increases again after the lockdown is removed. Furthermore, a reduced level of social activity but prolonged exposure of susceptible individuals have adverse effects. On the other hand, virus mutations that can gradually reduce the transmission rate by hopping to each new host along the infection path can significantly reduce the extent of the infection, but can not stop the spreading without additional social strategies. Our stochastic processes, based on graphs at the interface of biology and social dynamics, provide a new mathematical framework for simulations of various epidemic control strategies with high temporal resolution and virus traceability.
has issue date	2020-08-01(xsd:dateTime)
bibo:doi	10.1101/2020.07.30.20164491
has license	medrxiv
sha1sum (hex)	3017633d14bf5d80f31694880949ccdfde42f0b9
schema:url	https://doi.org/10.1101/2020.07.30.20164491
resource representing a document's title	Modeling latent infection transmissions through biosocial stochastic dynamics
resource representing a document's body	covid:3017633d14bf5d80f31694880949ccdfde42f0b9#body_text
is schema:about of	named entity 'curve' named entity 'stochastic processes' named entity 'area' named entity 'removed' named entity 'lockdown' »more»

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