Stochastic disease spreading and containment policies under state-dependent probabilities.

We analyze the role of disease containment policy in the form of treatment in a stochastic economic-epidemiological framework in which the probability of the occurrence of random shocks is state-dependent, namely it is related to the level of disease prevalence. Random shocks are associated with the diffusion of a new strain of the disease which affects both the number of infectives and the growth rate of infection, and the probability of such shocks realization may be either increasing or decreasing in the number of infectives. We determine the optimal policy and the steady state of such a stochastic framework, which is characterized by an invariant measure supported on strictly positive prevalence levels, suggesting that complete eradication is never a possible long run outcome where instead endemicity will prevail. Our results show that: (i) independently of the features of the state-dependent probabilities, treatment allows to shift leftward the support of the invariant measure; and (ii) the features of the state-dependent probabilities affect the shape and spread of the distribution of disease prevalence over its support, allowing for a steady state outcome characterized by a distribution alternatively highly concentrated over low prevalence levels or more spread out over a larger range of prevalence (possibly higher) levels.

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.

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.

It's worth a shot: urban density, endogenous vaccination decisions, and dynamics of infectious disease.

We develop an agent-based model of vaccine decisions across a heterogeneous network model with urban and rural regions. In the model, agents make rational decisions to vaccinate or not, based on the relative private costs of vaccinations and infections as well as an estimated probability of infection if not vaccinated. The model is a methodological advance in that it provides an economic rationale for traditional threshold models of vaccine decision-making that are commonly used in agent-based network models of vaccine choice. In the model, more dense urban regions have more connections between agents than less dense rural regions. Higher density leads to higher levels of vaccine usage and lower rates of infection in urban regions within the model. This finding adds to the more commonly discussed socio-economic reasons for higher levels of vaccination usage in urban areas compared to rural areas. In addition to this direct contribution, the paper emphasizes the importance of endogenous decision-making in models of epidemiology. For instance, we find that networks that lead to larger epidemics in exogenous vaccination models lead to smaller epidemics in our model because agents use vaccinations to offset the additional risk introduced by these network structures. Endogenous agent responses to risk need to be incorporated into theoretical and empirical models of economic epidemiology.

Rather doomed than uncertain: risk attitudes and transmissive behavior under asymptomatic infection.

We analyze the relation between individuals' risk aversion and their willingness to expose themselves to infection when faced with an asymptomatic infectious disease. We show that in a high prevalence environment, increasing individuals' risk aversion increases their propensity to engage in transmissive behavior. The reason for this result is that as risk aversion increases, exposure which leads to infection with certainty becomes relatively more attractive than the uncertain payoffs from protected behavior. We provide evidence from a laboratory experiment which is consistent with our theoretical findings.

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.

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.

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.

Behavioral responses to the 2015 MERS epidemic in Korea.

Understanding behavioral responses to epidemics is important in evaluating the broad health consequences of emerging infectious diseases. Building on the economic epidemiology literature, this study investigates individual behavioral responses to the 2015 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) epidemic in Korea using a panel of individuals in a nationally representative survey. Results show that exposure to the epidemic led to lasting impacts on smoking and drinking behaviors, indicating that emerging infectious disease outbreaks are motivations for behavioral changes and opportunities for public policy interventions. In particular, individuals in the hardest-hit regions or socially connected persons were more likely to change their risky behaviors, suggesting that intensity of exposure and social interactions are potential mechanisms.

Cost-effectiveness of combining drug and environmental treatments for environmentally transmitted diseases.

Control of neglected tropical diseases (NTDs) via mass drug administration (MDA) has increased considerably over the past decade, but strategies focused exclusively on human treatment show limited efficacy. This paper investigated trade-offs between drug and environmental treatments in the fight against NTDs by using schistosomiasis as a case study. We use optimal control techniques where the planner's objective is to treat the disease over a time horizon at the lowest possible total cost, where the total costs include treatment, transportation and damages (reduction in human health). We show that combining environmental treatments and drug treatments reduces the dependency on MDAs and that this reduction increases when the planners take a longer-run perspective on the fight to reduce NTDs. Our results suggest that NTDs with environmental reservoirs require moving away from a reliance solely on MDA to integrated treatment involving investment in both drug and environmental controls.

The allocation of time and risk of Lyme: A case of ecosystem service income and substitution effects.

Forests are often touted for their ecosystem services, including outdoor recreation. Historically forests were a source of danger and were avoided. Forests continue to be reservoirs for infectious diseases and their vectors - a disservice. We examine how this disservice undermines the potential recreational services by measuring the human response to environmental risk using exogenous variation in the risk of contracting Lyme Disease. We find evidence that individuals substitute away from spending time outdoors when there is greater risk of Lyme Disease infection. Individuals facing a higher risk of infection substitute away from outdoor leisure. On average individuals spent 1.54 fewer minutes outdoors at the average, 72 U.S. Centers for Disease Control and Prevention (CDC), confirmed cases of Lyme Disease. We estimate lost outdoor recreation of 9.41 hours per year per person in an average county in the North Eastern United States and an aggregate welfare loss on the order $2.8 billion to $5.0 billion per year.

Voluntary vaccinations and vaccine shortages: A theoretical analysis.

