1887
Research Open Access
Like 0

Abstract

Background

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.

Loading

Article metrics loading...

/content/10.2807/1560-7917.ES.2021.26.40.2001204
2021-10-07
2021-10-28
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2021.26.40.2001204
Loading
Loading full text...

Full text loading...

/deliver/fulltext/eurosurveillance/26/40/eurosurv-26-40-3.html?itemId=/content/10.2807/1560-7917.ES.2021.26.40.2001204&mimeType=html&fmt=ahah

References

  1. Anderson SC, Edwards AM, Yerlanov M, Mulberry N, Stockdale JE, Iyaniwura SA, et al. Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing. PLOS Comput Biol. 2020;16(12):e1008274.  https://doi.org/10.1371/journal.pcbi.1008274  PMID: 33270633 
  2. Courtemanche CJ, Garuccio J, Le A, Pinkston JC, Yelowitz A. Did social-distancing measures in Kentucky help to flatten the COVID-19 curve? Working Paper 29. Institute for the Study of Free Enterprise. Louiseville: University of Kentucky; 2020. Available from: https://uknowledge.uky.edu/isfe_papers/1
  3. Varghese C, Xu W. Quantifying what could have been - The impact of the Australian and New Zealand governments’ response to COVID-19. Infect Dis Health. 2020;25(4):242-4.  https://doi.org/10.1016/j.idh.2020.05.003  PMID: 32507662 
  4. Wu J, Tang B, Bragazzi NL, Nah K, McCarthy Z. Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada. J Math Ind. 2020;10(1):15.  https://doi.org/10.1186/s13362-020-00083-3  PMID: 32501416 
  5. Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N, et al. , Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health. 2020;5(5):e261-70.  https://doi.org/10.1016/S2468-2667(20)30073-6  PMID: 32220655 
  6. Tuite AR, Fisman DN, Greer AL. Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. CMAJ. 2020;192(19):E497-505.  https://doi.org/10.1503/cmaj.200476  PMID: 32269018 
  7. Kissler SM, Tedijanto C, Lipsitch M, Grad Y. Social distancing strategies for curbing the COVID-19 epidemic. medRxiv. 2020:03.22.20041079.  https://doi.org/10.1101/2020.03.22.20041079 
  8. Di Domenico L, Pullano G, Sabbatini CE, Boëlle PY, Colizza V. Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies. BMC Med. 2020;18(1):240-240.  https://doi.org/10.1186/s12916-020-01698-4  PMID: 32727547 
  9. Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, et al. , Imperial College COVID-19 Response Team. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584(7820):257-61.  https://doi.org/10.1038/s41586-020-2405-7  PMID: 32512579 
  10. Chen D, Zhou T. Evaluating the effect of Chinese control measures on COVID-19 via temporal reproduction number estimation. PLoS One. 2021;16(2):e0246715.  https://doi.org/10.1371/journal.pone.0246715  PMID: 33571273 
  11. Anderson SC, Mulberry N, Edwards AM, Stockdale JE, Iyaniwura SA, Falcao RC, et al. How much leeway is there to relax COVID-19 control measures? medRxiv. 2020:2020.06.12.20129833.  https://doi.org/10.1101/2020.06.12.20129833 
  12. The Government of British Columbia. Premier outlines plan to restart B.C. safely. Vancouver: The Government of British Columbia; 6 May 2020. Available from: https://news.gov.bc.ca/releases/2020PREM0026-000826
  13. Stockdale JE. LongTimeFrames GitHub repository. GitHub. [Accessed: 1 Mar 2021]. Available from: https://github.com/jessicastockdale/LongTimeFrames
  14. Siordia JA Jr. Epidemiology and clinical features of COVID-19: A review of current literature. J Clin Virol. 2020;127:104357.  https://doi.org/10.1016/j.jcv.2020.104357  PMID: 32305884 
  15. British Columbia Centre for Disease Control. British Columbia COVID-19 daily situation report, April 24th, 2020. Vancouver: British Columbia Centre for Disease Control; 24 Apr 2020. Available from: http://www.bccdc.ca/Health-Info-Site/Documents/BC_Surveillance_Summary_April_24_Final.pdf
  16. Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol. 2013;178(9):1505-12.  https://doi.org/10.1093/aje/kwt133  PMID: 24043437 
  17. Covid Act Now. U.S. COVID Risk & Vaccine Tracker. [Accessed: 14 Sep 2021]. Available from: https://covidactnow.org
  18. He W, Yi GY, Zhu Y. Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis. J Med Virol. 2020;92(11):2543-50.  https://doi.org/10.1002/jmv.26041  PMID: 32470164 
  19. Carlisle M. White house coronavirus official says effects of social distancing won’t be seen for 7 to 14 days. New York. Time. 2020;24. Available from: https://time.com/5808777/coronavirus-white-house-debra-birx-today-show/
  20. CTV News Montreal. COVID-19: Effect of social distancing measures will take time to appear, experts say. Ontario: CTV News. 18 Mar 2020. Available from: https://montreal.ctvnews.ca/covid-19-effect-of-social-distancing- measures-will-take-time-to-appear-experts-say-1.4858418
  21. Reuters. Two weeks in, Britain’s COVID-19 lockdown having an effect, study shows. London: Reuters; 8 Apr 2020. Available from: https://www.reuters.com/article/us- health-coronavirus-britain-tracker/two-weeks-in-britains-covid-19- lockdown-having-an-effect-study-shows-idUSKBN21Q1XH
  22. Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect Dis. 2020;20(8):911-9.  https://doi.org/10.1016/S1473-3099(20)30287-5  PMID: 32353347 
  23. Peccia J, Zulli A, Brackney DE, Grubaugh ND, Kaplan EH, Casanovas-Massana A, et al. SARS-CoV-2 RNA concentrations in primary municipal sewage sludge as a leading indicator of COVID-19 outbreak dynamics. medRxiv. 2020:2020.05.19.20105999.  https://doi.org/10.1101/2020.05.19.20105999 
  24. Ahmed W, Angel N, Edson J, Bibby K, Bivins A, O’Brien JW, et al. First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community. Sci Total Environ. 2020;728:138764.  https://doi.org/10.1016/j.scitotenv.2020.138764  PMID: 32387778 
  25. Google. COVID-19 community mobility reports. California: Google. [Accessed: 12 Jun 2020]. Available from: https://www.google.com/covid19/mobility
  26. Gao S, Rao J, Kang Y, Liang Y, Kruse J. Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special. arxiv. 2020;12(1). https://arxiv.org/abs/2004.04544
/content/10.2807/1560-7917.ES.2021.26.40.2001204
Loading

Data & Media loading...

Supplementary data

Submit comment
Close
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error