Optimal media reporting intensity on mitigating spread of an emerging infectious disease

PLoS One. 2019 Mar 21;14(3):e0213898. doi: 10.1371/journal.pone.0213898. eCollection 2019.

Abstract

Mass media reports can induce individual behaviour change during a disease outbreak, which has been found to be useful as it reduces the force of infection. We propose a compartmental model by including a new compartment of the intensity of the media reports, which extends existing models by considering a novel media function, which is dependent both on the number of infected individuals and on the intensity of mass media. The existence and stability of the equilibria are analyzed and an optimal control problem of minimizing the total number of cases and total cost is considered, using reduction or enhancement in the media reporting rate as the control. With the help of Pontryagin's Maximum Principle, we obtain the optimal media reporting intensity. Through parameterization of the model with the 2009 A/H1N1 influenza outbreak data in the 8th Hospital of Xi'an in Shaanxi Province of China, we obtain the basic reproduction number for the formulated model with two particular media functions. The optimal media reporting intensity obtained here indicates that during the early stage of an epidemic we should quickly enhance media reporting intensity, and keep it at a maximum level until it can finally weaken when epidemic cases have decreased significantly. Numerical simulations show that media impact reduces the number of cases during an epidemic, but that the number of cases is further mitigated under the optimal reporting intensity. Sensitivity analysis implies that the outbreak severity is more sensitive to the weight α1 (weight of media effect sensitive to infected individuals) than weight α2 (weight of media effect sensitive to media items).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Basic Reproduction Number
  • China / epidemiology
  • Communicable Diseases, Emerging / epidemiology*
  • Communicable Diseases, Emerging / prevention & control
  • Communicable Diseases, Emerging / transmission
  • Computer Simulation
  • Disease Outbreaks* / prevention & control
  • Disease Outbreaks* / statistics & numerical data
  • Epidemics / prevention & control
  • Epidemics / statistics & numerical data
  • Humans
  • Influenza A Virus, H1N1 Subtype
  • Influenza, Human / epidemiology
  • Mass Media*
  • Mathematical Concepts
  • Models, Biological

Grants and funding

The authors were supported by the National Natural Science Foundation of China (NSFC, 11631012, 11571273), the Natural Science and Engineering Research Council of Canada (NSERC) and the York Research Chair. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.