A simple dynamic model of vaccination is presented and analyzed to study how the amount of vaccines available affects people's vaccination decisions. In addition, the model is used to examine how the level of vaccination in equilibrium compares to the efficient or socially optimal level. It is shown that, when the stock of vaccines is large so that a shortage could never arise, an equilibrium is generically unique, and there is too little vaccination compared to the social optimum. When the stock of vaccines is small so that not everyone in a population could get vaccinated, a shortage could occur in equilibrium. Moreover, the occurrence of a shortage could be self-fulfilling: when agents expect a shortage to develop, they have a greater incentive to demand the vaccine right away, which increases the likelihood that a shortage will result; and when agents do not expect a shortage to arise, they are more willing to delay their vaccination decision, which reduces the likelihood of a shortage developing. This leads to the possible co-existence of multiple equilibria that differ in the level of vaccination. Multiple equilibria can arise, however, only if agents are uncertain about the cost of being infected - if agents are sufficiently certain about the cost of infection, then an equilibrium is unique. Furthermore, when the stock of vaccines is small, an equilibrium level of vaccination may be too high relative to the socially optimal level. Increasing the vaccine stock could have ambiguous effects on the level of vaccination in equilibrium but unambiguously increases social welfare.

Resistance diagnosis and the changing epidemiology of antibiotic resistance.

Widespread adoption of point-of-care resistance diagnostics (POCRD) reduces ineffective antibiotic use but could increase overall antibiotic use. Indeed, in the context of a standard susceptible-infected epidemiological model with a single antibiotic, POCRD accelerates the rise of resistance in the disease-causing bacterial population. When multiple antibiotics are available, however, POCRD may slow the rise of resistance even as more patients receive antibiotic treatment, belying the conventional wisdom that antibiotics are "exhaustible resources" whose increased use necessarily promotes the rise of resistance.

Network position and health care worker infections.

We use a newly collected data set coupled with an agent-based model to study the spread of infectious disease in hospitals. We estimate the average and marginal infections created by various worker groups in a hospital as a function of their network position in order to identify groups most crucial in a hospital-based epidemic. Surprisingly, we find that many groups with primary patient care responsibilities play a small role in spreading an infectious disease within our hospital data set. We also demonstrate that the effect of different network positions can be as important as the effect of different transmission rates for some categories of workers.

The optimal treatment of an infectious disease with two strains.

This paper explores the optimal treatment of an infectious disease in a Susceptible-Infected-Susceptible model, where there are two strains of the disease and one strain is more infectious than the other. The strains are perfectly distinguishable, instantly diagnosed and equally costly in terms of social welfare. Treatment is equally costly and effective for both strains. Eradication is not possible, and there is no superinfection. In this model, we characterise two types of fixed points: coexistence equilibria, where both strains prevail, and boundary equilibria, where one strain is asymptotically eradicated and the other prevails at a positive level. We derive regimes of feasibility that determine which equilibria are feasible for which parameter combinations. Numerically, we show that optimal policy exhibits switch points over time, and that the paths to coexistence equilibria exhibit spirals, suggesting that coexistence equilibria are never the end points of optimal paths.

Market analyses of livestock trade networks to inform the prevention of joint economic and epidemiological risks.

Conventional epidemiological studies of infections spreading through trade networks, e.g., via livestock movements, generally show that central large-size holdings (hubs) should be preferentially surveyed and controlled in order to reduce epidemic spread. However, epidemiological strategies alone may not be economically optimal when costs of control are factored in together with risks of market disruption from targeting core holdings in a supply chain. Using extensive data on animal movements in supply chains for cattle and swine in France, we introduce a method to identify effective strategies for preventing outbreaks with limited budgets while minimizing the risk of market disruptions. Our method involves the categorization of holdings based on position along the supply chain and degree of market share. Our analyses suggest that trade has a higher risk of propagating epidemics through cattle networks, which are dominated by exchanges involving wholesalers, than for swine. We assess the effectiveness of contrasting interventions from the perspectives of regulators and the market, using percolation analysis. We show that preferentially targeting minor, non-central agents can outperform targeting of hubs when the costs to stakeholders and the risks of market disturbance are considered. Our study highlights the importance of assessing joint economic-epidemiological risks in networks underlying pathogen propagation and trade.

Epidemics in markets with trade friction and imperfect transactions.

Market trade-routes can support infectious-disease transmission, impacting biological populations and even disrupting trade that conduces the disease. Epidemiological models increasingly account for reductions in infectious contact, such as risk-aversion behaviour in response to pathogen outbreaks. However, responses in market dynamics clearly differ from simple risk aversion, as are driven by other motivation and conditioned by "friction" constraints (a term we borrow from labour economics). Consequently, the propagation of epidemics in markets of, for example livestock, is frictional due to time and cost limitations in the production and exchange of potentially infectious goods. Here we develop a coupled economic-epidemiological model where transient and long-term market dynamics are determined by trade friction and agent adaptation, and can influence disease transmission. The market model is parameterised from datasets on French cattle and pig exchange networks. We show that, when trade is the dominant route of transmission, market friction can be a significantly stronger determinant of epidemics than risk-aversion behaviour. In particular, there is a critical level of friction above which epidemics do not occur, which suggests some epidemics may not be sustained in highly frictional markets. In addition, friction may allow for greater delay in removal of infected agents that still mitigates the epidemic and its impacts. We suggest that policy for minimising contagion in markets could be adjusted to the level of market friction, by adjusting the urgency of intervention or by increasing friction through incentivisation of larger-volume less-frequent transactions that would have limited effect on overall trade flow. Our results are robust to model specificities and can hold in the presence of non-trade disease-transmission routes.