Merging Economics and Epidemiology to Improve the Prediction and Management of Infectious Disease
Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as “economic epidemiology” or “epidemiological economics,” the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.
Keywordseconomic epidemiology epidemiological economics incentives infectious disease
Economic Epidemiology and Epidemiological Economics
Economic behavior is known to play a key role in disease transmission. Throughout history, new pathogens have emerged with the opening of new markets or trade routes. The Black Death in the fourteenth century, and the sixteenth century Columbian exchange—which brought smallpox and typhus to the Americas, and syphilis to Europe, are the best-known examples (McNeill 1977; Yoo et al. 2010). In the last few decades, the growth of global trade and travel have been implicated in the emergence of human infectious diseases such as plague, cholera, HIV (Tatem et al. 2006a, b), West Nile virus (Lanciotti et al. 2000), SARS (Guan et al. 2003; Hufnagel et al. 2004), as well as livestock diseases such as H9N2 Avian influenza, Bovine Spongiform Encephalopathy, Bluetongue or Foot and Mouth disease (Rweyemamu and Astudillo 2002; Karesh et al. 2005; Fevre et al. 2006; Purse et al. 2008), and diseases of wildlife—potentially white-nose syndrome in bats (Pikula et al. 2012). In the USA, many other wildlife diseases and zoonoses have been linked to live animal imports (Smith et al. 2009a). Trade and travel affect the likelihood that pathogens are spread internationally by altering the number and variety of infectious-susceptible contacts (Smith et al. 2007; Jones et al. 2008; Suhrcke et al. 2011; Daszak 2012; Kilpatrick and Randolph 2012). In the same way, the decisions people make to engage with others in their own community affect the spread of disease nationally. Since people take account of potential disease risks, it is possible to analyze the spread of disease as a function of the costs and benefits of disease risk management.
In recent years, work at the boundary between ecology, epidemiology, and economics has shed new light on the way that economic behavior affects the spread of pests and pathogens (reviewed in Perrings 2014). The approach, referred to either as economic epidemiology or as epidemiological economics (hereafter EE), initially focused on the relationship between preventive behavior and disease prevalence (Philipson 2000). More recently, it has focused on the economic causes and epidemiological consequences of the number and type of contacts people make (Gersovitz and Hammer 2003, 2004; Barrett and Hoel 2007; Funk et al. 2009; Funk et al. 2010; Springborn et al. 2010). That is, the economic factors behind contact and mixing decisions are treated as part of the disease transmission mechanism. The approach provides a deeper understanding of the dynamics of epidemics, and opens up a new set of disease management options that target either the contact rate (Kremer 1996; Auld 2003) or the probability that contact leads to infection (Geoffard and Philipson 1996).
EE models extend classic compartmental epidemiological models that divide the population into compartments defined by health and demographic status. The classic models focus on the basic reproductive ratio of the disease, R 0—the number of secondary cases in a naïve, wholly susceptible, and disease-free population that result from the initial introduction of pathogen (Kermack and Mckendrick 1929; Anderson and May 1979, 1991). In the simplest models, R 0 is the product of three factors: the contact rate, the conditional probability of transmission per contact, and the duration of the infectious period. It is used to indicate whether or not the infection prevalence will increase or decrease. When R 0 > 1 the pathogen may spread, when R 0 < 1 it will not. The basic reproductive ratio, or variants such as the effective reproduction number (which measures transmission in a population that may be only partially susceptible) and the control reproduction number (which measures transmission in a susceptible population with control measures in place), are then used to inform disease management (Brauer and Castillo-Chavez 2013). The EE approach treats the reproduction number as a function of the decisions that underpin contact between susceptible and infected individuals. It thus opens up a different set of management options.
The EE approach is ultimately grounded in bioeconomic models of renewable resource management (Clark 1973, 1976, 1979). EE models focus on the optimal disease avoidance strategy and how that feeds back into the spread of infectious diseases of people (Geoffard and Philipson 1996; Kremer 1996; Auld 2003; Francis 2008) and animals (Horan and Wolf 2005; Horan et al. 2010, 2011). The approach also considers the consequences of disease risk management for economic development (Barrett and Hoel 2007) and growth (Grossman 1972; Boucekkine and Laffargue 2010; Chakraborty et al. 2010). In what follows, we focus on two risk management strategies—contact reduction and selective mixing. However, we note that considerable attention has also been paid to vaccination (Francis 1997, 2004; Boulier et al. 2007; Cook et al. 2009).
