Fig. 2: Assessing mobility reduction and reopening. | Nature

Fig. 2: Assessing mobility reduction and reopening.

From: Mobility network models of COVID-19 explain inequities and inform reopening

Fig. 2

The Chicago metro area is used as an example; results for all metro areas are included in Extended Data Figs. 3, 4, Supplementary Figs. 10, 1524 and Supplementary Tables 4, 5, as indicated. a, Counterfactual simulations (left) of past reductions in mobility illustrate that the magnitude of the reduction (middle) was at least as important as its timing (right) (Supplementary Tables 4, 5). b, The model predicts that most infections at POIs occur at a small fraction of superspreader POIs (Supplementary Fig. 10). c, Left, the cumulative number of predicted infections after one month of reopening is plotted against the fraction of visits lost by partial instead of full reopening (Extended Data Fig. 3); the annotations within the plot show the fraction of maximum occupancy that is used as the cap and the horizontal red line indicates the cumulative number of predicted infections at the point of reopening (on 1 May 2020). Compared to full reopening, capping at 20% of the maximum occupancy in Chicago reduces the number of new infections by more than 80%, while only losing 42% of overall visits. Right, compared to uniformly reducing visits, the reduced maximum occupancy strategy always results in a smaller predicted increase in infections for the same number of visits (Extended Data Fig. 4). The horizontal grey line at 0% indicates when the two strategies result in an equal number of infections, and we observe that the curve falls well below this baseline. The y axis plots the relative difference between the predicted number of new infections under the reduced occupancy strategy compared to a uniform reduction. d, Reopening full-service restaurants has the largest predicted impact on infections, due to the large number of restaurants as well as their high visit densities and long dwell times (Supplementary Figs. 1524). Colours are used to distinguish the different POI categories, but do not have any additional meaning. All results in this figure are aggregated across 4 parameter sets and 30 stochastic realizations (n = 120). Shaded regions in ac denote the 2.5th to 97.5th percentiles; boxes in d denote the interquartile range and data points outside this range are shown as individual dots.

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