Evolution in health and medicine Sackler colloquium: a public choice framework for controlling transmissible and evolving diseases

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.

Vaccination as personal public good provision

Vaccination against infectious diseases has both private and public benefits. We study whether social preferences-concerns for the well-being of other people-are associated with one's decision regarding vaccination. We measure these social preferences for 549 online subjects with a public-good game and an altruism game. To the extent that one gets vaccinated out of concern for the health of others, contribution in the public-good game is analogous to an individual's decision to obtain vaccination, while our altruism game provides a different measure of altruism, equity, and efficiency concerns. We proxy vaccine demand with how quickly a representative individual voluntarily took the initial vaccination for COVID-19 (after the vaccine was widely available). We collect COVID-19 vaccination history separately from the games to avoid experimenter-demand effects. We find a strong result: Contribution in the public-good game is associated with greater demand to voluntarily receive a first dose, and thus also to vaccinate earlier. Compared to a subject who contributes nothing, one who contributes the maximum ($4) is 58% more likely to obtain a first dose voluntarily in the four-month period that we study (April through August 2021). In short, people who are more pro-social are more likely to take a voluntary COVID-19 vaccination. Behavior in our altruism game does not predict vaccination. We recommend further research on the use of pro-social preferences to help motivate individuals to vaccinate for other transmissible diseases, such as the flu and HPV.

Rational social distancing policy during epidemics with limited healthcare capacity

Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During recent epidemics there has been much debate about policy, including how and when to impose restrictions on behaviour. Policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Whether health services might be 'overwhelmed' has emerged as a key consideration. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. In order to achieve this, we develop a nested optimisation algorithm of both the government intervention strategy and the resulting equilibrium behaviour of individuals. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. We find an extremely sharp drop in peak infections at a critical maximum infection cost in the government's objective function. This is in marked contrast to the gradual reduction of infections if individuals make decisions without government intervention. We find optimal interventions vary less strongly in time when interventions are costly to the government and that the critical cost of the policy switch depends on how costly interventions are.

Convincing the "Herd" of immunity: Lessons from smallpox vaccination in 19th century Germany

Although vaccination is a cost-effective way to control infectious diseases, it is often met with popular resistance. Studying smallpox in 19th century Germany, this paper explores how economic incentives contribute to this phenomenon. The paper adds to the literature by combining mathematical epidemiology and unpublished archival evidence from two German states - Baden and Wurttemberg. The two states are an intriguing case because their initial conditions and vaccination laws were similar. Despite this, Baden experienced lower smallpox prevalence and higher vaccination uptake than Wurttemberg. The epidemiological model predicts that incentives to vaccinate decline rapidly when immunization reduces prevalence. The archival evidence reveals that Baden offset this decline by creating a public vaccination system which reduced costs for vaccinees and vaccinators alike. This suggests that the high fixed costs of centralized immunization policies can be compensated by economies of scale and popular acceptance.

The pox of politics: Troesken's tradeoff reexamined

In The Pox of Liberty, Werner Troesken details the tradeoff between liberal institutions and communicable disease. According to Troesken, individual freedom presents a danger to the public health in the face of infectious disease, while constitutional constraints restrict the government's ability to implement effective policy. Contra Troesken, I argue that decision-makers, amidst a crisis of contagion, neglect intertemporal tradeoffs, thereby discounting long run costs while favoring short run policies. These policies, once implemented, are difficult to reverse due to the path dependent nature of political institutions. Irreversible and self-reinforcing growth in political institutions established to enhance health can have an unintended negative impact on health during future crises, where political agents must operate in a more cumbersome and error-prone institutional environment. Using events from the history of public health in the U.S. as support for my theory, I conclude that Troesken's alleged tradeoff ought to be met with greater skepticism.