A common feature of EE models is that behavior affects, and is affected by, the disease risks involved in both contact and mixing decisions (Fenichel et al. 2011; Aadland et al. 2013; Fenichel and Wang 2013; Morin et al. 2013). While the term risk is used in many non-economic applications to denote the probability of an undesirable or bad outcome, we use the term risk to denote the product of the probability and the value of the bad outcome. It is an expected cost. Hence, disease risk is the probability of infection multiplied by the cost of infection. There is a considerable literature on the impact of disease risk, in the expected cost sense, on behavior (Francis 1997; Auld 2003; Chen 2004; Del Valle et al. 2005; Bootsma and Ferguson 2007; Klein et al. 2007; Chen 2009; Funk et al. 2009; Reluga 2010; Chen et al. 2011; Gersovitz 2011), at least some of which is empirically based (Caley et al. 2008; Gersovitz 2011; Fenichel et al. 2013). The evidence suggests that the expected cost of disease, or at least the part of cost that is carried directly by decision-makers, is weighed amongst the benefits and costs of contact and mixing decisions. An improved understanding of how these behavioral responses feed back into infectious disease dynamics strengthens capacity to predict the course of epidemics (Bauch and Earn 2004; Reluga 2010; Perra et al. 2011; Fenichel and Wang 2013).
Beyond improved prediction, the EE approach has the potential to reduce the social cost of disease management relative to classical approaches. Specifically, it allows public health authorities to go beyond traditional control methods such as vaccination, treatment, or social distancing, and to use economic incentives that change the course of epidemics by changing private contact and mixing decisions (Francis 2004; Chowell et al. 2009a; Fenichel 2013). In this paper, we review the development of the EE approach, and show how it is creating new options for the way epidemics are evaluated and managed.
The Basic Structure and Results of EE Models
It is useful to distinguish between the private decision problem (the decision-problem facing susceptible and infectious individuals, or those trading potentially infected animals or animal products) and the social decision-problem (the decision-problem facing public health or sanitary authorities). The main elements of both problems are an objective function describing the decision-maker’s goals, a constraint set describing the dynamics of the system being managed, a control or choice set—the mechanisms by which the decision-maker is able to influence those dynamics, and the feedback loops that link these components.
Because the choices people make change infectious disease transmission rates, they also change epidemiological dynamics. It follows that disease dynamics are sensitive both to the cost of disease (the income forgone during illness and the direct cost of illness) and the cost of disease avoidance. If the cost of disease is very low there is little incentive to avoid it, and disease dynamics will be those associated with proportionate mixing. If the cost of illness is very high, people will invest substantial resources in disease avoidance. In extreme cases, private decisions about selection of contacts can lead to an effective quarantine on infected individuals—an effect that would never occur in classical models. Disease dynamics are also sensitive to the benefits of contact. People trade-off disease risks against the benefits of contact. If there is much to be gained from contact they will accept much greater disease risks than if there is little to be gained (Areal et al. 2008; Fenichel et al. 2010; Gramig and Horan 2010; Horan et al. 2010).
Improved understanding of the behaviors that influence disease dynamics improves disease management. It increases both the number of control options open to public health authorities, and identifies how much public intervention is warranted. Depending on people’s goals, their resources, and the opportunities open to them, the behavior of some individuals may slow epidemics, while the behavior of others can speed them up (Kremer 1996; Aadland et al. 2013). If the private and social costs of disease and disease avoidance are the same, then the decisions people make in their own self-interest coincide with the decisions they would make if they were acting with the interests of society in mind. If the private and social costs of disease and disease avoidance are different—if people make private decisions that are not in the social interest—then public health authorities can use an understanding of the private decision process to incentivize people to make different decisions. In so doing, they can minimize the expected social cost of the disease and its control.
This opens up a novel set of disease management instruments aimed at confronting individuals with the external costs of their actions or compensating them for the external benefits their actions provide. Specifically, public health managers may select instruments that change the course of disease by changing contact and mixing incentives. The same costs of disease and disease avoidance that drive private contact and mixing decisions become potential points of leverage on contact and mixing behavior. If the social decision-maker is able to alter those costs through, for example, taxes, subsidies, access fees, penalties, and so on, then the social decision-maker is also able to change private behavior and disease dynamics (Francis 1997; Auld 2003; Francis 2004). For example, where tracking mechanisms allow the sale of diseased animals to be traced back to a specific hub in the supply chain, opening the responsible individuals up to legal penalties provides them with an incentive to exercise care. By increasing the private payoff to actions that confer benefits on others, it is possible to enhance the public good even if individuals act only in their private self-interest (Francis 1997; Sandler and Arce 2002).
