Infectious diseases and social distancing under state-dependent probabilities.

We analyze the implications of infectious diseases and social distancing in an extended SIS framework to allow for the presence of stochastic shocks with state dependent probabilities. Random shocks give rise to the diffusion of a new strain of the disease which affects both the number of infectives and the average biological characteristics of the pathogen causing the disease. The probability of such shock realizations changes with the level of disease prevalence and we analyze how the properties of the state-dependent probability function affect the long run epidemiological outcome which is characterized by an invariant probability distribution supported on a range of positive prevalence levels. We show that social distancing reduces the size of the support of the steady state distribution decreasing thus the variability of disease prevalence, but in so doing it also shifts the support rightward allowing eventually for more infectives than in an uncontrolled framework. Nevertheless, social distancing is an effective control measure since it concentrates most of the mass of the distribution toward the lower extreme of its support.

A Final Report Card on the States' Response to COVID-191

Two and a half years ago COVID-19 spread to the United States. Following the federalism model (New State Ice Co. vs Liebmann;Cheng and Lee 2019), the 50 states and their governors and legislators made many of their own pandemic policy choices to mitigate the damage from the virus. States learned from one another over time about what policies worked most and least effectively in terms of containing the virus while minimizing the negative effects of lockdown strategies on businesses and children. This study is an expanded and updated version of an October 2020 report card of how pandemic health, economy, and policy varied across the 50 states and the District of Columbia (Committee to Unleash Prosperity 2020). It examines three variables: health outcomes, economic performance throughout the pandemic, and impact on education. We find no relationship between reduced economic activity during the pandemic and mortality. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

The impact of corona populism: Empirical evidence from Austria and theory.

I study the co-evolution between public opinion and party policy in situations of crises by investigating a policy U-turn of a major Austrian right-wing party (FPÖ) during the Covid-19 pandemic. My analysis suggests the existence of both i) a "Downsian" effect, which causes voters to adapt their party preferences based on policy congruence and ii) a "party identification" effect, which causes partisans to realign their policy preferences based on "their" party's platform. Specifically, I use individual-level panel data to show that i) "corona skeptical" voters who did not vote for the FPÖ in the pre-Covid-19 elections of 2019 were more likely to vote for the party after it embraced "corona populism", and ii) beliefs of respondents who declared that they voted for the FPÖ in 2019 diverged from the rest of the population in three out of four health-related dimensions only after the turn, causing them to underestimate the threat posed by Covid-19 compared to the rest of the population. Using aggregate-level panel data, I study whether the turn has produced significant behavioral differences which could be observed in terms of reported cases and deaths per capita. Paradoxically, after the turn the FPÖ vote share is significantly positively correlated with deaths per capita, but not with the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing, which causes a correlation between the number of "corona skeptics" and the share of unreported cases after the turn. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study that involves information about participants' true vs. reported infection status. I finally study a simple heterogeneous mixing epidemiological model and show that a testing bias can indeed explain the apparent paradox of an increase in deaths without an increase in reported cases. My results can, among others, be used to enrich formal analyses regarding the co-evolution between voter and party behavior.

Welfare economics of managing an epidemic: an exposition.

This paper reviews recent findings on the normative analysis of private and governmental countermeasures against infectious diseases, focusing on COVID-19. Based on a model that relates the economic activity to infectious disease epidemics, policies that maximize social welfare are considered. Lockdowns in many countries are measures that restrict economic activity over a wide area, and the economic damage they cause is extremely large. Existing studies on the net benefit of lockdown implemented in 2020 have reached mixed conclusions as to whether it is warranted or not. Although the estimates of costs and effects are relatively stable, the setting of the value of a statistical life for converting effects into benefits has a wide range and is also likely to overestimate benefits. Therefore, a careful procedure for setting is particularly crucial to obtain a reliable evaluation of countermeasures. Compared to uniform restriction of activities, taking measures to restrict activities by selecting targets may improve efficiency. Attributes that can be used to select targets include those that can be identified at little or no cost, such as age and industry, and those that can only be identified at a cost, such as close contact with infectious individuals and the presence of pathogens. In comparison to lockdown, these measures may reduce human suffering and economic suffering. No trade-off exists between uniform activity restrictions and selective activity restrictions.

On the management of population immunity.

