Modeling the Co-evolution of Climate Impact and Population Behavior: A Mean-Field Analysis

Motivated by the climate crisis that is currently ravaging the planet, we propose and analyze a novel framework for the evolution of anthropogenic climate impact in which the evolution of human environmental behavior and environmental impact is coupled. Our framework includes a human decision-making process that captures social influence, government policy interventions, and the cost of environmentally-friendly behavior, modeled within a game-theoretic paradigm. By taking a mean-field approach in the limit of large populations, we derive the equilibria and their local stability characteristics. Subsequently, we study global convergence, whereby we show that the system converges to a periodic solution for almost all initial conditions. Numerical simulations confirm our findings and suggest that, before the system reaches such a periodic solution, the level of environmental impact might become dangerously high, calling for the design of optimal control strategies to influence the system trajectory.Comment: Submitted to the 22nd World Congress of the International Federation of Automatic Control (IFAC 2023

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

A novel framework for community modeling and characterization in directed temporal networks

Abstract We deal with the problem of modeling and characterizing the community structure of complex systems. First, we propose a mathematical model for directed temporal networks based on the paradigm of activity driven networks. Many features of real-world systems are encapsulated in our model, such as hierarchical and overlapping community structures, heterogeneous attitude of nodes in behaving as sources or drains for connections, and the existence of a backbone of links that model dyadic relationships between nodes. Second, we develop a method for parameter identification of temporal networks based on the analysis of the integrated network of connections. Starting from any existing community detection algorithm, our method enriches the obtained solution by providing an in-depth characterization of the very nature of the role of nodes and communities in generating the temporal link structure. The proposed modeling and characterization framework is validated on three synthetic benchmarks and two real-world case studies

A multi-layer network model to assess school opening policies during a vaccination campaign:a case study on COVID-19 in France

We propose a multi-layer network model for the spread of an infectious disease that accounts for interactions within the family, between children in classes and schools, and casual contacts in the population. The proposed framework is designed to test several what-if scenarios on school openings during the vaccination campaigns, thereby assessing the safety of different policies, including testing practices in schools, diverse home-isolation policies, and targeted vaccination. We demonstrate the potentialities of our model by calibrating it on epidemiological and demographic data of the spring 2021 COVID-19 vaccination campaign in France. Specifically, we consider scenarios in which a fraction of the population is vaccinated, and we focus our analysis on the role of schools as drivers of the contagions and on the implementation of targeted intervention policies oriented to children and their families. We perform our analysis by means of a campaign of Monte Carlo simulations. Our findings suggest that transmission in schools may play a key role in the spreading of a disease. Interestingly, we show that children’s testing might be an important tool to flatten the epidemic curve, in particular when combined with enacting temporary online education for classes in which infected students are detected. Finally, we test a vaccination strategy that prioritizes the members of large families and we demonstrate its good performance. We believe that our modeling framework and our findings could be of help for public health authorities for planning their current and future interventions, as well as to increase preparedness for future epidemic outbreaks

A time-varying network model for sexually transmitted infections accounting for behavior and control actions

In this article, we propose a stochastic network model for the spread of common sexually transmitted infections (STIs). The model expands the standard susceptible–infected–susceptible model by incorporating asymptomatic infected individuals—who are unaware that they are posing a health threat to themselves and the population—and individuals' behavioral preferences with regard to the use of protective measures during encounters. Behavioral preferences evolve according to a nontrivial mechanism accounting for the cost of using protection, the perceived risk, and persuasive effects due to sexual encounters with different-minded individuals. The disease spreads on a time-varying network of sexual contacts, generated using a mechanism inspired by continuous-time activity-driven networks. Such a network accounts for regular partners and casual encounters, which are regulated by a negotiation process that accounts for the individuals' behavioral preferences. Finally, three control measures are included in the model: (i) condom (social) marketing campaigns, (ii) routine screening at STI clinics, and (iii) partner notification. We use a mean-field approach to analytically derive the epidemic threshold in the limit of large-scale populations, in the absence of a partner network, for two distinct negotiation processes. Our results indicate that routine screening is key to the eradication of local outbreaks, while condom marketing campaigns become effective only when combined with screening. Monte Carlo simulations are then employed to extend our analytical findings, casting lights on the role of the partner network and on partner notification as a control measure in the spread of STIs.</p

A time-varying network model for sexually transmitted infections accounting for behavior and control actions

In this article, we propose a stochastic network model for the spread of common sexually transmitted infections (STIs). The model expands the standard susceptible–infected–susceptible model by incorporating asymptomatic infected individuals—who are unaware that they are posing a health threat to themselves and the population—and individuals' behavioral preferences with regard to the use of protective measures during encounters. Behavioral preferences evolve according to a nontrivial mechanism accounting for the cost of using protection, the perceived risk, and persuasive effects due to sexual encounters with different-minded individuals. The disease spreads on a time-varying network of sexual contacts, generated using a mechanism inspired by continuous-time activity-driven networks. Such a network accounts for regular partners and casual encounters, which are regulated by a negotiation process that accounts for the individuals' behavioral preferences. Finally, three control measures are included in the model: (i) condom (social) marketing campaigns, (ii) routine screening at STI clinics, and (iii) partner notification. We use a mean-field approach to analytically derive the epidemic threshold in the limit of large-scale populations, in the absence of a partner network, for two distinct negotiation processes. Our results indicate that routine screening is key to the eradication of local outbreaks, while condom marketing campaigns become effective only when combined with screening. Monte Carlo simulations are then employed to extend our analytical findings, casting lights on the role of the partner network and on partner notification as a control measure in the spread of STIs