The Influence of Risk Perception in Epidemics: A Cellular Agent Model

Abstract

Our work stems from the consideration that the spreading of a disease is modulated by the individual's perception of the infected neighborhood and his/her strategy to avoid being infected as well. We introduced a general "cellular agent" model that accounts for a hetereogeneous and variable network of connections. The probability of infection is assumed to depend on the perception that an individual has about the spreading of the disease in her local neighborhood and on broadcasting media. In the one-dimensional homogeneous case the model reduces to the DK one, while for long-range coupling the dynamics exhibits large fluctuations that may lead to the complete extinction of the disease. ⋆

Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it

“…To model this process, we defined two functions to describe an individual's cognition to the epidemic and where H ( i , t ) represents the cognition of individual i on day t due to external warning, which is related to daily illness attack rate R ( t ) reported by media broadcasts 17 18 and local temperature T ( t ) 19 . J ( i , t ) stands for the cognition of individual i on day t due to self-awareness, which is related to his or her social network degree D ( i , t ) and the age A ( i ).…”

“…In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate reported by media broadcasts 17 18 , climate 19 , and other environment factors. The association between human cognitive behavior and virus transmission has been investigated through cognitive behavioral theories 20 21 22 , showing highly daily virus attack rate evokes people's precaution to the disease.…”

Risk perception in epidemic modeling

“…The effect of the introduction of risk perception is evident: for high concentrations of infected individuals the probability of being infected is diminished. Therefore, while for J = 0 and z > 1 there is only one stable fixed point c = 1 (all individuals infected), by increasing J one can have stable fixed points c < 1, limit cycles and even chaotic behavior [28].…”

“…Moreover, we study the case of decreasing infection rate with increasing local infection level, that might also induce chaotic oscillations at the meanfield level (See Ref. [28] and Section II). However, one should consider that chaotic oscillations on networks easily desynchronize, and the resulting "microscopic chaos" is quite different from the synchronous oscillations predicted by mean-field analysis [29], that may nevertheless be observed in lattice models the presence of long-range coupling [30].…”

Community Structure in Social Networks: Applications for Epidemiological Modelling

“…We define an individual's risk perception as awareness of the disease based on which he acts to reduce the probability of becoming infected. We model this perception using the framework introduced by Bagnoli, Liò and Sguanci [26] , [27] . In this framework, as a result of alertness to the disease, the probability of an individual becoming infected τ is multiplied by a factor of where s is the number of the individual's infected connections and k is the connectivity (or degree) of the individual.…”