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?:abstract
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Transmission of SARS-CoV-2 appears especially effective in \'hot zone\' locations where individuals interact in close proximity. We present mathematical models describing two types of hot zones. First, we consider a metapopulation model of infection spread where transmission hot zones are explicitly described by independent demes in which the same people repeatedly interact (referred to as \'static\' hot zones, e.g. nursing homes, food processing plants, prisons, etc.). These are assumed to exists in addition to a \'community at large\' compartment in which virus transmission is less effective. This model yields a number of predictions that are relevant to interpreting epidemiological patterns in COVID19 data. Even if the rate of community virus spread is assumed to be relatively slow, outbreaks in hot zones can temporarily accelerate initial community virus growth, which can lead to an overestimation of the viral reproduction number in the general population. Further, the model suggests that hot zones are a reservoir enabling the prolonged persistence of the virus at \'infection plateaus\' following implementation of non-pharmaceutical interventions, which has been frequently observed in data. The second model considers \'dynamic\' hot zones, which can repeatedly form by drawing random individuals from the community, and subsequently dissolve (e.g. restaurants, bars, movie theaters). While dynamic hot zones can accelerate the average rate of community virus spread and can provide opportunities for targeted interventions, they do not predict the occurrence of infection plateaus or other atypical epidemiological dynamics. The models therefore identify two types of transmission hot zones with very different effects on the infection dynamics, which warrants further epidemiological investigations.
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