This paper considers a susceptible-infected-recovered type model of infectious diseases, such as COVID-19 or swine flu, in which costly treatment or vaccination confers immunity on recovered individuals. Once immune, individuals indirectly protect the remaining susceptibles, who benefit from a measure of herd immunity. Treatment and vaccination directly induce such herd immunity, which builds up over time. Optimal treatment is shown to involve intervention at early stages of the epidemic, while optimal vaccination may defer intervention to intermediate stages. Thus, while treatment and vaccination have superficial similarities, their effects and desirability at different stages of the epidemic are different. Equilibrium vaccination is qualitatively similar to socially optimal vaccination, while equilibrium treatment differs in nature from socially optimal treatment. The optimal policies are compared to traditional non-economic public health interventions which rely on herd immunity thresholds.

Do disease prevalence and severity drive COVID-19 vaccine demand?

Large scale vaccination of population is widely accepted to be the key to recovery from the devastating economic and public health impacts of the COVID-19 pandemic. However, low uptake of vaccine has challenged vaccination efforts in many parts of the world. The paper explores the determinants of demand for COVID-19 vaccination - specifically, the prevalence dependence hypothesis - that identifies infection prevalence and mortality as the key drivers of individual preventive behavior against infectious diseases. Using daily disease tracking and vaccination data from 47 European countries the paper finds strong evidence that COVID-19 infection rate and mortality rate drive future vaccination uptake. Specifically, results from fixed effects models suggest that while lagged infection prevalence induce vaccination uptake by 0.18 to 0.24 percent, while the effect of lagged mortality is significantly larger, ranging between 1.10 to 1.53 percent. The results highlight the critical role of behavioral response to epidemiological outcomes and are of critical significance for COVID-19 mitigation policies, especially as they relate to achieving vaccine-induced herd immunity and economic reopening.

Effectiveness of social distancing interventions in containing COVID-19 incidence: International evidence using Kalman filter.

The epidemiological literature has widely documented the importance of social distancing interventions in containing the spread of the COVID-19 pandemic. However, the epidemiological measure of virus reproduction, R0, provides a myopic view of containment, especially when the absolute number of cases is still high. The paper investigates cross-country variations concerning the impact of social distancing interventions on COVID-19 incidence by employing a statistical measure of containment, which models the daily number of cases as a structural time-series, state-space vector. Countries that adopt strict lockdown policies and provide economic support in the form of income augmentations and debt relief improve the response towards the pandemic. Countries like China and South Korea have been most influential in containing the spread of infections. European nations of France, Italy, Spain and the UK are witnessing a second wave of the virus, indicating that re-opening the European economy perhaps has instigated an exponential spread.

Anticipation of COVID-19 vaccines reduces willingness to socially distance.

We investigate how the anticipation of COVID-19 vaccines affects voluntary social distancing. In a large-scale preregistered survey experiment with a representative sample, we study whether providing information about the safety, effectiveness, and availability of COVID-19 vaccines affects the willingness to comply with public health guidelines. We find that vaccine information reduces peoples' voluntary social distancing, adherence to hygiene guidelines, and their willingness to stay at home. Getting positive information on COVID-19 vaccines induces people to believe in a swifter return to normal life. The results indicate an important behavioral drawback of successful vaccine development: An increased focus on vaccines can lower compliance with public health guidelines and accelerate the spread of infectious disease. The results imply that, as vaccinations roll out and the end of a pandemic feels closer, policies aimed at increasing social distancing will be less effective, and stricter policies might be required.

The economic value of quarantine is higher at lower case prevalence, with quarantine justified at lower risk of infection.

Some infectious diseases, such as COVID-19 or the influenza pandemic of 1918, are so harmful that they justify broad-scale social distancing. Targeted quarantine can reduce the amount of indiscriminate social distancing needed to control transmission. Finding the optimal balance between targeted versus broad-scale policies can be operationalized by minimizing the total amount of social isolation needed to achieve a target reproductive number. Optimality is achieved by quarantining on the basis of a risk threshold that depends strongly on current disease prevalence, suggesting that very different disease control policies should be used at different times or places. Aggressive quarantine is warranted given low disease prevalence, while populations with a higher base rate of infection should rely more on social distancing by all. The total value of a quarantine policy rises as case counts fall, is relatively insensitive to vaccination unless the vaccinated are exempt from distancing policies, and is substantially increased by the availability of modestly more information about individual risk of infectiousness.