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

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.

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

“…[53], Poletti et al [54] , He et al [55] , Springborn et al [56]—Mei et al . [43], Karimi et al . [45], Barrett et al .…”

“…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.…”

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

“…FCMs are used in numerous fields such as in engineering, error detection and medicine [36,37]. In this regard, Mei et al has applied FCM to describe how factors such as people's emotions and cognition functions influence each other to create epidemic infection [38]. FCM was also used by the Mago et al research to develop the required knowledge-based structure used for recognizing the specific causes and the symptoms of meningitis disease in children.…”

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

“…In 2014, Duan et al [41] studied the spreading velocity of any epidemic based on topology of weighted graph created. In 2014, Mei et al [42] studied the effect of individual behavior during any epidemic using fuzzy cognitive map study. They simulated their framework for H1N1 infection and found that infection can be controlled at low level based on decision making of individuals.…”