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Modeling information feedback during H1N1 outbreak using stochastic agent‐based models
Author(s) -
Loganathan P.,
Sundaramoorthy S.,
Lakshminarayanan S.
Publication year - 2011
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.571
Subject(s) - pandemic , intervention (counseling) , outbreak , psychological intervention , computer science , transmission (telecommunications) , information flow , operations research , risk analysis (engineering) , disease , disease transmission , epidemic model , econometrics , covid-19 , environmental health , medicine , economics , engineering , infectious disease (medical specialty) , virology , telecommunications , population , linguistics , philosophy , pathology , psychiatry
Influenza pandemics have struck thrice in the twentieth century and around 50 million global deaths have occurred due to these pandemics. Traditional methods of modeling intervention planning do not consider people's response in taking the vaccine with respect to the evolving characteristic of an epidemic. Intervention policies derived neglecting such feedback effects can be misleading in gauging the effectiveness of the recommended control strategies. To address this issue, we have developed a stochastic agent‐based model by including the information transmission feedback [through word of mouth (WOM)] for vaccine intake. The information flow among the agents regarding vaccine intake was modeled using diffusion theory. The model incorporates parameters from very recent H1N1 2009‐related studies. The developed model was then used to analyze how WOM influence is exerted into personal networks (at the micro level) and how it affects the macro evolution of the disease for different scenarios. Our results demonstrate that the disease progression with the inclusion of information transmission is very different from that predicted by models that consider only conventional phenomena. With further improvements, this model can be used to determine the optimal interventions that can form the basis of a public health systems' response to mitigate a future outbreak. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.