The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada

Meili Li; Ruijun Zhai; Junling Ma; Meili Li; Ruijun Zhai; Junling Ma

doi:10.3934/mbe.2023616

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 13849-13863. doi: 10.3934/mbe.2023616

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The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada

1.
School of Science, Donghua University, Shanghai 201620, China
2.
Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 2Y2, Canada

Received: 01 April 2023 Revised: 16 May 2023 Accepted: 01 June 2023 Published: 19 June 2023

We propose a new method to estimate the change of the effective reproduction number with time, due to either disease control measures or seasonally varying transmission rate. We validate our method using a simulated epidemic curve and show that our method can effectively estimate both sudden changes and gradual changes in the reproduction number. We apply our method to the COVID-19 case counts in British Columbia, Canada in 2020, and we show that strengthening control measures had a significant effect on the reproduction number, while relaxations in May (business reopening) and September (school reopening) had significantly increased the reproduction number from around 1 to around 1.7 at its peak value. Our method can be applied to other infectious diseases, such as pandemics and seasonal influenza.
- reproduction number,
- transmission rate,
- control measure,
- infectious period
Citation: Meili Li, Ruijun Zhai, Junling Ma. The effects of disease control measures on the reproduction number of COVID-19 in British Columbia, Canada[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 13849-13863. doi: 10.3934/mbe.2023616

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Abstract

We propose a new method to estimate the change of the effective reproduction number with time, due to either disease control measures or seasonally varying transmission rate. We validate our method using a simulated epidemic curve and show that our method can effectively estimate both sudden changes and gradual changes in the reproduction number. We apply our method to the COVID-19 case counts in British Columbia, Canada in 2020, and we show that strengthening control measures had a significant effect on the reproduction number, while relaxations in May (business reopening) and September (school reopening) had significantly increased the reproduction number from around 1 to around 1.7 at its peak value. Our method can be applied to other infectious diseases, such as pandemics and seasonal influenza.