Optimal vaccine subsidies for endemic diseases

In Goodkin-Gold et al. (2021), we analyzed optimal subsidies for a vaccine against an epidemic outbreak like Covid-19. This companion paper alters the underlying epidemiological model to suit endemic diseases requiring continuous vaccination of new cohorts-also suiting an epidemic like Covid-19 if, following Gans (2020), one assumes peaks are leveled by social distancing. We obtain qualitatively similar results: across market structures ranging from perfect competition to monopoly, the subsidy needed to induce first-best vaccination coverage on the private market is highest for moderately infectious diseases, which invite the most free riding; extremely infectious diseases drive more consumers to become vaccinated, attenuating externalities. Stylized calibrations to HIV, among other diseases, suggest that first-best subsidies can be exorbitantly high when suppliers have market power, rationalizing alternative policies observed in practice such as bulk purchases negotiated by the government on behalf of the consumers.

A theory of voluntary testing and self-isolation in an ongoing pandemic

Beyond Covid-19, there is a growing interest in what economic structures will be needed to face ongoing pandemics. In this paper, we focus on the diagnostic problem and examine a new paradigm of voluntary self-testing by private individuals. We develop a dynamic model where individuals without symptoms face daily choices of either taking the risk of going out (to work and socialize), staying at home in self-isolation, or using a test to verify whether they are infected before going out. Our central insight is that the equilibrium public infection risk falls when home-based testing becomes cheaper and easier to use, even if they generate both false-positive (type I error) and false-negative (type II error) test outcomes. We also show that the presence of naïve individuals actually reduces the equilibrium infection risk in the economy. Overall our model shows that, even if inaccurate, home-based tests are vital for an economy facing an ongoing pandemic.

A review and agenda for integrated disease models including social and behavioural factors

Social and behavioural factors are critical to the emergence, spread and containment of human disease, and are key determinants of the course, duration and outcomes of disease outbreaks. Recent epidemics of Ebola in West Africa and coronavirus disease 2019 (COVID-19) globally have reinforced the importance of developing infectious disease models that better integrate social and behavioural dynamics and theories. Meanwhile, the growth in capacity, coordination and prioritization of social science research and of risk communication and community engagement (RCCE) practice within the current pandemic response provides an opportunity for collaboration among epidemiological modellers, social scientists and RCCE practitioners towards a mutually beneficial research and practice agenda. Here, we provide a review of the current modelling methodologies and describe the challenges and opportunities for integrating them with social science research and RCCE practice. Finally, we set out an agenda for advancing transdisciplinary collaboration for integrated disease modelling and for more robust policy and practice for reducing disease transmission.

Containment efficiency and control strategies for the corona pandemic costs

The rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of measuring the effectiveness of containment strategies, balancing public health considerations with the economic costs of social distancing measures. We introduce a modified epidemic model that we name the controlled-SIR model, in which the disease reproduction rate evolves dynamically in response to political and societal reactions. An analytic solution is presented. The model reproduces official COVID-19 cases counts of a large number of regions and countries that surpassed the first peak of the outbreak. A single unbiased feedback parameter is extracted from field data and used to formulate an index that measures the efficiency of containment strategies (the CEI index). CEI values for a range of countries are given. For two variants of the controlled-SIR model, detailed estimates of the total medical and socio-economic costs are evaluated over the entire course of the epidemic. Costs comprise medical care cost, the economic cost of social distancing, as well as the economic value of lives saved. Under plausible parameters, strict measures fare better than a hands-off policy. Strategies based on current case numbers lead to substantially higher total costs than strategies based on the overall history of the epidemic.

Prosociality predicts health behaviors during the COVID-19 pandemic

Socially responsible behavior is crucial for slowing the spread of infectious diseases. However, economic and epidemiological models of disease transmission abstract from prosocial motivations as a driver of behaviors that impact the health of others. In an incentivized study, we show that a large majority of people are very reluctant to put others at risk for their personal benefit. Moreover, this experimental measure of prosociality predicts health behaviors during the COVID-19 pandemic, measured in a separate and ostensibly unrelated study with the same people. Prosocial individuals are more likely to follow physical distancing guidelines, stay home when sick, and buy face masks. We also find that prosociality measured two years before the pandemic predicts health behaviors during the pandemic. Our findings indicate that prosociality is a stable, long-term predictor of policy-relevant behaviors, suggesting that the impact of policies on a population may depend on the degree of prosociality.

