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Studying social awareness of physical distancing in mitigating COVID-19 transmission

  • Received: 05 August 2020 Accepted: 14 October 2020 Published: 28 October 2020
  • Since the initial identification of a COVID-19 case in Wuhan, China, the novel disease quickly becomes a global pandemic emergency. In this paper, we propose a dynamic model that incorporates individuals' behavior change in social interactions at different stages of the epidemics. We fit our model to the data in Ontario, Canada and calculate the effective reproduction number $\mathcal{R}_t$ within each stage. Results show that $\mathcal{R}_t$ > 1 if the public's awareness to practice physical distancing is rela-tively low and $\mathcal{R}_t$ < 1 otherwise. Simulations show that a reduced contact rate between the susceptible and asymptomatic/unreported symptomatic individuals is effective in mitigating the disease spread. Moreover, sensitivity analysis indicates that an increasing contact rate may lead to a second wave of disease outbreak. We also investigate the effectiveness of disease intervention strategies. Simulations demonstrate that enlarging the testing capacity and motivating infected individuals to test for an early diagnosis may facilitate mitigating the disease spread in a relatively short time. Results also indicate a significantly faster decline of confirmed positive cases if individuals practice strict physical distancing even if restricted measures are lifted.

    Citation: Xiaoying Wang. Studying social awareness of physical distancing in mitigating COVID-19 transmission[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7428-7441. doi: 10.3934/mbe.2020380

    Related Papers:

  • Since the initial identification of a COVID-19 case in Wuhan, China, the novel disease quickly becomes a global pandemic emergency. In this paper, we propose a dynamic model that incorporates individuals' behavior change in social interactions at different stages of the epidemics. We fit our model to the data in Ontario, Canada and calculate the effective reproduction number $\mathcal{R}_t$ within each stage. Results show that $\mathcal{R}_t$ > 1 if the public's awareness to practice physical distancing is rela-tively low and $\mathcal{R}_t$ < 1 otherwise. Simulations show that a reduced contact rate between the susceptible and asymptomatic/unreported symptomatic individuals is effective in mitigating the disease spread. Moreover, sensitivity analysis indicates that an increasing contact rate may lead to a second wave of disease outbreak. We also investigate the effectiveness of disease intervention strategies. Simulations demonstrate that enlarging the testing capacity and motivating infected individuals to test for an early diagnosis may facilitate mitigating the disease spread in a relatively short time. Results also indicate a significantly faster decline of confirmed positive cases if individuals practice strict physical distancing even if restricted measures are lifted.


