Utility of COVID-19 Decision Rules Related to Consecutive Decline in Positivity or Hospitalizations: A Data-driven Simulation Study

The White House issued Guidelines for Opening Up America Again to help state and local officials when reopening their economies. These included a “downward trajectory of positive tests as a percent of total tests within a 14-day period.” To examine this rule, we computed the probability of observing continuous decline in positivity when true positivity is in decline using data-driven simulation. Data for COVID-19 positivity reported in New York state from April 14 to May 5, 2020, where a clear reduction was observed, were used. First, a logistic regression model was fitted to the data, considering the fitted values as true positivity. Second, we created observed positivity by randomly selecting 25,000 people per day from a population with those true positivity for 14 days. The simulation was repeated 1,000 times to compute the probability of observing a consecutive decline. As sensitivity analyses, we performed the simulation with different daily numbers of tests (10 to 30,000) and length of observation (7 and 21 days). We further used daily hospitalizations as another metric, using data from the state of Indiana. With 25,000 daily tests, the probability of a consecutive decline in positivity for 14 days was 99.9% (95% CI: 99.7% to 100%). The probability dropped with smaller numbers of tests and longer lengths of consecutive observation, because there is more chance of observing an increase in positivity with smaller numbers of tests and longer observation. The probability of consecutive decline in hospitalizations was ∼0.0% regardless of the length of consecutive observation due to large variance. These results suggest that continuous declines in sample COVID-19 test positivity and hospitalizations may not be observed with sufficient probability, even when population probabilities truly decline. Criteria based on consecutive declines in metrics are unlikely to be useful for making decisions about relaxing COVID-19 mitigation efforts. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study is not supported by any specific funding sources. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approval by the IRB was exempted because we used only secondary published data. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data used in this study are publicly available from following data sources: 1. New York State Department of Health. NYSDOH COVID-19 Tracker [Available from: . 2. Regenstrief Institute. Regenstrief COVID-19 Dashboard [Available from: .