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Media impact switching surface during an infectious disease outbreak.

Xiao Y, Tang S, Wu J - Sci Rep (2015)

Bottom Line: Our analysis implies that media coverage significantly delayed the epidemic's peak and decreased the severity of the outbreak.Moreover, media impacts are not always effective in lowering the disease transmission during the entire outbreak, but switch on and off in a highly nonlinear fashion with the greatest effect during the early stage of the outbreak.The finding draws the attention to the important role of informing the public about 'the rate of change of case numbers' rather than 'the absolute number of cases' to alter behavioral changes, through a self-adaptive media impact switching on and off, for better control of disease transmission.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics, Xi'an Jiaotong University, Xi'an 710049, P. R. China.

ABSTRACT
There are many challenges to quantifying and evaluating the media impact on the control of emerging infectious diseases. We modeled such media impacts using a piecewise smooth function depending on both the case number and its rate of change. The proposed model was then converted into a switching system, with the switching surface determined by a functional relationship between susceptible populations and different subgroups of infectives. By parameterizing the proposed model with the 2009 A/H1N1 influenza outbreak data in the Shaanxi province of China, we observed that media impact switched off almost as the epidemic peaked. Our analysis implies that media coverage significantly delayed the epidemic's peak and decreased the severity of the outbreak. Moreover, media impacts are not always effective in lowering the disease transmission during the entire outbreak, but switch on and off in a highly nonlinear fashion with the greatest effect during the early stage of the outbreak. The finding draws the attention to the important role of informing the public about 'the rate of change of case numbers' rather than 'the absolute number of cases' to alter behavioral changes, through a self-adaptive media impact switching on and off, for better control of disease transmission.

No MeSH data available.


Related in: MedlinePlus

Illustrations of the switching surface of SC (A) and its solutions (B–C) with parameters as listed in Table 1.  represents the switching surfaces for the first (second) outbreak. The thick and thin curves denote the trajectories of the system (2) with (6) with media impact switched on and off, respectively. (D) Partial rank correlation coefficients illustrating the dependence of Sc on each parameter. Note that variable I varies in (1, 500) with mean value of 250, and Iq varies in (1, 50) with mean value of 4. Parameter α varies in (0, 0.02%) with mean value of 0.003%34, μ varies in (1/60, 1/80) with mean value of 1/7435.
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f2: Illustrations of the switching surface of SC (A) and its solutions (B–C) with parameters as listed in Table 1. represents the switching surfaces for the first (second) outbreak. The thick and thin curves denote the trajectories of the system (2) with (6) with media impact switched on and off, respectively. (D) Partial rank correlation coefficients illustrating the dependence of Sc on each parameter. Note that variable I varies in (1, 500) with mean value of 250, and Iq varies in (1, 50) with mean value of 4. Parameter α varies in (0, 0.02%) with mean value of 0.003%34, μ varies in (1/60, 1/80) with mean value of 1/7435.

Mentions: The formula Sc reveals dependence of the switching surface on the parameters and the numbers of infected and isolated individuals. During the disease outbreak, the switching surface Sc and the number of susceptible individuals change. Depending on the relative sizes of these populations, the media impact switches on and off dynamically. To examine how long and/or how often the media impact remains effective, we simulate the switching system using the parameters listed in Table 1. It is interesting to note that media impact remains effective almost until the peak of the epidemic, and then switches off, as shown in Fig. 2(B–C). This figure also shows that media impact may switch on again during the subsequent waves. In particular, increasing the susceptible size S(0) at day 50 induces the second wave. During this second wave media impact switches on, as shown in Fig. 2(B–C).


Media impact switching surface during an infectious disease outbreak.

Xiao Y, Tang S, Wu J - Sci Rep (2015)

Illustrations of the switching surface of SC (A) and its solutions (B–C) with parameters as listed in Table 1.  represents the switching surfaces for the first (second) outbreak. The thick and thin curves denote the trajectories of the system (2) with (6) with media impact switched on and off, respectively. (D) Partial rank correlation coefficients illustrating the dependence of Sc on each parameter. Note that variable I varies in (1, 500) with mean value of 250, and Iq varies in (1, 50) with mean value of 4. Parameter α varies in (0, 0.02%) with mean value of 0.003%34, μ varies in (1/60, 1/80) with mean value of 1/7435.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4296304&req=5

f2: Illustrations of the switching surface of SC (A) and its solutions (B–C) with parameters as listed in Table 1. represents the switching surfaces for the first (second) outbreak. The thick and thin curves denote the trajectories of the system (2) with (6) with media impact switched on and off, respectively. (D) Partial rank correlation coefficients illustrating the dependence of Sc on each parameter. Note that variable I varies in (1, 500) with mean value of 250, and Iq varies in (1, 50) with mean value of 4. Parameter α varies in (0, 0.02%) with mean value of 0.003%34, μ varies in (1/60, 1/80) with mean value of 1/7435.
Mentions: The formula Sc reveals dependence of the switching surface on the parameters and the numbers of infected and isolated individuals. During the disease outbreak, the switching surface Sc and the number of susceptible individuals change. Depending on the relative sizes of these populations, the media impact switches on and off dynamically. To examine how long and/or how often the media impact remains effective, we simulate the switching system using the parameters listed in Table 1. It is interesting to note that media impact remains effective almost until the peak of the epidemic, and then switches off, as shown in Fig. 2(B–C). This figure also shows that media impact may switch on again during the subsequent waves. In particular, increasing the susceptible size S(0) at day 50 induces the second wave. During this second wave media impact switches on, as shown in Fig. 2(B–C).

Bottom Line: Our analysis implies that media coverage significantly delayed the epidemic's peak and decreased the severity of the outbreak.Moreover, media impacts are not always effective in lowering the disease transmission during the entire outbreak, but switch on and off in a highly nonlinear fashion with the greatest effect during the early stage of the outbreak.The finding draws the attention to the important role of informing the public about 'the rate of change of case numbers' rather than 'the absolute number of cases' to alter behavioral changes, through a self-adaptive media impact switching on and off, for better control of disease transmission.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics, Xi'an Jiaotong University, Xi'an 710049, P. R. China.

ABSTRACT
There are many challenges to quantifying and evaluating the media impact on the control of emerging infectious diseases. We modeled such media impacts using a piecewise smooth function depending on both the case number and its rate of change. The proposed model was then converted into a switching system, with the switching surface determined by a functional relationship between susceptible populations and different subgroups of infectives. By parameterizing the proposed model with the 2009 A/H1N1 influenza outbreak data in the Shaanxi province of China, we observed that media impact switched off almost as the epidemic peaked. Our analysis implies that media coverage significantly delayed the epidemic's peak and decreased the severity of the outbreak. Moreover, media impacts are not always effective in lowering the disease transmission during the entire outbreak, but switch on and off in a highly nonlinear fashion with the greatest effect during the early stage of the outbreak. The finding draws the attention to the important role of informing the public about 'the rate of change of case numbers' rather than 'the absolute number of cases' to alter behavioral changes, through a self-adaptive media impact switching on and off, for better control of disease transmission.

No MeSH data available.


Related in: MedlinePlus