Epidemic spreading on modular networks: The fear to declare a pandemic

Valdez, Lucas D.; Braunstein, Lidia A.; Havlin, Shlomo

doi:10.1103/physreve.101.032309

Cited by 34 publications

(20 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analogously, network models of 'social contagions' predict the spread of beliefs or information through populations [24][25][26][27][28][29]. Studies focusing on specific aspects of social contact networks have shown that a variety of structural features-edge density, clustering coefficient and modularity-of those networks affect the progression of epidemics and information [30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

et al. 2021

View full text Add to dashboard Cite

Capturing the coupled dynamics between individual behavioural decisions that affect disease transmission and the epidemiology of outbreaks is critical to pandemic mitigation strategy. We develop a multiplex network approach to model how adherence to health-protective behaviours that impact COVID-19 spread are shaped by perceived risks and resulting community norms. We focus on three synergistic dynamics governing individual behavioural choices: (i) social construction of concern, (ii) awareness of disease incidence, and (iii) reassurance by lack of disease. We show why policies enacted early or broadly can cause communities to become reassured and therefore unwilling to maintain or adopt actions. Public health policies for which success relies on collective action should therefore exploit the behaviourally receptive phase ; the period between the generation of sufficient concern to foster adoption of novel actions and the relaxation of adherence driven by reassurance fostered by avoidance of negative outcomes over time.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Although networks with high rates of diffusion are more prone to spreading the epidemic over shorter time scales [39], diffusion does not significantly affect the prevalence of individual superspreader nodes. This analysis does not consider the presence of community structures, such as a group of nodes that are densely clustered with each other but are loosely connected to neighboring nodes [40,41]. The effect of nodal communities on superspreader risk merits further research.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Lieberthal

Gardner

2021

PLoS Comput Biol

View full text Add to dashboard Cite

Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.

show abstract

“…Analogously, network models of "social contagions" predict the spread of beliefs or information through populations (28)(29)(30)(31)(32)(33). Studies focusing on specific aspects of social contact networks have shown that a variety of structural features -edge density, clustering coefficient, modularity -of those networks affect the progression of epidemics and information (34)(35)(36)(37)(38)(39)(40).…”

Section: Introductionmentioning

confidence: 99%

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

Silk

Carrignon

Bentley

et al. 2020

Preprint

View full text Add to dashboard Cite

Decisions to adhere to health-protective behaviors (e.g. mask-wearing, social distancing, etc.) that impact the spread of COVID-19 are not made in isolation by each individual. They are instead the result of the social construction of perceived risks and resulting community norms. In populations in which disease is unlikely to spread throughout all communities simultaneously, community-driven perception of risk can drastically alter collective outcomes. A community could respond to a few infections by becoming fearful and adopting anticipatory behaviors that protect them from disease spread. Similarly, there could be false reassurance, in which low disease incidence over time leads to community consensus that protective actions are unnecessary (even if they are the reason cases remains rare). We therefore model COVID-19 spread with three synergistic dynamics governing individual behavioral choices: (1) Social construction of concern, (2) Awareness of disease incidence, and (3) Reassurance by lack of disease. We use a multiplex network approach that captures social communication and epidemiological spread. We find that effective protective policies enacted too early may backfire by allowing a community to become reassured and therefore unwilling to adopt or maintain further protective behaviors. Based on these insights, we propose that public health policies for which success relies on collective action should be designed to exploit the behaviourally receptive phase; the period between the generation of sufficient concern as to foster adoption of novel protective behaviors and the relaxation of adherence driven by reassurance fostered by avoidance of negative outcomes over time.

show abstract

Epidemic spreading on modular networks: The fear to declare a pandemic

Cited by 34 publications

References 40 publications

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

Contact Info

Product

Resources

About