Information theory links disease population dynamics to assess the impacts of risk perception and human behavior change on influenza transmission network
Date Issued
2015
Date
2015
Author(s)
You, Shu-Han
Abstract
The global burden of contagious disease is significantly associated with human behaviors. Recently, it seems to be generally recognized that risk perception has been played a key role in changing human behaviors for preventing disease and mitigating epidemic. Therefore, it is important to be able to quantify the risk perception and behavior change impact on the infectious disease transmission network. The purposes of this study were (i) to conduct surveys to evaluate quantitatively age-specific risk perception and behavior changes before and during the seasonal influenza, (ii) to use behavior-influenza models for evaluating the interaction between the spread of risk perception and influenza transmissibility in disease population dynamics, (iii)to incorporate information theory into the behavior-influenza models and the network behavior-influenza models to predict amount of risk perception spread in the influenza transmission networks, (iv) to use a probabilistic infection risk model to predict the infection risk of influenza, and (v) to use behavior change models to assess the impacts of health behavior change on the control effectiveness. This study conducted risk perception and health behavior surveys to provide age-and region-specific risk perception with health behavior responses information before (survey I) and during (survey I) seasonal influenza, respectively. Estimated risk perception spread rate (α) was incorporated into the proposed behavior-influenza transmission network model. The behavior-influenza transmission network model was developed by linking the behavior-influenza models and the network behavior-influenza models to elucidate the impacts of risk perception and behavior change, whereas the model without information bottleneck (IB) (NM-I model) and the model with IB (NM-II model) were considered as the network behavior-influenza models. The probabilistic risk model was further used to assess the infection risk effect of risk perception with health behavior change. The perception-based transmission model was incorporated into the health behavior change model for the age-specific health behavior change assessment. The negative feedback control model combining with human influenza experimental data was used to assess efficacies of vaccination and antiviral drug. In the present study, the results from survey analyses showed that (i) participants perceived the highest self-risk level by themselves than that based on the sources of government and reference information in surveys I and II, (ii) youngers aged 10 – 19 and 25 – 34yrs induced high percentage of perception scores than that aged 20 – 24 yr, and (iii) aged 10 – 19 and ≥ 45 yrs had health behavior change of personal hygiene (HBHP), medication (HBMed), and social distancing (HBSD). The simulation results from behavior-influenza transmission network modeling showed that (i) estimated α value in survey I was lower than in survey II, (ii) amount of risk perception information spread was increased with the effective information from contact numbers of individuals in both network behavior-influenza models, and (iii) maximum mutual risk perception information (MImax) estimates based on the NM-I model were lower than in NM-II model. The results from behavior change modeling indicated that (i) HBHP and HBMed behavior changes induced lower infection risk probability, (ii) highest MImax estimates were 3.1 bits for seasonal influenza A/H1N1 with antiviral drug, 2.9 bits for A/H3N2 without any control measures, and 3.2 bits for type B with vaccination, and (iii) highest maximum mutual risk perception information change ratio occurred significantly in antiviral drug control behavior. The conclusions of this study were (i) participants perceived the highest level of self-risk probability by themselves than that based on the sources of government and reference information, (ii) youngers had significantly health behavior change patterns, (iii) the NM-II model can effectively represent as a tool to assess MImax, and (iv) the behavior change model could reflect the negative feedback efficacies of control measures in a behavior-influenza transmission system. This study could quantitatively provide an information theory-based behavior-influenza transmission network model for understanding laypeople in response to emerging infectious diseases. This stud hopes that the constructed models could provide the perspective insights and serve as a predictive way to help risk assessors in designing the control measures on influenza transmission network.
Subjects
Information theory
Disease population dynamics
Risk perception
Human behavior
Influenza
Network
SDGs
Type
thesis