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During large scale outbreaks of infectious diseases, it is imperative that media report about the potential risks. Because media reporting plays a vital role in disseminating crucial information about diseases and its associated risk, understanding how media reports could influence individuals’ behavior and its potential impact on disease transmission dynamics is important. A mathematical model within an optimal control framework of a generic disease, accounting for treatment and media reporting of disease-induced deaths is formulated. Due to the complexity of choosing the best media function, our goal is to attempt to address the following research question: what is the effect of the media-induced functional response on mitigating the spread of the disease? Connecting the functional forms to the control problem is an approach that is not very developed in the literature. Thus, this study analyses the effect of different incidence functions on disease transmission, and the qualitative nature of epidemic dynamics by carrying out optimal control analysis using three different contact rates and a media function that is dependent on the number of deaths. Theoretical analyses show that the functional forms of the effective contact rate have no effect on initial disease transmission. Time-dependent controls for treatment and vaccination with a constant effective contact rate are incorporated to determine optimal control strategies. Numerical simulations show the short-term impact of media coverage on mitigating the spread of the disease, and it is observed that with three incidence functions used, the qualitative nature of the controls remains the same. The effective contact rates are graphically shown to have a population-level effect on the disease dynamics as the number of treated and recovered individuals could be significantly different. Finally, it is shown that treatment of infectives should be at its maximum rate for a longer period compared to vaccination, while concurrent implementation of vaccination and treatment is more impactful in mitigating the spread of the disease. Thus, it is imperative that media reports and health policy decision making on infectious diseases are contextualized.
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This content will become publicly available on January 1, 2026
Behavioral Dynamics of Epidemic Trajectories and Vaccination Strategies: A Simulation-Based Analysis
Human behavior shapes epidemic trajectories, evolving as individuals reassess risks over time. Our study closes the loop between epidemic status, individual risk assessments, and interactions. We developed an agent-based model where the individuals can alter their decisions based on perceived risks. In our model, agents’ perceived risk is proxied by their full awareness of actual risks, such as the probability of infection or death. We conducted several simulations of COVID-19 spread for a large metropolitan city akin to New York City, covering the period from December 2020 to May 2021. Our model allows residents to decide daily on traveling to crowded city areas or stay in neighborhoods with relatively lower population density. Our base run simulations indicate that when individuals assess their own risk and understand how diseases spread, they adopt behaviors that slow the spread of virus, leading to fewer cumulative cases and deaths but extending the duration of the outbreak. This model was then simulated with various vaccination strategies such as random distribution, prioritizing older individuals, high-contact-rate individuals, or crowded area residents, all within a risk-response behavioral framework. Results show that, in the presence of agents’ behavioral response, there is only a marginal difference across different vaccination strategies. Specifically, vaccination in crowded areas slightly outperformed other vaccination strategies in reducing infections and prioritizing the elderly was slightly more effective in decreasing deaths. The lack of a universally superior vaccination strategy comes from the fact that lowering a risk leads to more risky behavior which partly compensates for vaccination effects. The comparable outcomes of random versus targeted vaccinations highlight the importance of equitable distribution as another key focus in pandemic responses.
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- Award ID(s):
- 2229819
- PAR ID:
- 10569859
- Publisher / Repository:
- Journal of Artificial Societies and Social Simulation
- Date Published:
- Journal Name:
- Journal of Artificial Societies and Social Simulation
- Volume:
- 28
- Issue:
- 1
- ISSN:
- 1460-7425
- Subject(s) / Keyword(s):
- Agent-Based Model SARS-CoV-2 Vaccination Policy Behavioral Modeling Epidemic Infectious Disease Dynamics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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