Evolutionary perspectives on human behavior during the coronavirus pandemic: Insights from game theory

The coronavirus pandemic constitutes a global challenge to society and medicine. Here, we review evolutionary insights that are relevant for the understanding of how people respond to the pandemic and what to expect in the aftermath of the crisis. Specifically, we argue that the behavioral immune system (BIS) and sickness behavior (SB) comprise two adaptive responses to impending and actual infection, respectively, and that individuals activating their BIS differ from those showing SB in important ways that may have implications for the prevention and treatment of COVID-19. Moreover, we reframe some of the behavioral health issues associated with the pandemic in a game-theoretical scenario, illustrating the difficulties that arise when public health is treated as a 'public good'. Lay summary: The coronavirus pandemic constitutes a global challenge to society and medicine. In this article, we employ evolutionary theory to improve our understanding of how people respond to the pandemic. Specifically, we argue that human behavior is guided by ancient mechanisms involving either the avoidance of infection or defense against attacks in times of enhanced vulnerability. Moreover, we reframe some of the behavioral health issues associated with the pandemic in a game-theoretical scenario. This helps understand why most people comply with rules of social distancing, while a minority fails to do so for very different reasons. The evolutionary perspective also allows making some predictions for the course of the pandemic.

Feedback Between Behavioral Adaptations and Disease Dynamics

We study the feedback processes between individual behavior, disease prevalence, interventions and social networks during an influenza pandemic when a limited stockpile of antivirals is shared between the private and the public sectors. An economic model that uses prevalence-elastic demand for interventions is combined with a detailed social network and a disease propagation model to understand the feedback mechanism between epidemic dynamics, market behavior, individual perceptions, and the social network. An urban and a rural region are simulated to assess the robustness of results. Results show that an optimal split between the private and public sectors can be reached to contain the disease but the accessibility of antivirals from the private sector is skewed towards the richest income quartile. Also, larger allocations to the private sector result in wastage where individuals who do not need it are able to purchase it but who need it cannot afford it. Disease prevalence increases with household size and total contact time but not by degree in the social network, whereas wastage of antivirals decreases with degree and contact time. The best utilization of drugs is achieved when individuals with high contact time use them, who tend to be the school-aged children of large families.

Provisioning of Public Health Can Be Designed to Anticipate Public Policy Responses

Public health policies can elicit strong responses from individuals. These responses can promote, reduce, and even reverse the expected benefits of the policies. Therefore, projections of individual responses to policy can be important ingredients in policy design. Yet our foresight of individual responses to public health investment remains limited. This paper formulates a population game describing the prevention of infectious disease transmission when community health depends on the interactions of individual and public investments. We compare three common relationships between public and individual investments and explain how each relationship alters policy responses and health outcomes. Our methods illustrate how identifying system interactions between nature and society can help us anticipate policy responses.

Household perceptions and subjective valuations of indoor residual spraying programmes to control malaria in northern Uganda

BACKGROUND

Insecticide-based tools remain critical for controlling vector-borne diseases in Uganda. Securing public support from targeted populations for such tools is an important component in sustaining their long-run effectiveness. Yet little quantitative evidence is available on the perceived benefits and costs of vector control programmes among targeted households.

