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

Abstract

The recent experience of SARS-CoV-2 epidemics spreading revealed the importance of passive forms of infection transmissions. Apart from the virus survival outside the host, the latent infection transmissions caused by asymptomatic and presymptomatic hosts represent major challenges for controlling the epidemics. In this regard, social mixing and various biological factors play their subtle, but often critical, role. For example, a life-threatening condition may result in the infection contracted from an asymptomatic virus carrier. Here, we use a new recently developed microscopic agent-based modeling framework to shed light on the role of asymptomatic hosts and to unravel the interplay between the biological and social factors of these nonlinear stochastic processes. The model accounts for each human actor's susceptibility and the virus survival time, as well as traceability along the infection path. These properties enable an efficient dissection of the infection events caused by asymptomatic carriers from those which involve symptomatic hosts before they develop symptoms and become removed to a controlled environment. Consequently, we assess how their relative proportions in the overall infection curve vary with changing model parameters. Our results reveal that these proportions largely depend on biological factors in the process, specifically, the virus transmissibility and the critical threshold for developing symptoms, which can be affected by the virus pathogenicity. Meanwhile, social participation activity is crucial for the overall infection level, further modulated by the virus transmissibility.

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

“…Since the λ + value is directly related to the basic reproduction number R 0 , which characterizes the inherent biological transmission of the virus in an (initially) completely unprotected population (λ + and R 0 are roughly proportional, in realistic parameter ranges) [57], this conclusion has a simple and yet important interpretation. Namely, it means that medical, demographic, and environmental predispositions (determining R 0 ) [70,71] should be expected to strongly influence the behavior of the considered observables (detected cases and fatalities attributed to the virus) [72][73][74]. On the other hand, R 0 seems not to significantly impact the infection extinguishing time.…”

Worldwide clustering and infection cycles as universal features of multiscale stochastic processes in the SARS-CoV-2 pandemic

“…In this respect, a larger-scale picture may emerge by studying temporal fluctuations of the world infection dynamics. More subtle questions regard the indicators for hidden mechanisms arising from the interplay of the above-mentioned biological factors and different social behaviours 5,21,23,47,48 behind the observed epidemic development.…”

“…For this purpose, the empirical data analysis and theoretical modelling 22 provide complemen-tary views of these complex processes. For example, agent-based models capture the interplay of the bio-social factors at the elementary scale of the virus-host interactions at high temporal resolution 5,[23][24][25][26][27][28][29][30][31] . On the other hand, more traditional compartmental models 32 consider a coarse-grained picture of the population groups having different roles in the process.…”

Worldwide clustering and infection cycles as universal features of multiscale stochastic processes in the SARS-CoV-2 pandemic

“…For this purpose, the empirical data analysis and theoretical modelling 22 provide complementary views of these complex processes. For example, agent-based models capture the interplay of the bio-social factors at the elementary scale of the virus-host interactions at high temporal resolution 5,[23][24][25][26][27][28][29][30][31] . On the other hand, more traditional compartmental models 32 consider a coarse-grained picture of the population groups having different roles in the process.…”

“…In this respect, a larger-scale picture may emerge by studying temporal fluctuations of the world infection dynamics. More subtle questions regard the indicators for hidden mechanisms arising from the interplay of the above-mentioned biological factors and different social behaviours 5,21,23,47,48 behind the observed epidemic development.…”