Abstract
BackgroundMany countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions have moved to relax measures, while others have intensified efforts to reduce transmission.AimWe aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases.MethodsWe examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. We then examined how long it takes to observe this difference, given delays and noise in reported cases. We used a susceptible-exposed-infectious-removed (SEIR)-type model and publicly available data from British Columbia, Canada, collected between March and July 2020.ResultsIt takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20–26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days.ConclusionThe time until a change in control measures has an observed impact is longer than the mean incubation period of COVID-19 and the commonly used 14-day time period. Policymakers and practitioners should consider this when assessing the impact of policy changes. Rapid, consistent and real-time COVID-19 surveillance is important to minimise these time frames.
Highlights
In response to the coronavirus disease (COVID-19) pandemic, many countries have implemented largescale non-pharmaceutical interventions (NPI), with a particular focus on physical distancing measures
Understanding the possible trajectories that may arise from changing physical distancing measures is crucial, as is consideration of the timescale of such trajectory changes to ensure that they result in the desired impact within the expected time frame
To compute the empirical probability that a substantial difference occurs on a given day after introducing distancing measures, we considered the proportion of simulated samples for which the model with physical distancing shows fewer active cases than the non-distancing model by at least 10
Summary
In response to the coronavirus disease (COVID-19) pandemic, many countries have implemented largescale non-pharmaceutical interventions (NPI), with a particular focus on physical distancing measures. Aim: We aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases. Methods: We examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. Results: It takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20–26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days. Consistent and real-time COVID-19 surveillance is important to minimise these time frames
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