The large-scale infection of COVID-19 has led to a significant impact on lives and economies around the world and has had considerable impact on global public health. Social distancing, mask wearing and contact tracing have contributed to containing or at least mitigating the outbreak, but how public awareness influences the effectiveness and efficiency of such approaches remains unclear. In this study, we developed a discrete compartment dynamic model to mimic and explore how media reporting and the strengthening containment strategies can help curb the spread of COVID-19 using Shaanxi Province, China, as a case study. The targeted model is parameterized based on multi-source data, including the cumulative number of confirmed cases, recovered individuals, the daily number of media-reporting items and the imported cases from the rest of China outside Shaanxi from January 23 to April 11, 2020. We carried out a sensitivity analysis to investigate the effect of media reporting and imported cases on transmission. The results revealed that reducing the intensity of media reporting, which would result in a significant increasing of the contact rate and a sizable decreasing of the contact-tracing rate, could aggravate the outbreak severity by increasing the cumulative number of confirmed cases. It also demonstrated that diminishing the imported cases could alleviate the outbreak severity by reducing the length of the epidemic and the final size of the confirmed cases; conversely, delaying implementation of lockdown strategies could prolong the length of the epidemic and magnify the final size. These findings suggest that strengthening media coverage and timely implementing of lockdown measures can significantly reduce infection.
Citation: Jin Guo, Aili Wang, Weike Zhou, Yinjiao Gong, Stacey R. Smith?. Discrete epidemic modelling of COVID-19 transmission in Shaanxi Province with media reporting and imported cases[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 1388-1410. doi: 10.3934/mbe.2022064
The large-scale infection of COVID-19 has led to a significant impact on lives and economies around the world and has had considerable impact on global public health. Social distancing, mask wearing and contact tracing have contributed to containing or at least mitigating the outbreak, but how public awareness influences the effectiveness and efficiency of such approaches remains unclear. In this study, we developed a discrete compartment dynamic model to mimic and explore how media reporting and the strengthening containment strategies can help curb the spread of COVID-19 using Shaanxi Province, China, as a case study. The targeted model is parameterized based on multi-source data, including the cumulative number of confirmed cases, recovered individuals, the daily number of media-reporting items and the imported cases from the rest of China outside Shaanxi from January 23 to April 11, 2020. We carried out a sensitivity analysis to investigate the effect of media reporting and imported cases on transmission. The results revealed that reducing the intensity of media reporting, which would result in a significant increasing of the contact rate and a sizable decreasing of the contact-tracing rate, could aggravate the outbreak severity by increasing the cumulative number of confirmed cases. It also demonstrated that diminishing the imported cases could alleviate the outbreak severity by reducing the length of the epidemic and the final size of the confirmed cases; conversely, delaying implementation of lockdown strategies could prolong the length of the epidemic and magnify the final size. These findings suggest that strengthening media coverage and timely implementing of lockdown measures can significantly reduce infection.
