Impact of network assortativity on epidemic and vaccination behaviour

The resurgence of measles is largely attributed to the decline in vaccine adoption and the increase in mobility. Although the vaccine for measles is readily available and highly successful, its current adoption is not adequate to prevent epidemics. Vaccine adoption is directly affected by individual vaccination decisions, and has a complex interplay with the spatial spread of disease shaped by an underlying mobility (travelling) network. In this paper, we model the travelling connectivity as a scale-free network, and investigate dependencies between the network's assortativity and the resultant epidemic and vaccination dynamics. In doing so we extend an SIR-network model with game-theoretic components, capturing the imitation dynamics under a voluntary vaccination scheme. Our results show a correlation between the epidemic dynamics and the network's assortativity, highlighting that networks with high assortativity tend to suppress epidemics under certain conditions. In highly assortative networks, the suppression is sustained producing an early convergence to equilibrium. In highly disassortative networks, however, the suppression effect diminishes over time due to scattering of non-vaccinating nodes, and frequent switching between the predominantly vaccinating and non-vaccinating phases of the dynamics.Comment: 17 pages, 13 figure

A Polarized Temporal Network Model to Study the Spread of Recurrent Epidemic Diseases in a Partially Vaccinated Population

Motivated by massive outbreaks of COVID-19 that occurred even in populations with high vaccine uptake, we propose a novel multi-population temporal network model for the spread of recurrent epidemic diseases. We study the effect of human behavior, testing, and vaccination campaigns on the control of local outbreaks and infection prevalence. Our modeling framework decouples the vaccine effectiveness in protecting against transmission and the development of severe symptoms. Furthermore, the framework accounts for the polarizing effect of the decision to vaccinate and captures homophily, i.e., the tendency of people to interact with like-minded individuals. By means of a mean-field approach, we analytically derive the epidemic threshold. Our theoretical results suggest that, while vaccination campaigns reduce pressure on hospitals, they might facilitate resurgent outbreaks, highlighting the key role that testing campaigns may have in eradicating the disease. Numerical simulations are then employed to confirm and extend our theoretical findings to more realistic scenarios. Our numerical and analytical results agree that vaccination is not sufficient to achieve full eradication, without employing massive testing campaigns or relying on the population's responsibility. Furthermore, we show that homophily plays a critical role in the control of local outbreaks, highlighting the peril of a polarized network structure.Comment: Submitted to IEEE Transactions on Network Science and Engineerin

Coupled Evolutionary Behavioral and Disease Dynamics under Reinfection Risk

We study the interplay between epidemic dynamics and human decision making for epidemics that involve reinfection risk; in particular, the susceptible-infected-susceptible (SIS) and the susceptible-infected-recovered-infected (SIRI) epidemic models. In the proposed game-theoretic setting, individuals choose whether to adopt protection or not based on the trade-off between the cost of adopting protection and the risk of infection; the latter depends on the current prevalence of the epidemic and the fraction of individuals who adopt protection in the entire population. We define the coupled epidemic-behavioral dynamics by modeling the evolution of individual protection adoption behavior according to the replicator dynamics. For the SIS epidemic, we fully characterize the equilibria and their stability properties. We further analyze the coupled dynamics under timescale separation when individual behavior evolves faster than the epidemic, and characterize the equilibria of the resulting discontinuous hybrid dynamical system for both SIS and SIRI models. Numerical results illustrate how the coupled dynamics exhibits oscillatory behavior and convergence to sliding mode solutions under suitable parameter regimes.Comment: arXiv admin note: text overlap with arXiv:2203.1027

Modeling Coupled Disease-Behavior Dynamics of SARS-CoV-2 Using Influence Networks

SARS-CoV-2, the virus that causes COVID-19, has caused significant human morbidity and mortality since its emergence in late 2019. Not only have over three million people died, but humans have been forced to change their behavior in a variety of ways, including limiting their contacts, social distancing, and wearing masks. Early infectious disease models, like the classical SIR model by Kermack and McKendrick, do not account for differing contact structures and behavior. More recent work has demonstrated that contact structures and behavior can considerably impact disease dynamics. We construct a coupled disease-behavior dynamical model for SARS-CoV-2 by incorporating heterogeneous contact structures and decisions about masking. We use a contact network with household, work, and friend interactions to capture the variation in contact patterns. We allow decisions about masking to occur at a different time scale from disease spread which dramatically changes the masking dynamics. Drawing from the field of game theory, we construct an individual decision-making process that relies on perceived risk of infection, social influence, and individual resistance to masking. Through simulation, we find that social influence prevents masking, while perceived risk largely drives individuals to mask. Underlying contact structure also affects the number of people who mask. This model serves as a starting point for future work which could explore the relative importance of social influence and perceived risk in human decision-making