Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Effects of media reporting on mitigating spread of COVID-19 in the early phase of the outbreak

1 College of Mathematics and Information Science, Shaanxi Normal University, Xi’an 710062, China
2 School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji 721013, China
3 School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China

These authors contributed equally to this work.

Special Issues: Modeling the Biological, Epidemiological, Immunological, Molecular, Virological Aspects of COVID-19

The 2019 novel coronavirus disease (COVID-19) is running rampantly in China and is swiftly spreading to other countries in the world, which causes a great concern on the global public health. The absence of specific therapeutic treatment or effective vaccine against COVID-19 call for other avenues of the prevention and control measures. Media reporting is thought to be effective to curb the spreading of an emergency disease in the early stage. Cross-correlation analysis based on our collected data demonstrated a strong correlation between media data and the infection case data. Thus we proposed a deterministic dynamical model to examine the interaction of the disease progression and the media reports and to investigate the effectiveness of media reporting on mitigating the spread of COVID-19. The basic reproduction number was estimated as 5.3167 through parameterization of the model with the number of cumulative confirmed cases, the number of cumulative deaths and the daily number of media items. Sensitivity analysis suggested that, during the early phase of the COVID-19 outbreak, enhancing the response rate of the media reporting to the severity of COVID-19, and enhancing the response rate of the public awareness to the media reports, both can bring forward the peak time and reduce the peak size of the infection significantly. These findings suggested that besides improving the medical levels, media coverage can be considered as an effective way to mitigate the disease spreading during the initial stage of an outbreak.
  Article Metrics

Keywords COVID-19; media reporting; mathematical model; basic reproduction number; sensitivity analysis

Citation: Weike Zhou, Aili Wang, Fan Xia, Yanni Xiao, Sanyi Tang. Effects of media reporting on mitigating spread of COVID-19 in the early phase of the outbreak. Mathematical Biosciences and Engineering, 2020, 17(3): 2693-2707. doi: 10.3934/mbe.2020147


  • 1. Wuhan Municipal Health Commission, available from: http://wjw.wuhan.gov.cn/front/web/showDetail/2020010309017 (accessed on 8 February 2020).
  • 2. World Health Organization, available from: https://www.who.int/health-topics/coronavirus (accessed on 8 February 2020).
  • 3. P. Zhou, X. Yang, X. Wang, B. Hu, L. Zhang, W. Zhang, et al., Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin, BioRxiv, (2020). doi: 10.1101/2020.01.22.914952.
  • 4. 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.
  • 5. National Health Commission of the People's Republic of China, available from: http://www.nhc.gov.cn/xcs/yqtb/202002/6c305f6d70f545d59548ba17d79b8229.shtml (accessed on 8 February 2020).
  • 6. National Health Commission of the People's Republic of China, available from: http://www.nhc.gov.cn/xcs/zhengcwj/202002/18c1bb43965a4492907957875de02ae7.shtml (accessed on 8 February 2020).
  • 7. I. S. Kristiansen, P. A. Halvorsen, D. Gyrd-Hansen, Influenza pandemic: perception of risk and individual precautions in a general population, Cross sectional study, BMC Public Health, 7 (2007), 48.
  • 8. U. C. de Silva, J. Warachit, S. Waicharoen, M. Chittaganpitch, A preliminary analysis of the epidemiology of influenza A(H1N1) virus infection in Thailand from early outbreak data, June-July 2009, Euresurveilance, 14 (2009), 1-3.
  • 9. D. Roth, B. Henry, Social Distancing as a Pandemic Influenza Prevention Measure, National Collaborating Centre for Infectious Diseases, (2011).
  • 10. F. Huang, S. S. Zhou, S. S. Zhang, H. J. Wang, L. H. Tang, Temporal correlation analysis between malaria and meteorological factors in Motuo County, Tibet, Malaria J., 10 (2011), 54.
  • 11. G. E. Box, G. M. Jenkins, G. C. Reinsel, Time series analysis: forecasting and control, Forth edition, John Wiley & Sons, 2015.
  • 12. Health Commission of Hubei Province, available from: http://wjw.hubei.gov.cn/bmdt/ztzl/fkxxgzbdgrfyyq/ (accessed on 8 February 2020).
  • 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.
  • 14. Chinese Center for Disease Control and Prevention. Report about 2019-nCoV, available from: http://www.chinacdc.cn/yyrdgz/202001/P020200128523354919292.pdf (accessed on 8 February 2020).
  • 15. M. W. Shen, Z. H. Peng, Y. N. Xiao, L. Zhang, Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China, BioRxiv, (2020).
  • 16. Wuhan Municipal Health Commission, available from: http://wjw.wuhan.gov.cn/front/web/showDetail/2020011109035 (accessed on 8 February 2020).
  • 17. J. A. Backer, D. Klinkenberg, J. Wallinga, The incubation period of 2019-nCoV infections among travellers from Wuhan, China, MedRxiv, (2020).
  • 18. Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, Early transmission dynamics in Wuhan, China, of novel coronavirus infected pneumonia, New Engl. J. Med., (2020).
  • 19. S. Zhao, S. Musa, Q. Lin, J. Ran, G. Yang, W. Wang, et al., Estimating the unreported number of novel coronavirus (2019-nCoV) vases in China in the first half of January 2020: a data-driven modelling analysis of the early outbreak, J. Clin. Med., 9 (2020), 388.
  • 20. J. Chan, S. Yuan, K. Kok, K. To, H. Chu, J. Yang, et al., A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster, Lancet, 395 (2020), 514-523.
  • 21. J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet, 395 (2020), 689-697.
  • 22. Y. Liu, A. A. Gayle, A. Wilder-Smith, J. Rocklov, The reproductive number of COVID-19 is higher compared to SARS coronavirus, J. Travel. Med., (2020), 1-4.
  • 23. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, Vital Surveillances: The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19)-China, 2020, China CDC weekly, 2 (2020), 113-122.
  • 24. Wuhan Municipal Health Commission, available from: http://wjw.wuhan.gov.cn/front/web/showDetail/2020012409132 (accessed on 4 March 2020).


