Individual Decision Making Can Drive Epidemics: A Fuzzy Cognitive Map Study

Authored by Zhenghu Zu, Shan Mei, Yifan Zhu, Xiaogang Qiu, Xuan Zhou, A. V. Boukhanovsky, P. M. A. Sloot

Date Published: 2014-04

DOI: 10.1109/tfuzz.2013.2251638

Sponsors: European Union National Science Foundation of China

Platforms: No platforms listed

Model Documentation: Other Narrative Pseudocode Mathematical description

Model Code URLs: Model code not found

Abstract

Existing studies on the propagation of infectious diseases have not sufficiently considered the uncertainties that are related to individual behavior and its influence on individual decision making to prevent infections, even though it is well known that changes in behavior can lead to variations in the macrodynamics of the spread of infectious diseases. These influencing factors can be categorized into emotion-related and cognition-related components. We present a fuzzy cognitive map (FCM) denotative model to describe how the factors of individual emotions and cognition influence each other. We adjust the weight matrix of causal relationships between these factors by using a so-called nonlinear Hebbian learning method. Based on this FCM model, we can implement individual decision rules against possible infections for disease propagation studies. We take the simulation of influenza A [H1N1] spreading on a campus as an example. We find that individual decision making against infections (frequent washing, respirator usage, and crowd contact avoidance) can significantly decrease the at-peak number of infected patients, even when common policies, such as isolation and vaccination, are not deployed.

Tags

Infectious diseases Complex networks Agent-based modeling Fuzzy Cognitive Maps (FCMs) influenza A [H1N1] unsupervised learning