The coevolution of contagion and behavior with increasing and decreasing awareness.

Understanding the effects of individual awareness on epidemic phenomena is important to comprehend the coevolving system dynamic, to improve forecasting, and to better evaluate the outcome of possible interventions. In previous models of epidemics on social networks, individual awareness has often b...

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Main Authors: Samira Maghool, Nahid Maleki-Jirsaraei, Marco Cremonini
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0225447
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spelling doaj-e8fefbb8b3a24cda8fc714ef008b9e582021-03-03T21:16:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011412e022544710.1371/journal.pone.0225447The coevolution of contagion and behavior with increasing and decreasing awareness.Samira MaghoolNahid Maleki-JirsaraeiMarco CremoniniUnderstanding the effects of individual awareness on epidemic phenomena is important to comprehend the coevolving system dynamic, to improve forecasting, and to better evaluate the outcome of possible interventions. In previous models of epidemics on social networks, individual awareness has often been approximated as a generic personal trait that depends on social reinforcement, and used to introduce variability in state transition probabilities. A novelty of this work is to assume that individual awareness is a function of several contributing factors pooled together, different by nature and dynamics, and to study it for different epidemic categories. This way, our model still has awareness as the core attribute that may change state transition probabilities. Another contribution is to study positive and negative variations of awareness, in a contagion-behavior model. Imitation is the key mechanism that we model for manipulating awareness, under different network settings and assumptions, in particular regarding the degree of intentionality that individuals may exhibit in spreading an epidemic. Three epidemic categories are considered-disease, addiction, and rumor-to discuss different imitation mechanisms and degree of intentionality. We assume a population with a heterogeneous distribution of awareness and different response mechanisms to information gathered from the network. With simulations, we show the interplay between population and awareness factors producing a distribution of state transition probabilities and analyze how different network and epidemic configurations modify transmission patterns.https://doi.org/10.1371/journal.pone.0225447
collection DOAJ
language English
format Article
sources DOAJ
author Samira Maghool
Nahid Maleki-Jirsaraei
Marco Cremonini
spellingShingle Samira Maghool
Nahid Maleki-Jirsaraei
Marco Cremonini
The coevolution of contagion and behavior with increasing and decreasing awareness.
PLoS ONE
author_facet Samira Maghool
Nahid Maleki-Jirsaraei
Marco Cremonini
author_sort Samira Maghool
title The coevolution of contagion and behavior with increasing and decreasing awareness.
title_short The coevolution of contagion and behavior with increasing and decreasing awareness.
title_full The coevolution of contagion and behavior with increasing and decreasing awareness.
title_fullStr The coevolution of contagion and behavior with increasing and decreasing awareness.
title_full_unstemmed The coevolution of contagion and behavior with increasing and decreasing awareness.
title_sort coevolution of contagion and behavior with increasing and decreasing awareness.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description Understanding the effects of individual awareness on epidemic phenomena is important to comprehend the coevolving system dynamic, to improve forecasting, and to better evaluate the outcome of possible interventions. In previous models of epidemics on social networks, individual awareness has often been approximated as a generic personal trait that depends on social reinforcement, and used to introduce variability in state transition probabilities. A novelty of this work is to assume that individual awareness is a function of several contributing factors pooled together, different by nature and dynamics, and to study it for different epidemic categories. This way, our model still has awareness as the core attribute that may change state transition probabilities. Another contribution is to study positive and negative variations of awareness, in a contagion-behavior model. Imitation is the key mechanism that we model for manipulating awareness, under different network settings and assumptions, in particular regarding the degree of intentionality that individuals may exhibit in spreading an epidemic. Three epidemic categories are considered-disease, addiction, and rumor-to discuss different imitation mechanisms and degree of intentionality. We assume a population with a heterogeneous distribution of awareness and different response mechanisms to information gathered from the network. With simulations, we show the interplay between population and awareness factors producing a distribution of state transition probabilities and analyze how different network and epidemic configurations modify transmission patterns.
url https://doi.org/10.1371/journal.pone.0225447
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