This article has been cited by

  • 1. Qianqian Cui, Zengyun Hu, Yingke Li, Junmei Han, Zhidong Teng, Jing Qian, Dynamic variations of the COVID-19 disease at different quarantine strategies in Wuhan and mainland China, Journal of Infection and Public Health, 2020, 13, 6, 849, 10.1016/j.jiph.2020.05.014
  • 2. L.H.A. Monteiro, Short Communication, Ecological Complexity, 2020, 100836, 10.1016/j.ecocom.2020.100836
  • 3. Eladio J. Collado-Boira, Estefanía Ruiz-Palomino, Pablo Salas-Media, Ana Folch-Ayora, Maria Muriach, Pablo Baliño, “The COVID-19 outbreak”—An empirical phenomenological study on perceptions and psychosocial considerations surrounding the immediate incorporation of final-year Spanish nursing and medical students into the health system, Nurse Education Today, 2020, 92, 104504, 10.1016/j.nedt.2020.104504
  • 4. Akihiro Hisaka, Hideki Yoshioka, Hiroto Hatakeyama, Hiromi Sato, Yoshihiro Onouchi, Naohiko Anzai, Global Comparison of Changes in the Number of Test-Positive Cases and Deaths by Coronavirus Infection (COVID-19) in the World, Journal of Clinical Medicine, 2020, 9, 6, 1904, 10.3390/jcm9061904
  • 5. Piotr Staszkiewicz, Iwona Chomiak-Orsa, Igor Staszkiewicz, Dynamics of the COVID-19 Contagion and Mortality: Country Factors, Social Media, and Market Response Evidence From a Global Panel Analysis, IEEE Access, 2020, 8, 106009, 10.1109/ACCESS.2020.2999614
  • 6. Prasantha Bharathi Dhandapani, Dumitru Baleanu, Jayakumar Thippan, Vinoth Sivakumar, On stiff, fuzzy IRD-14 day average transmission model of COVID-19 pandemic disease, AIMS Bioengineering, 2020, 7, 4, 208, 10.3934/bioeng.2020018
  • 7. Oberiri Destiny Apuke, Bahiyah Omar, Fake News and COVID-19: Modelling the Predictors of Fake News Sharing Among Social Media Users, Telematics and Informatics, 2020, 101475, 10.1016/j.tele.2020.101475
  • 8. Zhiming Li, Zhidong Teng, Changxing Ma, 2019-nCoV Transmission in Hubei Province, China: Stochastic and Deterministic Analyses, Complexity, 2020, 2020, 1, 10.1155/2020/9012178

Reader Comments

your name: *   your email: *  

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved