Research article

The impact of intervention strategies and prevention measurements for controlling COVID-19 outbreak in Saudi Arabia

  • Received: 24 August 2020 Accepted: 29 October 2020 Published: 13 November 2020
  • On 11 March 2020, the World Health Organization announced the novel coronavirus COVID-19 outbreak as a pandemic due to the rapid growth in the number of cases worldwide. The ability of countries to contain and mitigate interventions is crucial in controlling the exponential spread of the novel virus. Several social distancing and control measurements have been applied in Saudi Arabia to mitigate COVID-19 epidemic such as quarantine, schools closure, suspending travels, reducing crowds, people movement restrictions, self-isolation and contacts tracing. This research aims to study the country interventions in Saudi Arabia and their impact on decreasing the spread of COVID-19. This paper examined different control measurements scenarios produced by a modified SEIR mathematical model with an emphasis on testing capacity expansion and number of critical cases. The modified SEIR mathematical model is solved numerically using Rung-Kutta analysis method for solving the modified SEIR system of ordinary differential equations. The simulation results revealed that the interventions are vital to flatten the virus spread curve. Early implementation of country interventions can delay the peak and decrease the population fatality rate.

    Citation: Adil Yousif, Awad Ali. The impact of intervention strategies and prevention measurements for controlling COVID-19 outbreak in Saudi Arabia[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 8123-8137. doi: 10.3934/mbe.2020412

    Related Papers:

  • On 11 March 2020, the World Health Organization announced the novel coronavirus COVID-19 outbreak as a pandemic due to the rapid growth in the number of cases worldwide. The ability of countries to contain and mitigate interventions is crucial in controlling the exponential spread of the novel virus. Several social distancing and control measurements have been applied in Saudi Arabia to mitigate COVID-19 epidemic such as quarantine, schools closure, suspending travels, reducing crowds, people movement restrictions, self-isolation and contacts tracing. This research aims to study the country interventions in Saudi Arabia and their impact on decreasing the spread of COVID-19. This paper examined different control measurements scenarios produced by a modified SEIR mathematical model with an emphasis on testing capacity expansion and number of critical cases. The modified SEIR mathematical model is solved numerically using Rung-Kutta analysis method for solving the modified SEIR system of ordinary differential equations. The simulation results revealed that the interventions are vital to flatten the virus spread curve. Early implementation of country interventions can delay the peak and decrease the population fatality rate.
    加载中


    [1] B. Ivorra, B. Martínez-López, J. M. Sánchez-Vizcaíno, Á. M. Ramos, Mathematical formulation and validation of the Be-FAST model for classical swine fever virus spread between and within farms, Ann. Oper. Res., 219 (2014), 25-47.
    [2] D. Cucinotta, M. Vanelli, WHO Declares COVID-19 a Pandemic, Acta bio-medica: Atenei Parmensis, 91 (2020), 157.
    [3] X. Rong, L. Yang, H. Chu, M. Fan, Effect of delay in diagnosis on transmission of COVID-19, Math. Biosci. Eng., 17 (2020), 2725-2740.
    [4] S. H. Ebrahim, Q. A. Ahmed, E. Gozzer, P. Schlagenhauf, Z. A. Memish, Covid-19 and community mitigation strategies in a pandemic, BMJ, 368 (2020), m1066.
    [5] S. Zhang, M. Diao, W. Yu, L. Pei, Z. Lin, D. Chen, Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis, Int. J. Infect. Dis., 93 (2020), 201-204.
    [6] H. Tian, Y. Li, Y. Liu, M. U. Kraemer, B. Chen, J. Cai, et al., Early evaluation of Wuhan City travel restrictions in response to the 2019 novel coronavirus outbreak, medRxiv (2020).
    [7] C. Rothe, M. Schunk, P. Sothmann, G. Bretzel, G. Froeschl, C. Wallrauch, et al., Transmission of 2019-nCoV infection from an asymptomatic contact in Germany, N. Engl. J. Med., 382 (2020), 970-971.
    [8] D. Fanelli, F. Piazza, Analysis and forecast of COVID-19 spreading in China, Italy and France, Chaos, Solitons Fractals, 134 (2020), 109761.
    [9] L. Di Domenico, G. Pullano, G. Pullano, N. Hens, V. Colizza, Expected impact of school closure and telework to mitigate COVID-19 epidemic in France, EPIcx Lab, 15 (2020).
    [10] P. Yang, J. Qi, S. Zhang, X. Wang, G. Bi, Y. Yang, et al., Feasibility Study of Mitigation and Suppression Intervention Strategies for Controlling COVID-19 Outbreaks in London and Wuhan, Medrxiv, (2020).
    [11] A. Pan, L. Liu, C. Wang, H. Guo, X. Hao, Q. Wang, et al., Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China, Jama, 323 (2020), 1915-1923.
    [12] S. Flaxman, S. Mishra, A. Gandy, H. J. T. Unwin, T. A. Mellan, H. Coupland, et al., Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, 584 (2020), 257-261.
    [13] E. A. Iboi, O. O. Sharomi, C. N. Ngonghala, A. B. Gumel, Mathematical Modeling and Analysis of COVID-19 pandemic in Nigeria, Math. Biosci. Eng., 17 (2020), 7192-7220.
    [14] R. M. Anderson, H. Heesterbeek, D. Klinkenberg, T. D. Hollingsworth, How will country-based mitigation measures influence the course of the COVID-19 epidemic?, Lancet, 395 (2020), 931-934.
    [15] M. Batista, Estimation of the final size of the COVID-19 epidemic, medRxiv, (2020), 2020.02.16.20023606.
    [16] Y. Zhang, C. You, Z. Cai, J. Sun, W. Hu, X.-H. Zhou, Prediction of the COVID-19 outbreak based on a realistic stochastic model, medRxiv, (2020), 2002.03.10.20033803.
    [17] 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.
    [18] L. Jia, K. Li, Y. Jiang, X. Guo, Prediction and analysis of Coronavirus Disease 2019, arXiv preprint arXiv, (2020), 2003.05447.
    [19] Z. Liu, P. Magal, O. Seydi, G. Webb, Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data, arXiv preprint arXiv, (2020), 2002.12298.
    [20] L. Peng, W. Yang, D. Zhang, C. Zhuge, L. Hong, Epidemic analysis of COVID-19 in China by dynamical modeling, arXiv preprint arXiv, (2020), 2002.06563.
    [21] J. F. Rabajante, Insights from early mathematical models of 2019-nCoV acute respiratory disease (COVID-19) dynamics, arXiv preprint arXiv, (2020), 2002.05296.
    [22] A. Teslya, T. M. Pham, N. G. Godijk, M. E. Kretzschmar, M. C. Bootsma, G. Rozhnova, Impact of self-imposed prevention measures and short-term government intervention on mitigating and delaying a COVID-19 epidemic, Available at SSRN 3555213 (2020).
    [23] J. Dehning, J. Zierenberg, F. P. Spitzner, M. Wibral, J. P. Neto, M. Wilczek, et al., Inferring COVID-19 spreading rates and potential change points for case number forecasts, arXiv preprint arXiv, (2020), 2004.01105.
    [24] I. Frost, G. Osena, J. Craig, S. Hauck, E. Kalanxhi, O. Gatalo, et al., COVID-19 in Middle Africa: National Projections of Total and Severe Infections Under Different Lockdown Scenarios, (2020).
    [25] D. Alboaneen, B. Pranggono, D. Alshammari, N. Alqahtani, R. Alyaffer, Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia, Int. J. Environ. Res. Public Health, 17 (2020), 4568.
    [26] S. Yezli, A. Khan, COVID-19 social distancing in the Kingdom of Saudi Arabia: Bold measures in the face of political, economic, social and religious challenges, Travel Med. Infect. Dis., (2020), 101692.
    [27] M. Barry, M. Al Amri, Z. A. Memish, COVID-19 in the Shadows of MERS-CoV in the Kingdom of Saudi Arabia, JEGH, 10 (2020), 1-3.
    [28] M. Willman, D. Kobasa, J. Kindrachuk, A comparative analysis of factors influencing two outbreaks of Middle Eastern respiratory syndrome (MERS) in Saudi Arabia and South Korea, Viruses, 11 (2019), 1119.
    [29] COVID 19 Dashboard: Saudi Arabia, 2020, available from: https://covid19.moh.gov.sa/.
    [30] John Hopkins University and Medince, Coronavirus COVID-19 Global Cases, 2020, available from: https://coronavirus.jhu.edu/map.html.
    [31] M. Roser, H. Ritchie, E. Ortiz-Ospina, J. Hasell, Coronavirus Pandemic (COVID-19), Our World in Data, 2020. Available from: https://ourworldindata.org/coronavirus.
    [32] D. He, J. Dushoff, T. Day, J. Ma, D. J. D. Earn, Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales, Proc. R. Soc. B, 280 (2013), 20131345.
    [33] Q. Lin, S. Zhao, D. Gao, Y. Lou, S. Yang, S. S. Musa, et al., A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action, Int. J. Infect. Dis., 2020.
    [34] Saudi Unified National plateform, Saudi Reports and Statistics, (2019), available from: https://www.my.gov.sa/wps/portal/snp/aboutksa/saudiReportsAndStatistics.
    [35] S. Zhao, Q. Lin, J. Ran, S. S. Musa, G. Yang, W. Wang, et al., Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak, Int. J. Infect. Dis., 92 (2020), 214-217.
    [36] B. Ivorra, M. R. Ferrández, M. Vela-Pérez, A. Ramos, Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China, Commun. Nonlinear Sci. Numer. Simul., 88 (2020), 105303.
    [37] S. M. S. Elsheikh, M. K. Abbas, M. A. Bakheet, A. Degoot, A mathematical model for the transmission of Corona Virus Disease (COVID-19) in Sudan, (2020).
    [38] T. Piasecki, P. B. Mucha, M. Rosińska, A new SEIR type model including quarantine effects and its application to analysis of Covid-19 pandemia in Poland in March-April 2020, arXiv preprint arXiv, (2020), 2005.14532.
    [39] Å. Björck, Numerical methods for least squares problems, SIAM. 1996.
    [40] S. Van Huffel, P. Lemmerling, Total least squares and errors-in-variables modeling: analysis, algorithms and applications. Springer Science & Business Media, 2013.
    [41] G. Kemmer, S. Keller, Nonlinear least-squares data fitting in Excel spreadsheets, Nat. Protoc., 5 (2010), 267.
    [42] 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.
    [43] R. Chowdhury, K. Heng, M. S. R. Shawon, G. Goh, D. Okonofua, C. Ochoa-Rosales, et al., Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries, Eur. J. Epidemiol., 35 (2020), 389-399.
    [44] N. Ferguson, D. Laydon, G. Nedjati Gilani, N. Imai, K. Ainslie, M. Baguelin, et al., Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand, (2020).
    [45] S. L. Chang, N. Harding, C. Zachreson, O. M. Cliff, M. Prokopenko, Modelling transmission and control of the COVID-19 pandemic in Australia, arXiv preprint arXiv, (2020), 2003.10218.

    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
  • Reader Comments
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(13) PDF downloads(3) Cited by()

Article outline

Figures and Tables

Figures(12)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog