Risk perception in epidemic modeling

Abstract

We investigate the effects of risk perception in a simple model of epidemic spreading. We assume that the perception of the risk of being infected depends on the fraction of neighbors that are ill. The effect of this factor is to decrease the infectivity, that therefore becomes a dynamical component of the model. We study the problem in the mean-field approximation and by numerical simulations for regular, random and scale-free networks. We show that for homogeneous and random networks, there is always a value of perception that stops the epidemics. In the "worst-case" scenario of a scale-free network with diverging input connectivity, a linear perception cannot stop the epidemics; however we show that a non-linear increase of the perception risk may lead to the extinction of the disease. This transition is discontinuous, and is not predicted by the mean-field analysis.

Efficiency of prompt quarantine measures on a susceptible-infected-removed model in networks

“…However, the basic reproduction number (and thus the epidemic threshold) remained equal to those of random preventive vaccination, and this failed to contain epidemics in a highly heterogeneous network unless almost all individuals were vaccinated. There are also studies investigating dynamic reactions of individuals to the spread of epidemics [20][21][22][23][24][25], such as behavioral responses of individuals by reducing their contact rates [25], based on the number of infected neighbors or by rewiring connections (i.e., disconnecting their connections to infected neighbors and reconnecting others) [21].…”

Spontaneous behavioural changes in response to epidemics

“…Spontaneous change of behaviour in response to epidemics (Ferguson, 2007), possibly related to risk perception (Bagnoli et al, 2007;Risau-Gusman and Zanette, 2008;Shaw and Schwartz, 2008), has been recently proposed as a relevant factor in the comprehension of infection dynamics. While the merits and influence of such phenomena are still debated (D'Onofrio et al, 2007;Moneim, 2007), experience from the 1918-19 pandemic indicates that a better understanding of behavioural patterns is crucial to improve model realism and enhance the effectiveness of containment/mitigation policies (Bootsma and Ferguson, 2007).…”

Suppression of epidemic spreading in complex networks by local information based behavioral responses

“…First, a general result from previous works is that the local information-based responses can enhance the epidemic threshold and reduce its prevalence, but global informationbased awareness, although being capable of altering the epidemic size, has little effect on the threshold. 6,24,25 In these works on the interplay between epidemic spreading in complex networks and human behavioral responses, a tacit hypothesis [24][25][26]28 is that local information-based behavioral response is a function of the density of infection among the local neighborhood, denoted as s/k, where s is the number of infected neighbors among a total of k neighbors. However, simple situations can be conceived where this assumption does not hold.…”

“…[24][25][26][27] Quantitatively, the impact of different types of information-based awareness can be characterized by how they modify the epidemic threshold and the final epidemic size (or epidemic prevalence). For example, Bagnoli et al 26 assumed that individuals' risk perception of epidemic is an exponential function of local and global information, and they showed that a nonlinear increase in the perception risk can lead to extinction of the disease. Funk et al 17 studied the impacts of awareness spread on both epidemic threshold and prevalence and found that, in a well-mixed population, spread of awareness can reduce the outbreak size but does not tend to affect the epidemic threshold.…”