References

[1]	T. Asselah, D. Durantel, E. Pasmant, G. Lau, R. F. Schinazi, COVID-19: Discovery, diagnostics and drug development, J. Hepatol., 74 (2021), 168–184. https://doi.org/10.1016/j.jhep.2020.09.031 doi: 10.1016/j.jhep.2020.09.031
[2]	D. Chen, T. Zhou, Control Efficacy on COVID-19, arXiv Preprint arXiv., (2020), 2003.00305. https://doi.org/10.1371/journal.pone.0246715
[3]	R. Karia, I. Gupta, H. Khandait, A. Yadav, COVID-19 and its modes of transmission, SN Compr. Clin. Med., 2 (2020), 1798–1801. https://doi.org/10.1007/s42399-020-00498-4 doi: 10.1007/s42399-020-00498-4
[4]	A. Das, An approximation-based approach for periodic estimation of effective reproduction number: A tool for decision-making in the context of coronavirus disease 2019 (COVID-19) outbreak, Public Health, 185 (2020), 199–201. https://doi.org/10.1016/j.puhe.2020.06.047 doi: 10.1016/j.puhe.2020.06.047
[5]	C. Signorelli, T. Scognamiglio, A. Odone, COVID-19 in Italy: Impact of containment measures and prevalence estimates of infection in the general population, Acta Biomed., 91 (2020), 175–179. https://doi.org/10.23750%2Fabm.v91i3-S.9511
[6]	T. Lee, H. D. Kwon, J. Lee, The effect of control measures on COVID-19 transmission in South Korea, Plos One, 16 (2021), e0249262. https://doi.org/10.1371/journal.pone.0249262 doi: 10.1371/journal.pone.0249262
[7]	N. Scott, A. Palmer, D. Delport, R. Abeysuriya, R. M. Stuart, C. C. Kerr, et al., Modelling the impact of relaxing COVID-19 control measures during a period of low viral transmission, Med. J. Aust., 214 (2021), 79–83. https://doi.org/10.5694/mja2.50845 doi: 10.5694/mja2.50845
[8]	L. Cirrincione, F. Plescia, C. Ledda, V. Rapisarda, D. Martorana, G. Lacca, et al., COVID-19 pandemic: New prevention and protection measures, Sustainability, 14 (2022), 4766. https://doi.org/10.3390/su14084766 doi: 10.3390/su14084766
[9]	J. Wallinga, P. Teunis, Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures, Am. J. Epidemiol., 160 (2004), 509–516. https://doi.org/10.1093/aje/kwh255 doi: 10.1093/aje/kwh255
[10]	H. Yu, W. J. Alonso, L. Feng, Y. Tan, Y. Shu, W. Yang, et al., Characterization of regional influenza seasonality patterns in China and implications for vaccination strategies: Spatio-temporal modeling of surveillance data, PLoS Med., 10 (2013), e1001552. https://doi.org/10.1371/journal.pmed.1001552 doi: 10.1371/journal.pmed.1001552
[11]	C. You, Y. Deng, W. Hu, J. Sun, Q. Lin, F. Zhou, et al., Estimation of the time-varying reproduction number of COVID-19 outbreak in China, Int. J. Hyg. Environ. Health, 228 (2020), 113555. https://doi.org/10.1016/j.ijheh.2020.113555 doi: 10.1016/j.ijheh.2020.113555
[12]	I. Locatelli, B. Trächsel, V. Rousson, Estimating the basic reproduction number for COVID-19 in Western Europe, Plos One, 16 (2021), e0248731. https://doi.org/10.1371/journal.pone.0248731 doi: 10.1371/journal.pone.0248731
[13]	Y. Alimohamadi, M. Taghdir, M. Sepandi, Estimate of the basic reproduction number for COVID-19: a systematic review and meta-analysis, J. Prev. Med. Public Health, 53 (2020), 151–157. https://doi.org/10.3961%2Fjpmph.20.076
[14]	A. Cori, N. M. Ferguson, C. Fraser, S. Cauchemez, A new framework and software to estimate time-varying reproduction numbers during epidemics, Am. J. Epidemiol., 178 (2013), 1505–1512. https://doi.org/10.1093/aje/kwt133 doi: 10.1093/aje/kwt133
[15]	H. Nishiura, Backcalculating the Incidence of Infection with COVID-19 on the Diamond Princess, J. Clin. Med., 9 (2020), 657. https://doi.org/10.3390/jcm9030657 doi: 10.3390/jcm9030657
[16]	E. Goldstein, J. Dushoff, J. Ma, J. B. Plotkin, D. J. Earn, M. Lipsitch, et al., Reconstructing influenza incidence by deconvolution of daily mortality time series, Proc. Natl. Acad. Sci. U S A, 106 (2009), 21825–21829. https://doi.org/10.1073/pnas.0902958106 doi: 10.1073/pnas.0902958106
[17]	P. Nouvellet, S. Bhatia, A. Cori, K. E. Ainslie, M. Baguelin, S. Bhatt, et al., Reduction in mobility and COVID-19 transmission, Nat. Commun., 12 (2021), 1090. https://doi.org/10.1038/s41467-021-21358-2 doi: 10.1038/s41467-021-21358-2
[18]	M. Lipsitch, C. Viboud, Influenza seasonality: Lifting the fog, Proc. Natl. Acad. Sci. U S A, 106 (2009), 3645–3646. https://doi.org/10.1073/pnas.0900933106 doi: 10.1073/pnas.0900933106
[19]	J. Dushoff, J. Plotkin, S. Levin, D. Earn, Dynamical resonance can account for seasonality of influenza epidemics, Proc. Natl. Acad. Sci. U S A, 101 (2004), 16915–16916. https://doi.org/10.1073/pnas.0407293101 doi: 10.1073/pnas.0407293101
[20]	BC Centre for Disease Control. 2020 British Columbia COVID-19 Dashboard on the BCCDC website. http://www.bccdc.ca/health-professionals/data-reports/respiratory-diseases
[21]	H. Xin, Y. Li, P. Wu, Z. Li, E. H. Y. Lau, Y Qin, et al., Estimating the latent period of coronavirus disease 2019 (COVID-19), Clin. Infect. Dis., 74 (2022), 1678–1681. https://doi.org/10.1093/cid/ciab746 doi: 10.1093/cid/ciab746
[22]	S. M. Moghadas, M. C. Fitzpatrick, P. Sah, A. Pandey, A. Shoukat, B. H. Singer, et al., The implications of silent transmission for the control of COVID-19 outbreaks, Proc. Natl. Acad. Sci. U S A, 117 (2020), 17513–17515. https://doi.org/10.1073/pnas.2008373117 doi: 10.1073/pnas.2008373117
[23]	A. W. Byrne, D. McEvoy, A. B. Collins, K. Hunt, M. Casey, A. Barber, et al., Inferred duration of infectious period of SARS-CoV-2: Rapid scoping review and analysis of available evidence for asymptomatic and symptomatic COVID-19 cases, BMJ Open, 10 (2020), e039856. http://doi.org/10.1136/bmjopen-2020-039856 doi: 10.1136/bmjopen-2020-039856
[24]	Y. Xiang, Y. Jia, L. Chen, L. Guo, B. Shu, E. Long, COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models, Infect. Dis. Model., 6 (2021), 324–342. https://doi.org/10.1016/j.idm.2021.01.001 doi: 10.1016/j.idm.2021.01.001
[25]	S. Zhao, B. Tang, S. S. Musa, S. Ma, J. Zhang, M. Zeng, et al., Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data, Epidemics, 36 (2021), 100482. https://doi.org/10.1016/j.epidem.2021.100482 doi: 10.1016/j.epidem.2021.100482
[26]	K. Chatterjee, K. Chatterjee, A. Kumar, S. Shankar, Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model, Med J Armed Forces India., 76 (2020), 147–155. https://doi.org/10.1016/j.mjafi.2020.03.022 doi: 10.1016/j.mjafi.2020.03.022
[27]	BC Centre for Disease Control. 2020 British Columbia COVID-19 Daily Situation Report. http://www.bccdc.ca/Health-Info-Site/Documents/BC-Surveillance-Summary-June%20-9-2020.pdf
[28]	BC Centre for Disease Control, COVID-19 Situation Report. https://bccdc.shinyapps.io/respiratory-covid-sitrep/COVID-19-outbreaks
[29]	BC Centre for Disease Control. Weekly Update on Variants of Concern (VOC) 30 April 2021. http://www.bccdc.ca/Health-Info-Site/Documents/VoC/VoC_weekly_04302021.pdf
[30]	Public Health Agency of Canada. COVID-19 Epidemiology Update: Testing and Variants. https://health-infobase.canada.ca/covid-19/testing-variants.html

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