METHODS

A survey was administered to a clustered random sample of 612 households in Gulu and Oyam districts of northern Uganda during a period of very high malaria transmission and following a pilot indoor residual spray (IRS) programme. A discrete choice experiment was conducted within the survey, in which respondents indicated their preferences for different IRS programmes relative to money compensation in a series of experimentally controlled, hypothetical choice sets. The data were analysed using conditional logit regression models to estimate respondents' willingness to accept (WTA) some amount of money compensation in lieu of foregone malaria risk reductions. Latent class models were used to analyse whether respondent characteristics predicted WTA.

RESULTS

Average WTA is estimated at $8.94 annually for a 10 % reduction in malaria risk, and additional co-benefits of IRS were estimated to be worth on average $54-$56 (depending on insecticide type) per round of IRS. Significant heterogeneity is observed: Four in five household heads in northern Uganda have high valuations for IRS programmes, while the remaining 20 % experience costly side effects of IRS (valued at between $2 and $3 per round). Statistically significant predictors of belonging to the high-value group include respondent gender, mean age of household members, participation in previous IRS, basic knowledge of mosquito reproduction, and the number of mosquito nets owned. Proxies for household income and wealth are not found to be statistically significant predictors of WTA.

CONCLUSIONS

This study suggests that the majority of people in areas of high malaria transmission like northern Uganda place a high value on vector control programmes using IRS. However, there is significant heterogeneity in terms of the perceived side effects (positive and negative). This has implications for sustaining public support for these programmes in the long-term.

Modelling input-output flows of severe acute respiratory syndrome in mainland China

BACKGROUND

Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.

METHODS

We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.

RESULTS

The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78% of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54% with a 1% increase in urban income, and intraregional flows increased by 8.35%. In contrast, the total interregional flows decreased by 3.38% with a 1% increase in rural income, and intraregional flows declined by 2.29%. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.

CONCLUSIONS

Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.

The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

Asymptomatic transmission and the resurgence of Bordetella pertussis

BACKGROUND

The recent increase in whooping cough incidence (primarily caused by Bordetella pertussis) presents a challenge to both public health practitioners and scientists trying to understand the mechanisms behind its resurgence. Three main hypotheses have been proposed to explain the resurgence: 1) waning of protective immunity from vaccination or natural infection over time, 2) evolution of B. pertussis to escape protective immunity, and 3) low vaccine coverage. Recent studies have suggested a fourth mechanism: asymptomatic transmission from individuals vaccinated with the currently used acellular B. pertussis vaccines.

METHODS

Using wavelet analyses of B. pertussis incidence in the United States (US) and United Kingdom (UK) and a phylodynamic analysis of 36 clinical B. pertussis isolates from the US, we find evidence in support of asymptomatic transmission of B. pertussis. Next, we examine the clinical, public health, and epidemiological consequences of asymptomatic B. pertussis transmission using a mathematical model.

RESULTS

We find that: 1) the timing of changes in age-specific attack rates observed in the US and UK are consistent with asymptomatic transmission; 2) the phylodynamic analysis of the US sequences indicates more genetic diversity in the overall bacterial population than would be suggested by the observed number of infections, a pattern expected with asymptomatic transmission; 3) asymptomatic infections can bias assessments of vaccine efficacy based on observations of B. pertussis-free weeks; 4) asymptomatic transmission can account for the observed increase in B. pertussis incidence; and 5) vaccinating individuals in close contact with infants too young to receive the vaccine ("cocooning" unvaccinated children) may be ineffective.

CONCLUSIONS

Although a clear role for the previously suggested mechanisms still exists, asymptomatic transmission is the most parsimonious explanation for many of the observations surrounding the resurgence of B. pertussis in the US and UK. These results have important implications for B. pertussis vaccination policy and present a complicated scenario for achieving herd immunity and B. pertussis eradication.

