Modelling based on mobile data finds that a small minority of super spreader venues account for a large majority of infections. It also shows that restricting maximum occupancy at these places would be more effective than uniformly reducing mobility.
The research, which was published this week, used anonymous mobile data covering the hourly movements of 98 million people across 10 US cities for two months beginning in March 2020.
The model suggests that if Chicago had reopened restaurants in May 2020 there would have been close to 600,000 additional infections that month. Opening gyms would have led to 149,000 extra infections. If all closed venues in Chicago had been open during May, the model predicts that there would have been 3.3 million extra cases of Covid-19 in the city.
If these venues were open with occupancy capped at 30%, the model predicts a lower 1.1 million additional infections would have occurred. If occupancy was capped at 20% new infections would have been reduced by more than 80% to about 650,000 cases.
The model also predicts higher infection rates among disadvantaged groups based on differences in mobility. Disadvantaged groups have not been able to reduce their mobility as sharply, and the places they visit are more crowded, found the study.
The work corroborates and fine tunes insights gained from anecdotal examples of spread events in restaurants, gyms, religious venues, nursing homes and other crowded indoor venues.
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Research paper (in English)
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