Long time frames to detect the impact of changing COVID-19 measures, Canada, March to July 2020
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Abstract
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.
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SciScore for 10.1101/2020.06.14.20131177: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our analysis has a number of limitations. Although policy changes happen at defined times, distancing behaviour and other COVID-19 control practices do not change instantaneously, and the time frame to detect changes may therefore be longer than we have estimated. We have not explored a wide range of growth rates or baseline prevalence levels and these may affect the results. The transmission model used is a deterministic SEIR variant, …
SciScore for 10.1101/2020.06.14.20131177: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our analysis has a number of limitations. Although policy changes happen at defined times, distancing behaviour and other COVID-19 control practices do not change instantaneously, and the time frame to detect changes may therefore be longer than we have estimated. We have not explored a wide range of growth rates or baseline prevalence levels and these may affect the results. The transmission model used is a deterministic SEIR variant, and does not include stochastic effects (except in the observation model), or age or risk structure. The model includes a fraction of the population practising physical distancing, thereby reducing their contact rates, but does not otherwise include heterogeneity in contact patterns. In particular, we focus on the case onset-to-reporting delay, but certain high-risk groups such as healthcare workers may be more likely to get tested and have expedited testing available. Our methodology could readily be extended to structured models, but this requires age-stratified counts and knowledge of the mixing terms. Indeed, any disease model will include exponential growth and decay, and this work is somewhat model-agnostic in that, whatever level of detail goes into producing this exponential behaviour, we can still perform the same eventual inference. Our approach to determining when the effect of modifying measures is observable relies on using case count data as the indicator for increased community-based transmission. However, public health officials...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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