Individual Altruism Cannot Overcome Congestion Effects in a Global Pandemic Game

A key challenge in responding to public health crises such as COVID-19 is the difficulty of predicting the results of feedback interconnections between the disease and society. As a step towards understanding these interconnections, we pose a simple game-theoretic model of a global pandemic in which individuals can choose where to live, and we investigate the global behavior that may emerge as a result of individuals reacting locally to the competing costs of isolation and infection. We study the game-theoretic equilibria that emerge from this setup when the population is composed of either selfish or altruistic individuals. First, we demonstrate that as is typical in these types of games, selfish equilibria are in general not optimal, but that all stable selfish equilibria are within a constant factor of optimal. Second, there exist infinitely-many stable altruistic equilibria;all but finitely-many of these are worse than the worst selfish equilibrium, and the social cost of altruistic equilibria is unbounded. Our work is in sharp contrast to recent work in network congestion games in which all altruistic equilibria are socially optimal. This suggests that a population without central coordination may react very poorly to a pandemic, and that individual altruism could even exacerbate the problem. © 2022 IEEE.

Robust Stochastic Stability in Dynamic and Reactive Environments

The theory of learning in games has extensively studied situations where agents respond dynamically to each other by optimizing a fixed utility function. However, in many settings of interest, agent utility functions themselves vary as a result of past agent choices. The ongoing COVID-19 pandemic provides an example: a highly prevalent virus may incentivize individuals to wear masks, but extensive adoption of mask-wearing reduces virus prevalence which in turn reduces individual incentives for mask-wearing. This paper develops a general framework using probabilistic coupling methods that can be used to derive the stochastically stable states of log-linear learning in certain games which feature such game-environment feedback. As a case study, we apply this framework to a simple dynamic game-theoretic model of social precautions in an epidemic and give conditions under which maximally-cautious social behavior in this model is stochastically stable.

Telerreabilitation in Multiple Sclerosis in the Pandemic Period: Brazilian Casuistics

Background: The reception and rehabilitation services for Multiple Sclerosis (MS) have been partially interrupted in most countries since the beginning of the COVID-19 pandemic. Objectives: To present the health care in neurorehabilitation promoted by the Brazilian Association of Multiple Sclerosis (ABEM) during a pandemic period by COVID-19. Methods: A Social Therapeutic Action Plan was developed to identify the needs of people with MS. Social Service call centers were made available via landline, cell phone, WhatsApp and e-mail at the beginning of the pandemic. Results: 2,975 telephone calls were made between March 23 to September 30, 2020, referrals were made and distributed as follows: art therapy = 29 (1%), physiotherapy = 2,185 (73.4%), neurology = 29 (1%), psychology = 638 (21.4%), psychiatry = 8 (0.3%), shiatsu = 86 (2.9%). Conclusions: In addition to raising awareness and encouraging people with MS not to abandon physical and mental health monitoring during the pandemic, it is extremely important to offer and provide essential services such as social and health care. The findings of this study showed the importance of the organization aimed at people with MS in the optimization and continuity of health care and access to effective interventions to promote quality of life. Diagnostic Criteria and Differential Diagnosis.