tetano
Editor, Senior Moderator
- Jessica E Stockdale[SUP]1[/SUP]
, Renny Doig[SUP]2[/SUP] , Joosung Min[SUP]2[/SUP] , Nicola Mulberry[SUP]1[/SUP] , Liangliang Wang[SUP]2[/SUP] , Lloyd T Elliott[SUP]2[/SUP] , Caroline Colijn[SUP]1[/SUP]
Many 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.
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. 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.
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.
Conclusion
The 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.
full article
https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.40.2001204