We have developed an agent-based modeling framework for simulations of bio-social stochastic processes underlying SARS-CoV-2 epidemics. The individual features of the agents that affect the process at elementary interaction scale incorporate their susceptibility to the virus, which helps us to differentiate between the symptomatic and asymptomatic cases, and the exposure time of each actor, as well as the virus survival time and potential mutations. The process is visualized by a growing bipartite graph of the infected host and viruses that they produce, see Fig.1. A large number of asymptomatic individuals, as well as the secondary transmission due to the virus survival outside the human host, comprise the latent infection transmission, contributing to the widespread epidemics. We demonstrate [1] how the infection curves depend on the social participation activity, which is the driving force of the dynamics, and the exposure time of each participant. Our simulations reveal the mechanisms through which the social lock-down becomes effective with a delay and how the second wave raises when it is lifted. Moreover, by tracing the virus transmission paths on the graph, we can study a potential mutation of the virus. For example, assuming that its transmissibility reduces with the number of different hosts along the infection path, we show that the infection curves slow down as compared to the no-mutation case. The advantage of this mathematical framework is that it reveals the genesis of the collective epidemic phenomenon across the scales starting from individual actors. It allows for implementing different intervention scenarios that can affect the social participation level as well as individual behavior of each actor. [1] B. Tadic, R. Melnik, medRxiv: doi: https://doi.org/10.1101/2020.07.30.20164491; PLoS one 15:e0241163 (2020)
