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.