Social heterogeneity drives complex patterns of the COVID-19 pandemic: insights from a novel Stochastic Heterogeneous Epidemic Model (SHEM)
Authors: Maltsev Alexander V. , Stern Michael D. 
Published: July 2020

AbstractIn today’s absence of a vaccine and impactful treatments, the most effective way to combat the virus is to find and implement mitigation strategies. An invaluable resource in this task is numerical modeling that can reveal key factors in COVID-19 pandemic development. On the other hand, it has become evident that regional infection curves of COVID-19 exhibit complex patterns which often differ from curves predicted by forecasting models. The wide variations in attack rate observed among different social strata suggest that this may be due to social heterogeneity not accounted for by regional models. We investigated this hypothesis by developing and using a new Stochastic Heterogeneous Epidemic Model (SHEM) that focuses on vulnerable subpopulations. We found that the isolation or embedding of vulnerable sub-clusters in a major population hub generated complex stochastic infection patterns which included multiple peaks and growth periods, an extended plateau, a prolonged tail, or a delayed second wave of infection. Embedded vulnerable groups became hotspots that drove infection despite efforts of the main population to socially distance, while isolated groups suffered delayed but intense infection. Amplification of infection by these hotspots facilitated transmission from one urban area to another, causing the epidemic to hopscotch in a stochastic manner to places it would not otherwise reach, resembling a microcosm of the situation worldwide as of September 2020. Our results suggest that social heterogeneity is a key factor in the formation of complex infection propagation patterns. Thus, the mitigation of vulnerable groups is essential to control the COVID-19 pandemic worldwide. The design of our new model allows it to be applied in future studies of real-world scenarios on any scale, limited only by memory and the ability to determine the underlying topology and parameters.Author SummaryCOVID-19 is a disease caused by the novel coronavirus SARS-CoV-2 that is both fatal and has a high transmission rate(R0), almost twice that of the 2017-2018 common influenza, presenting itself as a massive challenge to the world today. Existing mitigation strategies often are not efficient, and the mechanisms underlying complex infection patterns that distinguish themselves from simple curves remain unclear. Numerical modeling can identify pandemic mechanisms and inform policymakers how to improve mitigation strategies. One underexplored mechanism is social heterogeneity, specifically the contribution of vulnerable social subgroups, not accounted for by regional models. To investigate this, we developed a novel numerical model (dubbed SHEM) that examines the evolution of infection spread in a collection of diverse populations, connected by a network of links along which infection travels. We found that vulnerable subgroups that cannot implement mitigation strategies create infection hotspots which drive infection within and among urban areas, defeating mitigations. Furthermore, isolated vulnerable populations (which may hold a false sense of security in the real world) can create additional delayed infection spikes. This means effective mitigation of the COVID-19 pandemic requires close attention to vulnerable subgroups.