On the first hypothesis, the effort made to avoid risk, and so disease prevalence, has been found to be increasing in the cost of disease (Mummert and Weiss 2013). There is evidence that people are willing to pay more to avoid diseases they believe to be serious, and that their willingness to pay changes as their perception of the seriousness of the disease changes. A study of the number of passengers missing previously purchased flights during the 2009 swine flu or A/H1N1 influenza epidemic used flight records, Google Trends and the World Health Organization’s FluNet data to show that concern over H1N1 accounted for a small proportion (0.34%) of missed flights during the epidemic. The authors estimated that this represented around $50 M in travel-related benefits. They noted that while this was consistent with a self-protective response to the epidemic, the timing of responses correlated poorly with FluNet data. They concluded that responses were motivated by subjective rather than objective perceptions of risk (Fenichel et al. 2013).
For animal diseases (and emerging zoonoses), it has been shown that decisions affecting the national and international movement of livestock reflect the costs and benefits of disease risk mitigation, and strongly influence the probability of spread (Keeling et al. 2001; Kilpatrick et al. 2006, 2009). Analyses of the 2001 foot and mouth disease (FMD) outbreak in the UK, and the 2004 H5N1 avian influenza outbreak in Thailand, for example, show that differences in the compensation schemes applied in each case had significant effects on the relative costs of disease and disease avoidance, and hence on the dynamics of the disease. In the UK FMD outbreak, the structure of compensation to farmers perversely reduced the cost of disease and increased the cost of disease avoidance, so discouraging disease avoidance (Davies 2002). In the Thailand H5N1 outbreak, by contrast, the government offered farmers 100% compensation for every animal killed (significantly above the compensation formally allowed under the Animal Epidemic Act), effectively reducing the private cost of disease avoidance to zero (Tiensin et al. 2005).
Because disease risk reflects both the probability of infection and the cost of infection, trade growth that reduces cost more than proportionately to the increase in the probability of infection can, paradoxically, reduce risk (Fenichel and Horan 2007a, b; Fenichel et al. 2010; Horan et al. 2011, 2013). While there have been no formal tests of this hypothesis, there is considerable empirical evidence that people trade-off the price of goods and services against the risks they pose (Lusk and Coble 2005), just as they trade-off the rate of return and risk on asset holdings (Ghysels et al. 2005).
There is less evidence that the public management of infectious human disease is sensitive to the incentive effects of changes in the private cost of disease and disease avoidance. Although the World Health Organization recognizes the cost effectiveness of economic instruments (World Health Organization 2004), applications to the control of infectious human diseases are limited. The most obvious and long standing examples are the use of subsidies to lower the private cost of vaccination (Brito et al. 1991; Geoffard and Philipson 1996, 1997; Cook et al. 2009) or vaccination and treatment (Gersovitz and Hammer 2004; Gersovitz 2011). By contrast, standard control measures such as travel interdictions or enforced quarantine are classic, and often poorly targeted, examples of command and control instruments. Measures of this sort have, in particular cases, proved to be extremely costly (Thompson et al. 2002; Webby and Webster 2003; Smith et al. 2009b; Keogh-Brown et al. 2010). In some cases, for example, mandatory controls have increased the flow of infected emigrants from the epicenter of infectious disease outbreaks, so spreading the disease to uninfected sub-populations (Mesnard and Seabright 2009; Maharaj and Kleczkowski 2012).
The use of command-and-control instruments is particularly common at the national level, where governments have the authority to implement emergency controls on subject populations (World Health Organization 2006; Stern and Markel 2009; Steelfisher et al. 2012). Interestingly, it is also the preferred approach at the international level where the control options are prescribed by two multilateral agreements, the International Health Regulations and the Sanitary and Phytosanitary Agreement, even though there is no supranational body with sovereign authority over nation states (Perrings et al. 2010a, b). While measures of this sort do not directly target the incentives facing susceptible individuals they do have incentive effects. A study of the 2009 H1N1 epidemic in Mexico, for example, concluded that the prolongation of the epidemic through a second wave was induced by the private response to social distancing measures implemented by the health authorities (Herrera-Valdez et al. 2011). Similar effects were observed in the 2007 Dengue outbreak in Taiwan (Hsieh and Chen 2009), and the 2002–2003 SARS epidemic (Chowell et al. 2004).
The question to consider is whether mandatory measures are cost effective, once the incentive effects of those measures are taken into account. The optimal control program in all cases depends on a number of factors, including the nature of disease, the size of each population, the length of the time horizon, or the discount rate applied, as well as the characteristics of the controls (Brandeau et al. 2003). This makes it difficult to generalize. We are unaware of empirical studies of the relative cost effectiveness of mandatory and incentive-based measures for the ex post control of outbreaks. It seems clear, however, that incentive-based measures are able to reduce the ex ante risk of disease more cost effectively than direct controls over the mobility of people or the movement of goods. A study of the cost effectiveness of a number of different classes of primary disease prevention (controls aimed at preventing new cases of disease) found that that measures aimed at changing the environment within which people make decisions are significantly more cost-effective than measures aimed at clinical or nonclinical interventions on individuals (Chokshi and Farley 2012). Measures aimed at changing the environment within which people make decisions include, for example, taxes designed to increase the private cost of risky behaviors. Measures aimed at individuals include, for example, quarantine or screening programs. The study showed that in terms of costs per quality-adjusted life-year the proportion of preventive measures that are cost saving is higher among environmental interventions (46%) than among clinical interventions (16%) or nonclinical, person-directed interventions (13%). Given that individual restrictions or obligations also pose more legal and ethical challenges (National Research Council 2007), this indicates that incentive-based measures may offer a significant advantage.
For plant diseases, a recent example of the use of incentives, in the form of conditional market access, concerns management of disease risk associated with international plant trade. With a 2011 amendment to the Plant Protection Act, the USDA established a new “gray list” designation available for plants known as “Not Approved Pending a Pest Risk Analysis” (NAPPRA) for species that might be pests, or serve as hosts of pests or pathogens (US Department of Agriculture-Animal and Plant Health Inspection Service 2011). This rule change made it simpler to restrict access to US markets for particular taxa of plants which pose a biological risk (Liebhold et al. 2012). Currently, the only mechanism for approving NAPPRA listings for importation is a detailed pest risk assessment (PRA) assessing the threat of pest infestation, transit, colonization, spread, and damage. In April 2013, the USDA formally proposed a further amendment that would allow US import market access for NAPPRA listings conditional on exporters’ adoption of Integrated Pest Risk Management Measures (IPRMM) (US Department of Agriculture-Animal and Plant Health Inspection Service 2013). IPRMM involves certification that sufficient phytosanitary measures are being applied from the beginning of production to the end of distribution. Market access in an IPRMM program would be particularly flexible and dynamic. Access for approved producers could be revoked if the producer failed to meet the conditions at any time (US Department of Agriculture-Animal and Plant Health Inspection Service 2013). While the US is at the forefront of the IPRMM approach, interest is global. In 2012 parties to the International Plant Protection Convention adopted a standard known as ISPM-36 which recommended and outlined the use of integrated measures to manage pest and pathogen host risk for international plant trade (International Plant Protection Convention 2012). Attempts to bring pathogen introduction risks into the Fish and Wildlife Service injurious species regulations are an effort to follow this, but so far have not been successful.
In some spheres of environmental management, command-and-control instruments are being replaced, or at least supplemented, by economic instruments designed to penalize those whose actions harm others (Stavins 2003) or to incentivize those whose actions benefit others (Kinzig et al. 2011). There are many such instruments already in use for managing invasive pests and pathogens. They include charges covering the cost of inspection and interception, excise taxes, environmental bonds, damage bonds, import deposits, restoration deposits, ballast water fees, and tradable risk permits (Eisworth and Johnson 2002; Horan et al. 2002; Olson 2006; Emerton and Howard 2008; Gren 2008). A number of these instruments also reverse the burden of proof, in that they require those whose actions are a source of risk to insure society against the consequences of their actions (Perrings et al. 2002; Keller and Perrings 2011; Barbier et al. 2013).
The potential for the use of market-based mechanisms (taxes) to correct the external costs that infected individuals impose on society in the course of an epidemic has already been demonstrated in simulation models (Goldman and Lightwood 2002; Gersovitz and Hammer 2004, 2005). Similar results have been found for the use of subsidies on the cost of vaccines (Francis 2004; Chen 2006). There is, however, scope for reducing the cost of disease avoidance in other ways. Measures that reduce the income loss from private disease avoidance, for example, can be particularly effective. Just as regulations governing physical safety in the workplace have reduced the incidence of work-related accidents, so rights to paid sick leave can reduce infectious disease risks (Aronsson et al. 2000; Skåtun 2003).
Given the pressure on public health authorities to develop more targeted and cost effective disease management strategies (Glass et al. 2006; Fenichel 2013), incentive-based disease prevention programs are increasingly attractive options. The CDC’s current HIV prevention program, for example, is focused on risk targeting, bringing a geographic specificity to prevention policies, and developing a rank ordering of policies by cost effectiveness (Centers for Disease Control and Prevention 2009). The plan explicitly aims to “Identify, develop and evaluate effective behavioral interventions and strategies” (Centers for Disease Control and Prevention 2011). This requires measuring variables at scales that allow prioritization of funding across locations and risk categories. It is recognized that where the prevalence of disease is low, people will not take as much care to limit their exposure as they do where prevalence is high, making disease eradication problematic (Aadland et al. 2013). By encouraging private individuals to make decisions that are in the social interest, incentive-based measures can counteract effects of this kind.
One other implication of the EE approach is that the measures used to monitor and predict disease risk can be broadened. In addition to prevalence measures, it becomes possible to use measures of disease risk mitigation or the drivers of disease risk mitigation. Aside from the travel data used in the H1N1 study, for example, it is possible to employ time use surveys (Zagheni et al. 2008) and home media consumption measurement by audience research firms. These have the appealing feature that a representative sample of residents is monitored continuously over time and in a consistent way across a large set of countries. Coincident with an outbreak, deviations in television viewership, for example, can provide a proxy for assessing changes in time spent at home and thus in social contacts. It is also possible to exploit the much larger data base on avoidance behavior to other sources of human health risk such as air pollution and drinking water contamination (Zivin and Neidell 2013). Beyond such measures, data on prices, sales, employment, output, exports, and imports may be as valuable for predicting epidemics as data on current disease status (Suhrcke et al. 2011).
In summary, the EE approach is opening up new options for both the prediction and management of epidemics. By improving our understanding of contact behavior the approach is strengthening capacity to project the future course of disease. By identifying the gap between the private and social cost of private disease risk mitigation, the EE approach makes it possible to induce people to behave in ways that are consistent with the public good. That is, it helps to identify both the private choices that best serve the public interest, and the incentives needed to lead people to make those choices. This opens up the prospect of more cost-effective disease control. Many governments are already committed to subsidizing vaccines. Many also use penalties to discourage importation of infected animals or plants. There is, however, scope for making more and better-informed use of instruments of this kind in the future.
This publication was made possible by grant #1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health. In addition, Rick Horan acknowledges funding from the USDA National Institute of Food and Agriculture, grant #2011-67023-30872. Marm Kilpatrick acknowledges NSF grant #81140 - 443559. Peter Daszak acknowledges the generous support of the American people through the United States Agency for International Development (USAID) Emerging Pandemic Threats PREDICT.
The contents are the responsibility of the authors and do not necessarily reflect the views of the NSF, NIGMS, USDA, USAID, or the United States Government.