Interaction of Vaccination and Reduction of Antibiotic Use Drives Unexpected Increase of Pneumococcal Meningitis

Antibiotic-use policies may affect pneumococcal conjugate-vaccine effectiveness. The reported increase of pneumococcal meningitis from 2001 to 2009 in France, where a national campaign to reduce antibiotic use was implemented in parallel to the introduction of the 7-valent conjugate vaccine, provides unique data to assess these effects. We constructed a mechanistic pneumococcal transmission model and used likelihood to assess the ability of competing hypotheses to explain that increase. We find that a model integrating a fitness cost of penicillin resistance successfully explains the overall and age-stratified pattern of serotype replacement. By simulating counterfactual scenarios of public health interventions in France, we propose that this fitness cost caused a gradual and pernicious interaction between the two interventions by increasing the spread of nonvaccine, penicillin-susceptible strains. More generally, our results indicate that reductions of antibiotic use may counteract the benefits of conjugate vaccines introduced into countries with low vaccine-serotype coverages and high-resistance frequencies. Our findings highlight the key role of antibiotic use in vaccine-induced serotype replacement and suggest the need for more integrated approaches to control pneumococcal infections.

Timing of antimicrobial use influences the evolution of antimicrobial resistance during disease epidemics

How can antimicrobial drugs be deployed optimally during infectious disease epidemics? Our mathematical models show it is optimal to delay treatment to maximize successful treatments. In formulating policy, however, this must be balanced against the risk of incorrectly predicting the peak of an epidemic.

Background: Although the emergence and spread of antibiotic resistance have been well studied for endemic infections, comparably little is understood for epidemic infections such as influenza. The availability of antimicrobial treatments for epidemic diseases raises the urgent question of how to deploy treatments to achieve maximum benefit despite resistance evolution. Recent simulation studies have shown that the number of cases prevented by antimicrobials can be maximized by delaying the use of treatments during an epidemic. Those studies focus on indirect effects of antimicrobial use: preventing disease among untreated individuals. Here, we identify and examine direct effects of antimicrobial use: the number of successfully treated cases.

Methodology: We develop mathematical models to study how the schedule of antiviral use influences the success or failure of subsequent use due to the spread of resistant strains.

Results: Direct effects are maximized by postponing drug use, even with unlimited stockpiles of drugs. This occurs because the early use of antimicrobials disproportionately drives emergence and spread of antibiotic resistance, leading to subsequent treatment failure. However, for antimicrobials with low effect on transmission, the relative benefit of delaying antimicrobial deployment is greatly reduced and can only be reaped if the trajectory of the epidemic can be accurately estimated early.

Conclusions and implications: Health planners face uncertainties during epidemics, including the possibility of early containment. Hence, despite the optimal deployment time near the epidemic peak, it will often be preferable to initiate widespread antimicrobial use as early as possible, particularly if the drug is ineffective in reducing transmission.

Applying evolutionary biology to address global challenges

Two categories of evolutionary challenges result from escalating human impacts on the planet. The first arises from cancers, pathogens, and pests that evolve too quickly and the second, from the inability of many valued species to adapt quickly enough. Applied evolutionary biology provides a suite of strategies to address these global challenges that threaten human health, food security, and biodiversity. This Review highlights both progress and gaps in genetic, developmental, and environmental manipulations across the life sciences that either target the rate and direction of evolution or reduce the mismatch between organisms and human-altered environments. Increased development and application of these underused tools will be vital in meeting current and future targets for sustainable development.

The economics of vaccination

The market for vaccinations is widely believed to be characterized by market failures, because individuals do not internalize the positive externalities that their vaccination decisions may confer on other individuals. Francis (1997) provided a set of assumptions under which the equilibrium vaccination pattern is socially optimal. We show that his conditions are not necessary for the welfare theorem to hold but that in general, the market yields inefficiently low vaccination uptake. Equilibrium non-optimality may obtain if (i) agents can recover from infection, (ii) vaccines are imperfect, (iii) individuals are ex ante heterogeneous, (iv) vaccination timing is inflexible or (v) the planning horizon is finite. Apart from the case with heterogeneity, inefficiencies result from the presence of strategic interaction.

