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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. 

Abstract In nuclear matter in isolated neutron stars, the flavor content (e.g., proton fraction) is subject to weak interactions, establishing flavor (β-)equilibrium. However, there can be deviations from this equilibrium during the merger of two neutron stars. We study the resulting out-of-equilibrium dynamics during the collision by incorporating direct and modified Urca processes (in the neutrino-transparent regime) into general-relativistic hydrodynamics simulations with a simplified neutrino transport scheme. We demonstrate how weak-interaction-driven bulk viscosity in postmerger simulations can emerge and assess the bulk viscous dynamics of the resulting flow. We further place limits on the impact of the postmerger gravitational-wave strain. Our results show that weak-interaction-driven bulk viscosity can potentially lead to a phase shift of the postmerger gravitational-wave spectrum, although the effect is currently on the same level as the numerical errors of our simulation. 

Abstract This review aims at providing an extensive discussion of modern constraints relevant for dense and hot strongly interacting matter. It includes theoretical first-principle results from lattice and perturbative QCD, as well as chiral effective field theory results. From the experimental side, it includes heavy-ion collision and low-energy nuclear physics results, as well as observations from neutron stars and their mergers. The validity of different constraints, concerning specific conditions and ranges of applicability, is also provided.