Effect of individual protective behaviors on influenza transmission: an agent-based model

Abstract

It is well established in the epidemiological literature that individual behaviors have a significant effect on the spread of infectious diseases. Agent-based models are increasingly being recognized as the next generation of epidemiological models. In this research, we use the ability of agent-based models to incorporate behavior into simulations by examining the relative importance of vaccination and social distancing, two common measures for controlling the spread of infectious diseases, with respect to seasonal influenza. We modeled health behaviour using the result of a Health Belief Model study focused on influenza. We considered a control and a treatment group to explore the effect of education on people's health-related behaviors patterns. The control group reflects the behavioral patterns of students based on their general knowledge of influenza and its interventions while the treatment group illustrates the level of behavioral changes after individuals have been educated by a health care expert. The results of this study indicate that self-initiated behaviors are successful in controlling an outbreak in a high contact rate location such as a university. Self-initiated behaviors resulted in a population attack rate decrease of 17% and a 25% reduction in the peak number of cases. The simulation also provides significant evidence for the effect of an HBM theory-based educational program to increase the rate of applying the target interventions (vaccination by 22% percent and social distancing by 41%) and consequently to control the outbreak.

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

“…[43], Zhang et al . [44], Karimi et al [45]—Social DistancingPoletti et al . [46], Mummert & Weiss [47], Zhong et al [48]Larson & Nigmatulina [49], Wang [50], Greer [51], Morin et al .…”

“…We retrieved 16 papers [14,45,51,56,64,72,73,83–85,97,98,103,111,170,187] describing BCMs in which there is no two-way interaction with a disease transmission model. Morin et al .…”

“…Karimi et al . also use the HBM for their ABM on influenza in 2015 [45]. For validation, the authors compare their model output with similar influenza ABMs in the literature.…”

“…[56] used television viewing habits (average viewing time) as a proxy for social distancing, although this proxy is far removed from a direct estimation of social distancing in an outbreak situation. More promising sources of information include: survey data using, for instance, the HBM framework (also see [191,195,196]) or time-use surveys [3,14,23,24,45,48,60,72,85,127,183] or digital sources such as social media [25,60,66,146,197]. Real-life data collection during the influenza A/H1N1 pandemic in 2009 has been a milestone for the parametrization of BCMs with increased collection of both behaviour and disease-related information.…”

An application of the Continuous Opinions and Discrete Actions (CODA) model to adolescent smoking initiation

“…ABM can incorporate various individual and environmental characteristics into the model to account for population heterogeneity. For example, Karimi et al used ABM to study the effect of individual behaviors on flu transmission [ 16 ]. In addition, ABM takes social interactions and the corresponding impact on individuals into consideration.…”