Knowledge of Emergency Medicine Residents in Relation to Prevention of Tetanus

INTRODUCTION

Knowledge of emergency medicine residents about the management of patients suspected of having tetanus-favoring wounds is very important due to their responsibility for the treatment of such patients. The aim of the present study was to evaluate this knowledge and making sure of the adequacy of instructions they have received in relation to prevention of tetanus.

METHODS

A reliable and reproducible questionnaire was used to evaluate knowledge of all the emergency medicine residents in Imam Hussein Hospital in Tehran, Iran, about conditions favoring tetanus (9 questions) and proper interventions in such conditions (12 questions). The questionnaires were completed and scored as poor and good. The Mann-Whitney U test was used to analyze data. Statistical significance was set at P<0.05.

RESULTS

In the present study, 73 emergency medicine residents were evaluated (45.2% male). Knowledge of 31 (42.5%) residents in relation to conditions favoring tetanus and 41 (56.2%) residents in correct therapeutic interventions was in good level. The most frequent incorrect answer was related to diabetic ulcers and wounds in patients with sepsis. There was an increase in scores of conditions favoring tetanus (P<0.001) and correct therapeutic interventions (P=0.001) with an increase in educational years. However, age (P=0.64), gender (P=0.31), job experience (P=0.38) and participation in educational courses (P=0.67) had no effect on the knowledge level of emergency medicine residents.

CONCLUSION

According to the findings of the present study, the knowledge of emergency medicine residents about correct management of patients suspected of tetanus was low, which emphasizes the necessity of providing further instructions on prevention of tetanus in wound management.

Operational issues and network effects in vaccine markets

One of the most important concerns for managing public health is the prevention of infectious diseases. Although vaccines provide the most effective means for preventing infectious diseases, there are two main reasons why it is often difficult to reach a socially optimal level of vaccine coverage: (i) the emergence of operational issues (such as yield uncertainty) on the supply side, and (ii) the existence of negative network effects on the consumption side. In particular, uncertainties about production yield and vaccine imperfections often make manufacturing some vaccines a risky process and may lead the manufacturer to produce below the socially optimal level. At the same time, negative network effects provide incentives to potential consumers to free ride off the immunity of the vaccinated population. In this research, we consider how a central policy-maker can induce a socially optimal vaccine coverage through the use of incentives to both consumers and the vaccine manufacturer. We consider a monopoly market for an imperfect vaccine; we show that a fixed two-part subsidy is unable to coordinate the market, but derive a two-part menu of subsidies that leads to a socially efficient level of coverage.

Optimizing treatment regimes to hinder antiviral resistance in influenza across time scales

The large-scale use of antivirals during influenza pandemics poses a significant selection pressure for drug-resistant pathogens to emerge and spread in a population. This requires treatment strategies to minimize total infections as well as the emergence of resistance. Here we propose a mathematical model in which individuals infected with wild-type influenza, if treated, can develop de novo resistance and further spread the resistant pathogen. Our main purpose is to explore the impact of two important factors influencing treatment effectiveness: i) the relative transmissibility of the drug-resistant strain to wild-type, and ii) the frequency of de novo resistance. For the endemic scenario, we find a condition between these two parameters that indicates whether treatment regimes will be most beneficial at intermediate or more extreme values (e.g., the fraction of infected that are treated). Moreover, we present analytical expressions for effective treatment regimes and provide evidence of its applicability across a range of modeling scenarios: endemic behavior with deterministic homogeneous mixing, and single-epidemic behavior with deterministic homogeneous mixing and stochastic heterogeneous mixing. Therefore, our results provide insights for the control of drug-resistance in influenza across time scales.

Pathogen mutation modeled by competition between site and bond percolation

While disease propagation is a main focus of network science, its coevolution with treatment has yet to be studied in this framework. We present a mean-field and stochastic analysis of an epidemic model with antiviral administration and resistance development. We show how this model maps to a coevolutive competition between site and bond percolation featuring hysteresis and both second- and first-order phase transitions. The latter, whose existence on networks is a long-standing question, imply that a microscopic change in infection rate can lead to macroscopic jumps in expected epidemic size.