[1] | World Health Organization (WHO), Coronavirus disease (COVID-19) pandemic. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019. |
[2] | P. Zhou, X. Yang, X. Wang, B. Hu, L. Zhang, W. Zhang, et al., A pneumonia outbreak associated with a new coronavirus of probable bat origin, Nature, 579 (2020), 270–273. doi: 10.1038/s41586-020-2012-7. doi: 10.1038/s41586-020-2012-7 |
[3] | C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395 (2020), 497–506. doi: 10.1016/S0140-6736(20)30183-5. doi: 10.1016/S0140-6736(20)30183-5 |
[4] | World Health Organization(WHO), Timeline: WHO's COVID-19 response. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline. |
[5] | J. M. Tchuenche, N. Dube, C. P. Bhunu, R. J. Smith?, C. T. Bauch, The impact of media coverage on the transmission dynamics of human influenza, BMC Public Health, 11 (2011), 1–4. doi: 0.1186/1471-2458-11-S1-S5. |
[6] | Q. Yan, Y. Tang, D. Yan, J. Wang, L. Yang, X. Yang, et al., Impact of media reports on the early spread of COVID-19 epidemic, J. Theor. Biol., 502 (2020), 110385. doi: 10.1016/j.jtbi.2020.110385. doi: 10.1016/j.jtbi.2020.110385 |
[7] | Y. Xiao, S. Tang, J. Wu, Media impact switching surface during an infectious disease outbreak, Sci. Rep., 5 (2015), 7838. doi: 10.1038/srep07838. doi: 10.1038/srep07838 |
[8] | P. Song, Y. Xiao, Analysis of an epidemic system with two response delays in media impact function, Bull. Math. Biol., 81 (2019), 1582–1612. doi: 10.1007/s11538-019-00586-0. doi: 10.1007/s11538-019-00586-0 |
[9] | M. S. Rahman, M. L. Rahman, Media and education play a tremendous role in mounting AIDS awareness among married couples in Bangladesh, AIDS Res. Ther., 4 (2007), 10. doi: 10.1186/1742-6405-4-10. doi: 10.1186/1742-6405-4-10 |
[10] | M. Cinelli, W. Quattrociocchi, A. Galeazzi, The COVID-19 social media infodemic, Sci. Rep., 10 2020, 16598. doi: 10.1038/s41598-020-73510-5. doi: 10.1038/s41598-020-73510-5 |
[11] | 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), 2673–2707. doi: 10.3934/mbe.2020147. doi: 10.3934/mbe.2020147 |
[12] | Shaanxi Provincial Health Commission. Available from: http://sxwjw.shaanxi.gov.cn/sy/ztzl/fyfkzt/gzdt_2232/202004/t20200411_2118054.html. |
[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. doi: 10.3390/jcm9020462 |
[14] | B. Tang, F. Xia, S. Tang, N. L. Bragazzi, Q. Li, X. Sun, et al., The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China, Int. J. Infect. Dis., 96 (2020), 636–647. doi: 10.1016/j.ijid.2020.03.018. doi: 10.1016/j.ijid.2020.03.018 |
[15] | S. Zhao, S. S. Musa, Q. Lin, J. Ran, G. Yang, W. Wang, et al., Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: a data-driven modelling analysis of the early outbreak, J. Clin. Med., 9 (2020), 388. doi: 10.3390/jcm9020388. doi: 10.3390/jcm9020388 |
[16] | 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. doi: 10.1016/j.ijid.2020.01.050. doi: 10.1016/j.ijid.2020.01.050 |
[17] | B. Tang, N. L. Bragazzi, Q. Li, S. Tang, Y. Xiao, J. Wu, An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov), Infect. Dis. Modell., 5 (2020), 248–255. doi: 10.1016/j.idm.2020.02.001. doi: 10.1016/j.idm.2020.02.001 |
[18] | M. Gatto, E. Bertuzzo, L. Mari, S. Miccoli, L. Carraro, R. Casagrandi, A. Rinaldo, Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures, Proc. Nat. Acad. Sci., 117 (2020), 10484–10491. doi: 10.1073/pnas.2004978117. doi: 10.1073/pnas.2004978117 |
[19] | C. C. Kerr, R. M. Stuart, D. Mistry, R. G. Abeysuriya, K. Rosenfeld, G. R. Hart, et al., Covasim: an agent-based model of COVID-19 dynamics and interventions, PLOS Comput. Biol., 17 (2021), e1009149. doi: 10.1371/journal.pcbi.1009149. doi: 10.1371/journal.pcbi.1009149 |
