2013
DOI: 10.1016/j.physa.2013.07.067
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Impact of media coverage on epidemic spreading in complex networks

Abstract: h i g h l i g h t s• A new SIS network model obtained by introducing an information variable is proposed. • The diseases can be controlled through high efficiency of implementation.• The introduced parameters have significant impact on the final prevalence density.• The results may suggest effective control strategies incorporating media coverage. a b s t r a c t An SIS network model incorporating the influence of media coverage on transmission rate is formulated and analyzed. We calculate the basic reproducti… Show more

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Cited by 79 publications
(42 citation statements)
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References 41 publications
(66 reference statements)
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“…Research in this area is, among others, related to information flow within the networks [11], factors affecting performance of marketing campaigns [12] and the selection of highly influential nodes [13]. Campaigns based on information spreading can be targeted to increase the number of customers reached within the network [14], or alternatively can have quite the opposite goal, namely, to decrease disease transmission [15]. Models from epidemic research, such as SIR (Susceptible-Infected-Recovered) or SIS (Susceptible-Infected-Susceptible) [16], were implemented for the purposes of analyzing the spread of information with their extensions targeted to indirect and direct transmission [17] or heterogeneous networks [18].…”
Section: Introductionmentioning
confidence: 99%
“…Research in this area is, among others, related to information flow within the networks [11], factors affecting performance of marketing campaigns [12] and the selection of highly influential nodes [13]. Campaigns based on information spreading can be targeted to increase the number of customers reached within the network [14], or alternatively can have quite the opposite goal, namely, to decrease disease transmission [15]. Models from epidemic research, such as SIR (Susceptible-Infected-Recovered) or SIS (Susceptible-Infected-Susceptible) [16], were implemented for the purposes of analyzing the spread of information with their extensions targeted to indirect and direct transmission [17] or heterogeneous networks [18].…”
Section: Introductionmentioning
confidence: 99%
“…Accordingly, in models exploring malware, we can find the same compartment. Thus, proposals have been made for SIS (see, for example, ), SIR (see, for example, ), SEIR (see, for example, ), SEIRS , SEIQS , SEIQRS , SEIRS‐V , SEIS‐V , SEIQV , SIRS , SEI , SIC , SIRQ , and so on. It may be seen that there is no single compartmental model that has been addressed in a greater number of works but that there is a certain homogeneity in the compartmental models proposed.…”
Section: Mathematical Models For Simulating the Malware Propagationmentioning
confidence: 99%
“…Over the years several compartmental models have been evolved. These involve susceptibleinfected-susceptible (SIS) models [10,11], susceptible-infected-recovered (SIR) [12][13][14], susceptible-infected-recovered-susceptible (SIRS) [15,16], susceptible-exposed-infectedrecovered (SEIR) [17,18], SEIQS (quarantined class included) [19], SEIRS [20,21], SEIQRS [22], SEIRS-V (including vaccinated subpopulation) [23] models and so on.…”
Section: Introductionmentioning
confidence: 99%