Abstract
Epidemiological data about SARS-CoV-2 spread indicate that the virus is not transmitted uniformly in the population. The transmission tends to be more effective in select settings that involve exposure to relatively high viral dose, such as in crowded indoor settings, assisted living facilities, prisons or food processing plants. To explore the effect on infection dynamics, we describe a new mathematical model where transmission can occur (i) in the community at large, characterized by low-dose exposure and mostly mild disease, and (ii) in so-called transmission hot zones, characterized by high-dose exposure that can be associated with more severe disease. The model yields different types of epidemiological dynamics, depending on the relative importance of hot zone and community transmission. Interesting dynamics occur if the rate of virus release/deposition from severely infected people is larger than that of mildly infected individuals. Under this assumption, we find that successful infection spread can hinge upon high-dose hot zone transmission, yet the majority of infections are predicted to occur in the community at large with mild disease. In this regime, residual hot zone transmission can account for continued virus spread during community lockdowns, and the suppression of hot zones after community interventions are relaxed can cause a prolonged lack of infection resurgence following the reopening of society. This gives rise to the notion that targeted interventions specifically reducing virus transmission in the hot zones have the potential to suppress overall infection spread, including in the community at large. Epidemiological trends in the USA and Europe are interpreted in light of this model.
Highlights
As the United States and other countries around the world have witnessed waves of SARS-CoV-2 spread and the associated morbidity and mortality, it is clear that a better understanding of SARS-CoV infection dynamics will benefit the efforts to reduce infection burden, through non-pharmaceutical interventions and vaccines
Mathematical models have been used to characterize the dynamics of SARS-CoV-2 and predict potential numbers of COVID-19 cases [1,2,3,4,5,6,7], which has resulted in the estimation of the basic reproduction number [1,8], a better understanding of expected transmission dynamics in the absence and presence of non-pharmaceutical interventions [9,10,11,12,13,14,15,16], and in the critical effect of age structure on disease dynamics [11,17], among many other contributions
We consider a mathematical model that distinguishes between patients with a mild or asymptomatic infection and those with severe SARS-CoV-2 infection, including symptomatic but ambulatory COVID-19 patients
Summary
As the United States and other countries around the world have witnessed waves of SARS-CoV-2 spread and the associated morbidity and mortality, it is clear that a better understanding of SARS-CoV infection dynamics will benefit the efforts to reduce infection burden, through non-pharmaceutical interventions and vaccines. Mathematical models have been used to characterize the dynamics of SARS-CoV-2 and predict potential numbers of COVID-19 cases [1,2,3,4,5,6,7], which has resulted in the estimation of the basic reproduction number [1,8], a better understanding of expected transmission dynamics in the absence and presence of non-pharmaceutical interventions [9,10,11,12,13,14,15,16], and in the critical effect of age structure on disease dynamics [11,17], among many other contributions. According to our mathematical model, these dynamics are a direct consequence of the assumed viral dose-dependency, which might warrant further attention from a clinical and epidemiological perspective. 2 With this theory in mind, we interpret epidemiological data that document SARS-CoV-2 dynamics in different states in the USA and in European countries
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