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    [1] World Health Organization, (2020). Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
    [2] N. van Doremalen, T. Bushmaker, D. H. Morris, M. G. Holbrook, A. Gamble, B. N. Williamson, et al., Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1, N. Engl. J. Med., 382 (2020), 1564-1567. doi: 10.1056/NEJMc2004973
    [3] X. He, E. H. Y. Lau, P. Wu, X. Deng, J. Wang, X. Hao, et al., Temporal dynamics in viral shedding and transmissibility of COVID-19, Nat. Med., 26 (2020), 672-675. doi: 10.1038/s41591-020-0869-5
    [4] A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, et al., Early dynamics of transmission and control of COVID-19: a mathematical modelling study, Lancet Infect. Dis., 20 (2020), 553-558. doi: 10.1016/S1473-3099(20)30144-4
    [5] Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, N. Engl. J. Med., 382 (2020), 1199-1207. doi: 10.1056/NEJMoa2001316
    [6] The novel coronavirus pneumonia emergency response epidemiology team, The epidemiological characteristics of an outbreak of 2019 novel coronavirus disease (COVID-19)-China, China CDC Weekly, 2 (2020), 113-122.
    [7] Epidemiologic Summary: COVID-19 in Ontario: January 15, 2020 to May 18, 2020, Ontario Goverment, (2020). Available from: https://files.ontario.ca/moh-covid-19-report-en-2020-05-18.pdf.
    [8] S. He, S. Tang, L. Rong, A discrete stochastic model of the COVID-19 outbreak: Forecast and control, Math. Biosci. Eng., 17 (2020), 2792-2804. doi: 10.3934/mbe.2020153
    [9] K. Iwata, C. Miyakoshi, A simulation on potential secondary spread of novel coronavirus in an exported country using a stochastic epidemic SEIR model, J. Clin. Med., 9 (2020), 944. doi: 10.3390/jcm9040944
    [10] Z. Liu, P. Magal, O. Seydi, G. Webb, Understanding unreported cases in the COVID-19 epidemic outbreak in Wuhan, China, and the importance of major public health interventions, Biology, 9 (2020), 50.
    [11] Z. Liu, P. Magal, O. Seydi, G. Webb, A COVID-19 epidemic model with latency period, Infect. Disease Model., 5 (2020), 323-337. doi: 10.1016/j.idm.2020.03.003
    [12] S. Ruan, Likelihood of survival of coronavirus disease 2019, Lancet Infect. Dis., 20 (2020), 630- 631. doi: 10.1016/S1473-3099(20)30257-7
    [13] B. Tang, X. Wang, Q. Li, N. L. Bragazzi, S. Tang, Y. Xiao, et al., Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions, J. Clin. Med., 9 (2020), 462. doi: 10.3390/jcm9020462
    [14] C. Yang, J. Wang, A mathematical model for the novel coronavirus epidemic in Wuhan, China, Math. Biosci. Eng., 17 (2020), 2708-2724.
    [15] W. Zhou, A. Wang, F. Xia, Y. Xiao, S. Tang, Effects of media reporting on mitigating spread of COVID-19 in the early phase of the outbreak, Math. Biosci. Eng., 17 (2020), 2693-2707. doi: 10.3934/mbe.2020147
    [16] H. Zhao, Z. Feng, Staggered release policies for COVID-19 control: Costs and benefits of relaxing restrictions by age and risk, Math. Biosci., 326 (2020), 108405. doi: 10.1016/j.mbs.2020.108405
    [17] E. Abdollahi, M. Haworth-Brockman, Y. Keynan, J. M. Langley, S. M. Moghadas, Simulating the effect of school closure during COVID-19 outbreaks in Ontario, Canada, BMC Med., 18 (2020), 1-8.
    [18] B. Tang, F. Scarabel, N. L. Bragazzi, Z. McCarthy, M. Glazer, Y. Xiao, et al., De-escalation by reversing the escalation with a stronger synergistic package of contact tracing, quarantine, isolation and personal protection: feasibility of preventing a COVID-19 rebound in Ontario, Canada, as a Case Study, Biology, 9 (2020), 100. doi: 10.3390/biology9050100
    [19] A. R. Tuite, D. N. Fisman, A. L. Greer, Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada, CMAJ, 192 (2020), E497-E505.
    [20] J. Wu, B. Tang, N. L. Bragazzi, K. Nah, Z. McCarthy, Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada, J. Math. Ind., 10 (2020), 15.
    [21] O. Diekmann, J. A. P. Heesterbeek, Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation, John Wiley, (2000).
    [22] H. Nishiura, G. Chowell, The effective reproduction number as a prelude to statistical estimation of time-dependent epidemic trends, in Mathematical and Statistical Estimation Approaches in Epidemiology, Springer, Dordrecht, (2009).
    [23] P. Van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29-48. doi: 10.1016/S0025-5564(02)00108-6
    [24] K. Mizumoto, K. Kagaya, A. Zarebski, G. Chowell, Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020, Euro Surveill., 25 (2020), 12.
    [25] H. Nishiura, T. Kobayashi, T. Miyama, A. Suzuki, S. Jung, K. Hayashi, et al., Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19), Int. J. Infect. Dis., 94 (2020), 154-155. doi: 10.1016/j.ijid.2020.03.020
    [26] C. Wang, L. Liu, X. Hao, H. Guo, Q. Wang, J. Huang, et al., Evolving epidemiology and impact of non-pharmaceutical interventions on the outbreak of coronavirus disease 2019 in Wuhan, China, MedRxiv, https://doi.org/10.1101/2020.03.03.20030593.
    [27] Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19), CDC, (2020). Available from: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html.
    [28] H. Haario, M. Laine, A. Mira, E. Saksman, DRAM: Efficient adaptive MCMC, Stat. Comput., 16 (2006), 339-354. doi: 10.1007/s11222-006-9438-0
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