- Aadland, D., Finnoff, D. & Huang, K. (2013) Syphilis Cycles. The B.E. Journal of Economic Analysis and Policy, 14, 297–348.Google Scholar
- Anderson, R. & May, R.M. (1991) Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford.Google Scholar
- Barbier, E.B., Knowler, D., Gwatipedza, J., Reichard, S.H. & Hodges, A.R. (2013) Implementing Policies to Control Invasive Plant Species. BioScience, 63, 132-138.Google Scholar
- Barrett, S. & Hoel, M. (2007) Optimal disease eradication. Environment and Development Economics, 12, 627-652.Google Scholar
- Bauch, C.T. & Earn, D.J.D. (2004) Vaccination and the theory of games. Proceedings of the National Academy of Sciences, 101, 13391-13394.Google Scholar
- Bootsma, M.C. & Ferguson, N.M. (2007) The effect of public health measures on the 1918 influenza pandemic in U.S. cities. Proceedings of the National Academy of Sciences, 104, 7588-7593.Google Scholar
- Boucekkine, R. & Laffargue, J.P. (2010) On the distributional consequences of epidemics. Journal of Economic Dynamics & Control 34, 231-245.Google Scholar
- Boulier, B.L., Satta, T.S. & Goldfarb, R.S. (2007) Vaccination Externalities. The B.E. Journal of Economic Analysis and Policy, 7, 37Google Scholar
- Brauer, F. & Castillo-Chavez, C. (2013) Mathematical Models for Communicable Diseases. Society for Industrial and Applied Mathematics, Philadelphia.Google Scholar
- Brito, D.L., Sheshinski, E. & Intriligator, M.D. (1991) Externalities and compulsary vaccinations. Journal of Public Economics, 45, 69-90.Google Scholar
- Busenberg, S. & Castillo-Chavez, C. (1991) A general solution of the problem of mixing of subpopulations and its application to risk- and age structured epidemic models for the spread of AIDS. Mathematical Medicine and Biology, 8, 1-29Google Scholar
- Centers for Disease Control and Prevention (2009) HIV Prevention in the United States: At a critical crossroads. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention.Google Scholar
- Centers for Disease Control and Prevention (2011) Strategic Plan, National Center for HIV/AIDS Prevention. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention.Google Scholar
- Chakraborty, S., Papageorgiou, C. & Sebastian, F.P. (2010) Disease, infection dynamics, and development. Journal of Monetary Economics, 57, 859-872.Google Scholar
- Clark, C.W. (1976) Mathematical Bioeconomics: the Optimal Management of Renewable Resources. John Wiley, New York, NY.Google Scholar
- Clark, C. (1979) Mathematical Models in the Economics of Renewable Resources. SIAM Review, 21, 81-99.Google Scholar
- Daszak, P. (2012) Anatomy of a pandemic. The Lancet, 380, 1883-1884.Google Scholar
- Eisworth, M.E. & Johnson, W.S. (2002) Managing nonindigenous invasive species: insights from dynamic analysis. Environmental and Resource Economics, 23, 319-342.Google Scholar
- Emerton, L. & Howard, G. (2008) A Toolkit for the Economic Analysis of Invasive Species. Global Invasive Species Programme, Nairobi.Google Scholar
- Fenichel, E. & Horan, R. (2007a) Gender-based harvesting in wildlife disease management. American Journal of Agricultural Economics, 89, 904-920.Google Scholar
- Fenichel, E. & Horan, R. (2007b) Jointly-determined ecological thresholds and economics trade-offs in wildlife disease management. Natural Resources Modeling, 20, 511-547.Google Scholar
- Fenichel, E.P. & Wang, X. (2013) The mechanism and phenomenon of adaptive human behavior during an epidemic and the role of information. In: Modeling the Interplay between Human Behavior and Spread of Infectious Diseases, A. D’onofrio, P. Manfredi (editors), Berlin: Springer, pp 153–170.Google Scholar
- Fenichel, E.P., Castillo-Chavez, C., Ceddia, M.G., Chowell, G., Gonzalez Parra, P.A., Hickling, G.J., Holloway, G., Horan, R., Morin, B., Perrings, C., Springborn, M., Velazquez, L. & Villalobos, C. (2011) Adaptive human behavior in epidemiological models. Proceedings of the National Academy of Sciences, 108, 6306-6311.Google Scholar
- Francis, P.J. (1997) Dynamic epidemiology and the market for vaccinations. Journal of Public Economics, 63, 383-406.Google Scholar
- Francis, P.J. (2004) Optimal tax/subsidy combinations for the flu season. Journal of Economic Dynamics & Control, 28, 2037-2054.Google Scholar
