title: Media coverage and hospital notifications: Correlation analysis and optimal media impact duration to manage a pandemic.
abstract: News reporting has the potential to modify a community's knowledge of emerging infectious diseases and affect peoples' attitudes and behavior. Here we developed a quantitative approach to evaluate the effects of media on such behavior. Statistically significant correlations between the number of new hospital notifications, during the 2009 A/H1N1 influenza epidemic in the Shaanxi province of China, and the number of daily news items added to eight major websites were found from Pearson correlation and cross-correlation analyses. We also proposed a novel model to examine the implication for transmission dynamics of these correlations. The model incorporated the media impact function into the intensity of infection, and enhanced the traditional epidemic SEIR model with the addition of media dynamics. We used a nonlinear least squares estimation to identify the best-fit parameter values in the model from the observed data. We also carried out the uncertainty and sensitivity analyses to determine key parameters during early phase of the disease outbreak for the final outcome of the outbreak with media impact. The findings confirm the importance of responses by individuals to the media reports, with behavior changes having important consequence for the emerging infectious disease control. Therefore, for mitigating emerging infectious diseases, media reports should be focused on how to guide people's behavioral changes, which are critical for limiting the spread of disease.
authors.affiliation: College of Mathematics and Information Science, Shaanxi Normal University, Xi׳an 710062, PR China.
authors.forename: Qinling
authors.initials: Q
authors.lastname: Yan
authors.affiliation: College of Mathematics and Information Science, Shaanxi Normal University, Xi׳an 710062, PR China. Electronic address: sytang@snnu.edu.cn.
authors.forename: Sanyi
authors.initials: S
authors.lastname: Tang
authors.affiliation: Department of Design, School of the Arts, Media, Performance & Design, York University, Toronto, Ontario, Canada M3J 1P3.
authors.forename: Sandra
authors.initials: S
authors.lastname: Gabriele
authors.affiliation: Centre for Disease Modelling, York University, Toronto, Ontario, Canada M3J 1P3.
authors.forename: Jianhong
authors.initials: J
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D002681:China / Q000453:epidemiology; D006761:Hospitals / Q000706:statistics & numerical data*; D054884:Host-Pathogen Interactions; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000502:physiology; D007251:Influenza, Human / Q000175:diagnosis / Q000453:epidemiology* / Q000821:virology; D016018:Least-Squares Analysis; D008402:Mass Media / Q000706:statistics & numerical data*; D017711:Nonlinear Dynamics; D058873:Pandemics / Q000517:prevention & control*; D013997:Time Factors
keywords: A/H1N1; Basic reproduction number; Behavior change; Correlation analysis; Media report; SEIR model