ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
Introduction: High flow nasal oxygen (HFNO) and continuous positive airway pressure (CPAP) therapy are recognised treatments for hypoxia which were widely used throughout the COVID-19 pandemic. Large scale studies such as RECOVERY-RS [1] compared HFNO and CPAP to conventional oxygen therapy in patients suitable for mechanical ventilation. TSDFT had capacity to offer ward based HFNO/CPAP to patients deemed both suitable and not suitable for mechanical ventilation. We set out to review the outcomes of all patients who received HFNO/ CPAP for COVID-19 pneumonitis at our trust. Methods: A retrospective observational study of all patients with COVID-19 Pneumonitis who received CPAP/HFNO was conducted at a district general hospital in South West England. Electronic records and ICD10 diagnostic codes were reviewed between September 2020 and October 2021. Results: 90 patients received HFNO or CPAP. The median age was 68 years. 50 (55%) survived to hospital discharge. Survival to hospital discharge was greater in females (71%) than males (42%). Survival decreased from 100% in the 21-30 years age group, to 33.3% in the > 70 years age group. On review of co-morbidities the overall survival rate was similar, except for patients with cardiac failure or valvular disease, of which only 4 of 19 patients survived (21%) All patients under 40 years survived to hospital discharge. There was no relationship between number of days of therapy and survival to discharge. Conclusions: Among this cohort, survival to hospital discharge after HFNO or CPAP for COVID-19 Pneumonitis was greater in younger patients, females and those without cardiovascular failure.