- Funk, S., Gilad, E., Watkins, C. & Jansen, V. (2009) The spread of awareness and its impact on epidemic outbreaks. Proc National Acad Sci, 106, 6872 - 6877.Google Scholar
- Galeotti, A. & Rogers, B.W. (2013) Strategic immunization and group structure. American Economic Journal: Microeconomics, 5, 1-32.Google Scholar
- Geoffard, P.-Y. & Philipson, T. (1996) Rational epidemics and their public control. International Economic Review, 37, 603-624.Google Scholar
- Geoffard, P.-Y. & Philipson, T. (1997) Disease Eradication: Private versus Public Vaccination. American Economic Review, 87, 222-230.Google Scholar
- Gersovitz, M. (2011) The economics of infection control. Annual Review of Resource Economics, 3, 277-296.Google Scholar
- Gersovitz, M., and J.S.Hammer (2003) Infectious diseases, public policy, and the marriage of economics and epidemiology. The World Bank Research Observer 18, 129-157.Google Scholar
- Gersovitz, M. & Hammer, J.S. (2004) The economical control of infectious diseases. The Economic Journal 114, 1-27.Google Scholar
- Gersovitz, M. & Hammer, J.S. (2005) Tax/Subsidy Policies Toward Vector-Borne Infectious Diseases. Journal of Public Economics, 89, 647-674.Google Scholar
- Ghysels, E., Santa-Clara, P. & Valkanov, R. (2005) There is a risk-return trade-off after all. Journal of Financial Economics, 76, 509-548.Google Scholar
- Glass, R., Glass, L., Beyeler, W. & Min, H. (2006) Targeted Social Distancing Design for Pandemic Influenza. Emerging Infectious Diseases, 12, 3017 - 3026.Google Scholar
- Goldman, S. & Lightwood, J. (2002) Cost Optimization in the SIS Model of Infectious Disease with Treatment. Topics in Economic Analysis and Policy, 2, 1-22.Google Scholar
- Gramig BM, Horan, RD (2010) Jointly-determined livestock disease dynamics and decentralized economic behavior (in review) Google Scholar
- Gren, I.-M. (2008) Economics of alien invasive species management - choices of targets and policies. Boreal Environmental Research, 13, 17-32.Google Scholar
- Grossman, M. (1972) On the concept of health capital and demand for health. Journal of Political Economy, 80, 223-255.Google Scholar
- Guan, Y., Zheng, B.J., He, Y.Q., Liu, X.L., Zhuang, Z.X., Cheung, C.L., Luo, S.W., Li, P.H., Zhang, L.J., Guan, Y.J., Butt, K.M., Wong, K.L., Chan, K.W., Lim, W., Shortridge, K.F., Yuen, K.Y., Peiris, J.S.M. & Poon, L.L.M. (2003) Isolation and characterization of viruses related to the SARS coronavirus from animals in Southern China. Science, 302, 276-278.PubMedGoogle Scholar
- Herrera-Valdez, M.A., Cruz-Aponte, M. & Castillo-Chavez, C. (2011) Multiple outbreaks for the same pandemic: Local transportation and social distancing explain the different “Waves” of A-H1N1PDM cases observed in México during 2009. Mathematical Biosciences and Engineering, 8, 21-48.PubMedGoogle Scholar
- Horan, R. & Wolf, C. (2005) The economics of managing infectious wildlife disease. American Journal of Agricultural Economics, 87, 537-551.Google Scholar
- Horan, R.D., Perrings, C., Lupi, F. & Bulte, E.H. (2002) Biological pollution prevention strategies under ignorance: The case of invasive species. American Journal of Agricultural Economics, 84, 1303-1310.Google Scholar
- Horan, R.D., Fenichel, E., Wolf, C.A. & Gramig, B.M. (2010) Managing infectious animal disease systems. Annual Review of Resource Economics, 2, 101-124.Google Scholar
- Horan, R.D., Fenichel, E.P. & Melstrom, R.T. (2011) Wildlife Disease Bioeconomics. International Review of Environmental and Resource Economics, 5, 23-61.Google Scholar
- Horan RD, Fenichel EP, Finnoff D, Wolf CA (2013) Managing Epidemiological Risks Through Trade. Working Paper. East Lansing: Department of Agricultural, Food, and Resource Economics, Michigan State UniversityGoogle Scholar
- Hsieh, Y.-H. & Chen, C. (2009) Turning points, reproduction number, and impact of climatological events for multi-wave dengue outbreaks. Trop Med Int Health., 14, 628 - 638.Google Scholar
- Hufnagel, L., Brockmann, D. & Geisel, T. (2004) Forecast and control of epidemics in a globalized world. Proceedings of the National Academy of Sceinces, 101, 15124-15129.Google Scholar
- International Plant Protection Convention (2012) ISPM 36: Integrated Measures for Plants for Planting. http://www.ippc.int/publications/integrated-measures-plants-planting. Accessed 2012
