Interplay of epidemic spreading and vaccine uptake under complex social contagion (2412.11766v1)

Abstract: Modeling human behavior is essential to accurately predict epidemic spread, with behaviors like vaccine hesitancy complicating control efforts. While epidemic spread is often treated as a simple contagion, vaccine uptake may follow complex contagion dynamics, where individuals' decisions depend on multiple social contacts. Recently, the concept of complex contagion has received strong theoretical underpinnings thanks to the generalization of spreading phenomena from pairwise to higher-order interactions. Although several potential applications have been suggested, examples of complex contagions motivated by real data remain scarce. Surveys on COVID-19 vaccine hesitancy in the US suggest that vaccination attitudes may indeed depend on the vaccination status of social peers, aligning with complex contagion principles. In this work, we examine the interactions between epidemic spread, vaccination, and vaccine uptake attitudes under complex contagion. Using the SIR model with a dynamic, threshold-based vaccination campaign, we simulate scenarios on an age-structured multilayer network informed by US contact data. Our results offer insights into the role of social dynamics in shaping vaccination behavior and epidemic outcomes.

• The paper introduces a modified SIR model coupled with complex social contagion to analyze the interplay between epidemic spread and vaccine adoption.
• It employs both homogeneous and heterogeneous network models to reveal how initial vaccine support thresholds determine disease control outcomes.
• The study underscores the need for tailored public health strategies that integrate social dynamics with traditional epidemiological interventions.

An Analytical Perspective on the Dynamics of Epidemic Spreading and Vaccine Uptake

The paper "Interplay of Epidemic Spreading and Vaccine Uptake under Complex Social Contagion" explores the nuanced relationship between human behavior and epidemic modeling, specifically focusing on the dynamics of vaccine uptake amidst an epidemic. The authors examine this interplay using a theoretical framework integrated with empirical data on COVID-19 vaccine hesitancy in the United States. This exploration is crucial for comprehending how behavioral dynamics impact public health interventions like vaccination campaigns.

Model and Methodological Framework

The research employs a modified SIR model augmented with a vaccination campaign, tied intimately with a complex contagion model based on opinion dynamics. This framework is applied over an age-structured multilayer network generated from real-world contact patterns in the US. The incorporation of complex contagion is significant, diverging from the traditional simple contagion models by considering that an individual's decision to vaccinate is influenced by the proportion of vaccinated peers, embodying the principles of social reinforcement.

The computational experiments are executed on both homogenous and heterogeneous network models, extending the analysis to Erdős-Rényi (ER) and Barabási-Albert (BA) networks to evaluate the robustness of the findings across differing topologies.

Numerical Results and Their Implications

Under the homogeneous threshold scenario, the research delineates the parameter space where vaccination efforts succeed or fail to mitigate epidemic outbreaks. A critical insight is the dependency of epidemiological outcomes on both the initial fraction of vaccine supporters and the complexity of social contagion (measured by the threshold parameter). As vaccination rates and initial support increase, the possibility of reaching a disease-free phase becomes viable, indicating a strategic interplay between vaccination coverage and collective social behavior.

In exploring heterogeneous thresholds, informed by empirical survey data, the authors observe significant variances in epidemiological impact across different US states, attributable to diversity in vaccination attitudes. The analysis shows that initial support for vaccination (those already or intending to vaccinate as soon as possible) strongly correlates with lower disease prevalence, whereas high anti-vaccine sentiment correlates with higher prevalence under various vaccination strategies.

Theoretical and Practical Implications

Theoretically, the paper provides a framework for modeling the overlay of biological-physical contagions with behavioral-social contagions, utilizing real-world data to ground its assumptions. The work aligns with and extends recent advances in understanding how higher-order interactions in networks can influence contagion processes.

Practically, the insights gleaned here underscore the importance of considering complex social determinants in public health strategies. Effective vaccination campaigns must account not only for logistical factors but also for social dynamics and behavioral trends. The research highlights the need for tailored communication strategies and interventions that enhance vaccination coverage through social influence and peer networks.

Future Speculations

Future developments in artificial intelligence and computational social science could enhance the modeling of such complex systems by integrating more nuanced behavioral data and employing advanced network dynamics models. The potential for AI to simulate these interactions in real-time could provide more responsive public health strategies adaptable to emerging data.

In summary, this paper offers a meticulous analysis of how complex social behaviors intertwine with epidemic dynamics. Although rooted in the context of COVID-19, the findings hold broader applicability to other scenarios where social contagion significantly impacts public health interventions.