The timing and targeting of treatment in influenza pandemics influences the emergence of resistance in structured populations

Antiviral resistance in influenza is rampant and has the possibility of causing major morbidity and mortality. Previous models have identified treatment regimes to minimize total infections and keep resistance low. However, the bulk of these studies have ignored stochasticity and heterogeneous contact structures. Here we develop a network model of influenza transmission with treatment and resistance, and present both standard mean-field approximations as well as simulated dynamics. We find differences in the final epidemic sizes for identical transmission parameters (bistability) leading to different optimal treatment timing depending on the number initially infected. We also find, contrary to previous results, that treatment targeted by number of contacts per individual (node degree) gives rise to more resistance at lower levels of treatment than non-targeted treatment. Finally we highlight important differences between the two methods of analysis (mean-field versus stochastic simulations), and show where traditional mean-field approximations fail. Our results have important implications not only for the timing and distribution of influenza chemotherapy, but also for mathematical epidemiological modeling in general. Antiviral resistance in influenza may carry large consequences for pandemic mitigation efforts, and models ignoring contact heterogeneity and stochasticity may provide misleading policy recommendations.

Resistance of influenza to common antiviral agents carries the possibility of causing large morbidity and mortality through failure of treatment and should be taken into account when planning public health interventions focused on stopping transmission. Here we present a mathematical model of influenza transmission which incorporates heterogeneous contact structure and stochastic transmission events. We find scenarios when treatment either induces large levels of resistance or no resistance at identical values of transmission rates depending on the number initially infected. We also find, contrary to previous results, that targeted treatment causes more resistance at lower treatment levels than non-targeted treatment. Our results have important implications for the timing and distribution of antivirals in epidemics and highlight important differences in how transmission is modeled and where assumptions made in previous models cause them to lead to erroneous conclusions.

Behavioral responses to epidemics in an online experiment: using virtual diseases to study human behavior

We report the results of a study we conducted using a simple multiplayer online game that simulates the spread of an infectious disease through a population composed of the players. We use our virtual epidemics game to examine how people respond to epidemics. The analysis shows that people's behavior is responsive to the cost of self-protection, the reported prevalence of disease, and their experiences earlier in the epidemic. Specifically, decreasing the cost of self-protection increases the rate of safe behavior. Higher reported prevalence also raises the likelihood that individuals would engage in self-protection, where the magnitude of this effect depends on how much time has elapsed in the epidemic. Individuals' experiences in terms of how often an infection was acquired when they did not engage in self-protection are another factor that determines whether they will invest in preventive measures later on. All else being equal, individuals who were infected at a higher rate are more likely to engage in self-protective behavior compared to those with a lower rate of infection. Lastly, fixing everything else, people's willingness to engage in safe behavior waxes or wanes over time, depending on the severity of an epidemic: when prevalence is high, people are more likely to adopt self-protective measures as time goes by; when prevalence is low, a ‘self-protection fatigue’ effect sets in whereby individuals are less willing to engage in safe behavior over time.

Behavioral Epidemiology of Infectious Diseases: An Overview

The focus of the growing discipline of behavioral epidemiology (BE) of infectious diseases is on individual behavior as a key determinant of infection trajectories. This overview departs from the central, but static, role of human behavior in traditional mathematical models of infection to motivate the importance of including behavior into epidemiological models. Our aim is threefold. First, we attempt to motivate the historical and cultural background underpinning the BE revolution, focusing on the issue of rational opposition to vaccines as a natural endpoint of the changed relation between man and disease in modern industrialized countries. Second, we review those contributions, from both mathematical epidemiology and economics, that forerun the current “epidemic” of studies on BE. Last, we offer a more detailed overview of the current epidemic phase of BE studies and, still motivated by the issue of immunization choices, introduce some baseline ideas and models.

