Modeling latent infection transmissions through biosocial stochastic dynamics

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.

Microscopic dynamics modeling unravels the role of asymptomatic virus carriers in SARS-CoV-2 epidemics at the interplay between biological and social factors

“…It arises from individual actors’ behaviour that can get infected and spread the infectious agents via multiplexity of contacts. In this regards, the human participation activity conditioned by daily mobility patterns has been recognised as a primary driving force for the epidemics spreading 1–3 . Contrary to many social processes, the epidemic spreading has a vital biological component, which is prominent at the elementary interactions scale 4,5 .…”

“…Hence, a better understanding of the factors that determine the proportion of the asymptomatic virus carries in a given social environment, and their impact on the overall infection growth is vital for managing the disease outbreak. In this work, we tackle these problems using the microscopic dynamics approach within the agent-based modelling framework developed in 1 .…”

“…Complementary to the standard mean-field models with continuous-time dynamics of interdependent equations for groups 31,32 , the microscopic agent-based modeling of SARS-CoV-2 epidemics gains an increasing attention 1,33–37 . This modelling approach provides us with intrinsic microscopic mechanisms of the epidemic processes, and the information on how it develops from the elementary interactions to the global-scale outcome.…”

“…This modelling approach provides us with intrinsic microscopic mechanisms of the epidemic processes, and the information on how it develops from the elementary interactions to the global-scale outcome. Moreover, it allows for considering individual features of the actors, their mobility and location of the interactions and participation in coupled stochastic processes 1,33,34,36 ? , 37 .…”

“…, 37 . We have recently developed an agent-based model 1 , where the human actors possess individual susceptibility to the virus. It thus allows us to differentiate between highly susceptible individuals, who can develop symptoms from those who are less susceptible and may become asymptomatically infected.…”