Modeling information feedback during H1N1 outbreak using stochastic agent‐based models

Abstract

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. 

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

“…The largest category embodies 76 references managing information as a dynamic parameter [3,25,34,40,43,47–50,53,55,60,61,65–68,70,71,74,77,80,82,86,92,94–96,99,100,102,104–109,113,115–117,119–126,128–131,133,134,137,148,149,153,157,161,165,166,168,171–178,184–186,188,189]. In this category, instead of a threshold, the information is a continuous input in the decision-making process of individuals.…”

Trends in parameterization, economics and host behaviour in influenza pandemic modelling: a review and reporting protocol

“…In our review, the inclusion of individuals' behaviour was more common among simulation models although, instead of basing behaviour representation on game or microeconomic theory, it was usually based on simple rules and assumptions. Different kinds of behaviours were considered in several models, including voluntary isolation, increased social distancing once infected, and preventive behaviour [33,42,66,74,79,[82][83][84][85]. The inclusion of behaviour can lead to substantially different conclusions.…”

Information Exposure, Consumption, Lifestyle, and Inclination Towards Behaviour Change During the Covid-19 Pandemic

“…This is because the critical role of person-to-person transmission in the spread of outbreaks (e.g., respiratory infections, Ebola) are equally important with the behavioural compliance in the success of multiple infection control interventions (i.e., individuals need to consent to vaccination, or adhere to quarantine restrictions). During a pandemic, people need to modify their behaviour based on the information they receive from the authority; however how the people adhere to that order very much depends on the amount, type of information received, how members of the public consumed them, as well as the demographic profile of the individuals (Durham, Casman, 2011., & Loganathan, Sundaramoorthy, Lakshminarayanan, 2011.…”