Modeling layered non-pharmaceutical interventions against SARS-CoV-2 in the United States with Corvid

Dennis L Chao; Assaf P Oron; Devabhaktuni Srikrishna; Michael Famulare

To demonstrate the features of the model, we simulated the effects of using NPIs to reduce SARS-CoV-2 transmission. We first test the effectiveness of workplace-based policies then add additional "layers" of interventions that are enacted at the same time [2] . We present the impact of these interventions in figures that plot the simulated number of new symptomatic cases each day and the cumulative percentage of the population that has become symptomatic, and compare simulations with no interventions to simulations where these policies are started on either day 30 (early) or day 90 (later) of the epidemic. These simulations are meant to demonstrate features of the model and the qualitative effects of NPIs-the intervention parameters were not set to reflect any specific real-world setting. no intervention ll/wfh ll/wfh+school ll/wfh+school+shelter ll/wfh+school+shelter+home iso ll/wfh+school+shelter+self iso ll/wfh+school+shelter+home iso+quar Figure 1 : Simulating layered NPIs. NPIs were started on day 30 (top panels) or 90 (bottom panels) of the epidemic. The left plots show log-transformed numbers of newly symptomatic people per day per 100,000 population, and the right plots show cumulative % of the population that has been symptomatic. Each curve represents one stochastic simulation. The days when workplace policies are in effect is shown as a blue horizontal dashed line near the x-axis and school closures as a red line.