Epidemic responses under uncertainty

Vaccination equilibria interplayed with epidemics and interval reference points

Given the limitations of fixed payoffs and reference points, we introduce interval payoffs and reference points to capture fluctuations and diversity in objective costs and payoffs. We develop a prospect theory based evolutionary vaccination game model that incorporates fixed (interval) reference points to evaluate the role of psychological factors in updating vaccination strategies. The results suggest that, under a higher reference point, when vaccination costs are small, changes in the objective payoff of infected individuals have no significant effect on the vaccination equilibrium, especially when the interval objective payoff is relatively small. However, increasing vaccination costs decreases vaccination equilibrium. By analyzing the relationship between interval reference points and objective payoffs, we observe that when the objective payoff approaches the reference point, the vaccination equilibrium gradually decreases as the rationality coefficient decreases. In contrast, the vaccination equilibrium increases when the objective payoff deviates further from the reference point. In addition, we examine how different sensitivity coefficients affect individual behavior. When the gain sensitivity coefficient is small (or the loss sensitivity coefficient is high), the vaccination equilibrium is more responsive to changes in the loss (or gain) sensitivity coefficient. These findings suggest that vaccination decisions are affected by cost-effectiveness and individual sensitivity perception patterns, whether under a fixed or interval reference point.

Understanding Nash epidemics

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socioeconomic cost. Compartmentalized epidemic models have been extended to include this endogenous decision making: Individuals choose their behavior to optimize a utility function, self-consistently giving rise to population behavior. Here, we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behavior. We leverage an analytic solution that yields fully time-dependent rational population behavior to obtain, 1) a simple relationship between rational social distancing behavior and the current number of infections; 2) scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; 3) characteristic infection costs that divide regimes of strong and weak behavioral response; 4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding of the disease dynamics, useful for both individuals and policymakers. In particular, the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap," rational behavior.

A Scoping Review and Taxonomy of Epidemiological-Macroeconomic Models of COVID-19

OBJECTIVES

The COVID-19 pandemic placed significant strain on many health systems and economies. Mitigation policies decreased health impacts but had major macroeconomic impact. This article reviews models combining epidemiological and macroeconomic projections to enable policy makers to consider both macroeconomic and health objectives.

METHODS

A scoping review of epidemiological-macroeconomic models of COVID-19 was conducted, covering preprints, working articles, and journal publications. We assessed model methodologies, scope, and application to empirical data.

RESULTS

We found 80 articles modeling both the epidemiological and macroeconomic outcomes of COVID-19. Model scope is often limited to the impact of lockdown on health and total gross domestic product or aggregate consumption and to high-income countries. Just 14% of models assess disparities or poverty. Most models fall under 4 categories: compartmental-utility-maximization models, epidemiological models with stylized macroeconomic projections, epidemiological models linked to computable general equilibrium or input-output models, and epidemiological-economic agent-based models. We propose a taxonomy comparing these approaches to guide future model development.

CONCLUSIONS

The epidemiological-macroeconomic models of COVID-19 identified have varying complexity and meet different modeling needs. Priorities for future modeling include increasing developing country applications, assessing disparities and poverty, and estimating of long-run impacts. This may require better integration between epidemiologists and economists.

Accurately summarizing an outbreak using epidemiological models takes time

Recent outbreaks of Mpox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic susceptible-infectious-recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.

Modeling a traffic light warning system for acute respiratory infections

The high morbidity of acute respiratory infections constitutes a crucial global health burden. In particular, for SARS-CoV-2, non-pharmaceutical intervention geared to enforce social distancing policies, vaccination, and treatments will remain an essential part of public health policies to mitigate and control disease outbreaks. However, the implementation of mitigation measures directed to increase social distancing when the risk of contagion is a complex enterprise because of the impact of NPI on beliefs, political views, economic issues, and, in general, public perception. The way of implementing these mitigation policies studied in this work is the so-called traffic-light monitoring system that attempts to regulate the application of measures that include restrictions on mobility and the size of meetings, among other non-pharmaceutical strategies. Balanced enforcement and relaxation of measures guided through a traffic-light system that considers public risk perception and economic costs may improve the public health benefit of the policies while reducing their cost. We derive a model for the epidemiological traffic-light policies based on the best response for trigger measures driven by the risk perception of people, instantaneous reproduction number, and the prevalence of a hypothetical acute respiratory infection. With numerical experiments, we evaluate and identify the role of appreciation from a hypothetical controller that could opt for protocols aligned with the cost due to the burden of the underlying disease and the economic cost of implementing measures. As the world faces new acute respiratory outbreaks, our results provide a methodology to evaluate and develop traffic light policies resulting from a delicate balance between health benefits and economic implications.

From reductive to generative crisis: businesspeople using polysemous justifications to make sense of COVID-19

Both lay understandings of crisis moments and influential psychological models of cognition in times of uncertainty emphasize how crises limit thinking. Conversely, scholars as diverse as Foucault, Swidler, Bourdieu, and Butler have elaborated generative conceptions of crisis, which specify crises as moments of change, transformation, and heightened cognition. The research presented here takes up the question of how crises become thinkable, as actors gradually make sense of a newly uncertain context. Against a backdrop of polarization on the topic, in-depth interviews with 60 businesspeople navigating the coronavirus pandemic show that they see public health and economic well-being as interrelated. This has important effects on how businesses interpret and implement government directives and public health guidelines, from choosing to close before being mandated to do so, to staying closed even when allowed to reopen. Taken together, these findings substantiate generative models of crisis while drawing attention to the polysemous justifications elaborated by actors as they navigate shifting cultural and social scaffoldings.