Evolutionary medicine: its scope, interest and potential

This review is aimed at readers seeking an introductory overview, teaching courses and interested in visionary ideas. It first describes the range of topics covered by evolutionary medicine, which include human genetic variation, mismatches to modernity, reproductive medicine, degenerative disease, host-pathogen interactions and insights from comparisons with other species. It then discusses priorities for translational research, basic research and health management. Its conclusions are that evolutionary thinking should not displace other approaches to medical science, such as molecular medicine and cell and developmental biology, but that evolutionary insights can combine with and complement established approaches to reduce suffering and save lives. Because we are on the cusp of so much new research and innovative insights, it is hard to estimate how much impact evolutionary thinking will have on medicine, but it is already clear that its potential is enormous.

Improving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1

This article synthesizes and extends discussions held during an international meeting on "Surveillance for Decision Making: The Example of 2009 Pandemic Influenza A/H1N1," held at the Center for Communicable Disease Dynamics (CCDD), Harvard School of Public Health, on June 14 and 15, 2010. The meeting involved local, national, and global health authorities and academics representing 7 countries on 4 continents. We define the needs for surveillance in terms of the key decisions that must be made in response to a pandemic: how large a response to mount and which control measures to implement, for whom, and when. In doing so, we specify the quantitative evidence required to make informed decisions. We then describe the sources of surveillance and other population-based data that can presently--or in the future--form the basis for such evidence, and the interpretive tools needed to process raw surveillance data. We describe other inputs to decision making besides epidemiologic and surveillance data, and we conclude with key lessons of the 2009 pandemic for designing and planning surveillance in the future.

Adaptive human behavior in epidemiological models

The science and management of infectious disease are entering a new stage. Increasingly public policy to manage epidemics focuses on motivating people, through social distancing policies, to alter their behavior to reduce contacts and reduce public disease risk. Person-to-person contacts drive human disease dynamics. People value such contacts and are willing to accept some disease risk to gain contact-related benefits. The cost-benefit trade-offs that shape contact behavior, and hence the course of epidemics, are often only implicitly incorporated in epidemiological models. This approach creates difficulty in parsing out the effects of adaptive behavior. We use an epidemiological-economic model of disease dynamics to explicitly model the trade-offs that drive person-to-person contact decisions. Results indicate that including adaptive human behavior significantly changes the predicted course of epidemics and that this inclusion has implications for parameter estimation and interpretation and for the development of social distancing policies. Acknowledging adaptive behavior requires a shift in thinking about epidemiological processes and parameters.

Past evolutionary tradeoffs represent opportunities for crop genetic improvement and increased human lifespan

The repeated evolution of complex adaptations – crop mimicry by weeds, for example, or CO₂-concentrating C4 photosynthesis – shows the power of natural selection to solve difficult problems that limited fitness in past environments. The sophistication of natural selection's innovations contrasts with the relatively simple changes (e.g., increasing the expression of existing genes) readily achievable by today's biotechnology. Mutants with greater expression of these genes arose repeatedly over the course of evolution, so their present rarity indicates rejection by natural selection. Similarly, medical interventions that simply up- or down-regulate existing physiological mechanisms presumably recreate phenotypes also rejected by past natural selection. Some tradeoffs that constrained past natural selection still apply, such as those resulting from conservation of matter. But tradeoffs between present human goals and individual fitness in past environments may represent fairly easy opportunities to achieve our goals by reversing some effects of past selection. This point is illustrated with three examples, based on tradeoffs between (i) individual-plant fitness versus whole-crop performance, (ii) the fitness of symbionts (rhizobia) versus that of their legume hosts, and (iii) human fertility versus longevity in the context of environmental cues, such as consumption of ‘famine foods’, that predict trends in population size.