[20] | R. E. Baker, S. W. Park, W. Yang, G. A. Vecchi, C. J. E. Metcalf, B. T. Grenfell, The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections, Proc. Nat. Acad. Sci., 117 (2020), 30547–30553. doi: 10.1073/pnas.2013182117. doi: 10.1073/pnas.2013182117 |
[21] | R. Laxminarayan, B. Wahl, S. R. Dudala, K. Gopal, B. C. Mohan, S. Neelima, Epidemiology and transmission dynamics of COVID-19 in two Indian states, Science, 370 (2020), 691–697. doi: 10.1126/science.abd7672. doi: 10.1126/science.abd7672 |
[22] | J. Zhang, M. Litvinova, Y. Liang, Y. Wang, W. Wang, S. Zhao, et al., Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China, Science, 368 (2020), 1481–1486. doi: 10.1126/science.abb8001. doi: 10.1126/science.abb8001 |
[23] | X. Hao, S. Cheng, D. Wu, T. Wu, X. Lin, C. Wang, Reconstruction of the full transmission dynamics of COVID-19 in Wuhan, Nature, 584 (2020), 420–424. doi: 10.1038/s41586-020-2554-8. doi: 10.1038/s41586-020-2554-8 |
[24] | D. He, S. Zhao, Q. Lin, Z. Zhuang, P. Cao, M. H. Wang, et al., The relative transmissibility of asymptomatic COVID-19 infections among close contacts, Int. J. Infect. Dis., 94 (2020), 145–147. doi: 10.1016/j.ijid.2020.04.034. doi: 10.1016/j.ijid.2020.04.034 |
[25] | X. Wang, S. Tang, Y. Chen, X. Feng, Y. Xiao, Z. Xu, When will be the resumption of work in Wuhan and its surrounding areas during COVID-19 epidemic? A data-driven network modeling analysis, Sci. Sinica, 50 (2020), 969–978. |
[26] | B. Maier, D. Brockmann, Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China, Science, 368 (2020), 742–746. doi: 10.1126/science.abb4557. doi: 10.1126/science.abb4557 |
[27] | J. Li, P. Yuan, J. Heffernan, T. Zheng, N. Ogden, B. Sander, et al., Fangcang shelter hospitals during the COVID-19 epidemic, Wuhan, China, Bull. World Health Organ., 98 (2020), 830–841. doi: 10.2471/BLT.20.258152. doi: 10.2471/BLT.20.258152 |
[28] | 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). doi: 10.1056/NEJMoa2001316. doi: 10.1056/NEJMoa2001316 |
[29] | M. Li, G. Sun, J. Zhang, Y. Zhao, X. Pei, L. Li, et al., Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases, Math. Biosci. Eng., 17 (2020), 3710–3720. doi: 10.3934/mbe.2020208. doi: 10.3934/mbe.2020208 |
[30] | F. Huang, S. Zhou, S. Zhang, H. Wang, L. Tang, Temporal correlation analysis between malaria and meteorological factors in Motuo County, Tibet, Malar. J., 10 (2011), 54. doi: 10.1186/1475-2875-10-54. doi: 10.1186/1475-2875-10-54 |
[31] | Shaanxi Provincial Bureau Of Statistics. Available from: http://tjj.shaanxi.gov.cn/upload/2020/pro/3sxtjnj/zk/indexch.html. |
[32] | 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. doi: 10.3934/mbe.2020153 |
[33] | Chinese Center for Disease Control and Prevention, Report about 2019-nCov, Available from: http://www.chinacdc.cn/yyrdgz/202001/P020200128523354919292.pdf. |
[34] | A. Morton, B. F. Finkenstädt, Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods, J. R. Stat. Soc. Ser. C Appl. Stat., 54 (2010), 575–594. doi: 10.1111/j.1467-9876.2005.05366.x. doi: 10.1111/j.1467-9876.2005.05366.x |
[35] | J. M. Heffernan, R. J. Smith?, L. M. Wahl, Perspectives on the basic reproduction ratio, J. R. Soc. Interface, 2 (2005), 281–283. doi: 10.1098/rsif.2005.0042. doi: 10.1098/rsif.2005.0042 |
[36] | L. J. S. Allen, P. van den Driessche, The basic reproduction number in some discrete-time epidemic models, J. Differ. Equations Appl., 14 (2008), 1127–1147. doi: 10.1080/10236190802332308. doi: 10.1080/10236190802332308 |
[37] | Y. Liu, A. A. Gayle, A. Wilder-Smith, J. Rocklöv, The reproductive number of COVID-19 is higher compared to SARS coronavirus, J. Travel Med., 27 (2020), 1–4. doi: 10.1093/jtm/taaa021. doi: 10.1093/jtm/taaa021 |
mbe-19-02-064--Supplementary.pdf |