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Modeling latent infection transmissions through biosocial stochastic dynamics

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  • Bosiljka Tadić
  • Roderick Melnik

Abstract

The events of the recent SARS-CoV-2 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-2 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.

Suggested Citation

  • Bosiljka Tadić & Roderick Melnik, 2020. "Modeling latent infection transmissions through biosocial stochastic dynamics," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-16, October.
  • Handle: RePEc:plo:pone00:0241163
    DOI: 10.1371/journal.pone.0241163
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    References listed on IDEAS

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    1. Roman Wölfel & Victor M. Corman & Wolfgang Guggemos & Michael Seilmaier & Sabine Zange & Marcel A. Müller & Daniela Niemeyer & Terry C. Jones & Patrick Vollmar & Camilla Rothe & Michael Hoelscher & To, 2020. "Virological assessment of hospitalized patients with COVID-2019," Nature, Nature, vol. 581(7809), pages 465-469, May.
    2. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    3. Dhar, Deepak, 2006. "Theoretical studies of self-organized criticality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(1), pages 29-70.
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    Cited by:

    1. Đorđević, J. & Papić, I. & Šuvak, N., 2021. "A two diffusion stochastic model for the spread of the new corona virus SARS-CoV-2," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    2. Sebastian A Müller & Michael Balmer & William Charlton & Ricardo Ewert & Andreas Neumann & Christian Rakow & Tilmann Schlenther & Kai Nagel, 2021. "Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-32, October.
    3. Weiwei Zhang & Shiyong Liu & Nathaniel Osgood & Hongli Zhu & Ying Qian & Peng Jia, 2023. "Using simulation modelling and systems science to help contain COVID‐19: A systematic review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(1), pages 207-234, January.
    4. Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    5. Nagel, Kai & Rakow, Christian & Müller, Sebastian A., 2021. "Realistic agent-based simulation of infection dynamics and percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).

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