Author: Michael E. Hochberg
Title: Importance of suppression and mitigation measures in managing COVID-19 outbreaks Document date: 2020_4_2
ID: 4fkb1udl_31
Snippet: The copyright holder for this preprint . https://doi.org/10.1101/2020.03.31.20048835 doi: medRxiv preprint Although not explored here, numerical simulations for parameter values associated with points above the dashed line in (A) were influenced by 'herd immunity', i.e., when the proportion of the population in the susceptible class, S/N<(1.0-1.0/â„›) (19) . Moreover, note that the result in (A) appears to contrast with the results in Steir et al.....
Document: The copyright holder for this preprint . https://doi.org/10.1101/2020.03.31.20048835 doi: medRxiv preprint Although not explored here, numerical simulations for parameter values associated with points above the dashed line in (A) were influenced by 'herd immunity', i.e., when the proportion of the population in the susceptible class, S/N<(1.0-1.0/â„›) (19) . Moreover, note that the result in (A) appears to contrast with the results in Steir et al. (5) , who showed higher case growth rates with city size. The discrepancy can be explained by the different units employed in each study (case growth rate in (5) vs. fraction of total population infected at some point during a fixed time interval (this study)) and how â„› was estimated in (5) (found to be city-size dependent) vs. assumed invariant in the present study. Case growth rate (number of new cases on day tnumber of new cases on day t-1 / number of new cases on day t-1) was found to increase with community size in the present study (not shown). Numbers above points in (B) refer to the day that maximum hospitalization occurs, and are only shown for the Black line conditions (note that when â„›C=1.0, maximum levels begin on day 90 and are constant thereafter; for â„›C=1.1, maximum levels occur after 200 days). See main text for additional details.
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