- Keeling, M.J., Woolhouse, M.E.J., Shaw, D.J., Matthews, L., Chase-Topping, M., Haydon, D.T., Cornell, S.J., Kappey, J., Wilesmith, J. & Grenfell, B.T. (2001) Dynamics of the 2001 UK Foot and Mouth Epidemic: Stochastic Dispersal in a Heterogeneous Landscape. Science, 294, 813-817.PubMedGoogle Scholar
- Keller, R.P. & Perrings, C. (2011) International Policy Options for Reducing the Environmental Impacts of Invasive Species. Bioscience, 61, 1005-1012.Google Scholar
- Kermack, W.O. & Mckendrick, A.G. (1929) Contributions to the mathematical theory of epidemics, part 1. Proceedings of the Royal Society of London, Series A, 115, 700-721.Google Scholar
- Kilpatrick, A.M. & Randolph, S.E. (2012) Drivers, dynamics, and control of emerging vector-borne zoonotic diseases. The Lancet, 380, 1946-1955.Google Scholar
- Kilpatrick, A.M., Gillin, C.M. & Daszak, P. (2009) Wildlife-livestock conflict: the risk of pathogen transmission from bison to cattle outside Yellowstone National Park. Journal of Applied Ecology, 46, 476-485.Google Scholar
- Klein, E., Laxminarayan, R., Smith, D.L. & Gilligan, C.A. (2007) Economic incentives and mathematical models of disease. Environment and Development Economics, 12, 707-732.Google Scholar
- Kremer, M. (1996) Integrating behavioral choice into epidemiological models of AIDS. Quarterly Journal of Economics, 111, 549-573.Google Scholar
- Lanciotti RS, Kerst AJ, Nasci RS, Godsey MS, Mitchell CJ, Savage HM, Komar N, Panella NA, Allen BC, Volpe KE, Davis BS, Roehrig JT (2000) Rapid detection of West Nile virus from human clinical specimens, field-collected mosquitoes, and avian samples by a TaqMan reverse transcriptase-PCR assay. Journal of Clinical Microbiology, 38, 4066–4071.Google Scholar
- Liebhold, A.M., Brockerhoff, E.G., Garrett, L.J., Parke, J.L. & Britton, K.O. (2012) Live plant imports: the major pathway for forest insect and pathogen invasions of the US. Frontiers in Ecology and the Environment. Frontiers in Ecology and the Environment, 10, 135-143.Google Scholar
- Lusk, J.L. & Coble, K.H. (2005) Risk Perceptions, Risk Preference, and Acceptance of Risky Food. American Journal of Agricultural Economics, 87, 393-405.Google Scholar
- McNeill, W.H. (1977) Plagues and People. Anchor Books, New York.Google Scholar
- Mesnard, A. & Seabright, P. (2009) Escaping epidemics through migration? Quarantine measures under incomplete information about infection risk. Journal of Public Economics, 93, 931-938.Google Scholar
- National Research Council (2007) Ethical and Legal Considerations in Mitigating Pandemic Disease: Workshop Summary. Washington, DC: The National Academies Press.Google Scholar
- Olson, L.J. (2006) The economics of terrestrial invasive species: a review of the literature. Review of Agricultural and Resource Economics, 35, 178-194.Google Scholar
- Perrings, C. (2014) Our Uncommon Heritage: Biodiversity, Ecosystem Services and Human Wellbeing. Cambridge University Press, Cambridge.Google Scholar
- Perrings C, Williamson M, Barbier EB, Delfino D, Dalmazzone S, Shogren J, Simmons P, Watkinson A (2002) Biological invasion risks and the public good: an economic perspective. Conservation Ecology, 6, 1. http://www.consecol.org/vol6/iss1/art1
- Perrings C, Mooney H, Lonsdale M, Burgeil S (2010a) Globalization and invasive species: policy and management options. In: Bioinvasions and Globalization: Ecology, Economics, Management and Policy, C Perrings, H Mooney, M Williamson (editors), Oxford: Oxford University Press, pp. 235-250.Google Scholar
- Philipson T (2000) Economic epidemiology and infectious diseases. In: Handbook of Health Economics, JC Anthony, PN Joseph (editors), New York: Elsevier, pp. 1761-1799.Google Scholar
- Purse, B.V., Brown, H.E., Harrup, L., Mertens, P.P. & Rogers, D.J. (2008) Invasion of bluetongue and other orbivirus infections into Europe: the role of biological and climatic processes. Scientific and Technical Review International Office of Epizootics, 27, 427-442.Google Scholar
- Rweyemamu, M.M. & Astudillo, V.M. (2002) Global perspective for foot and mouth disease control. Revue Scientifique Et Technique De L Office International Des Epizooties, 21, 765-773.Google Scholar
- Sandler, T. & Arce M, D.G. (2002) A conceptual framework for understanding global and transnational public goods for health. Fiscal Studies, 23, 195-222.Google Scholar
- Springborn, M., Costello, C. & Ferrier, P. (2010) Optimal random exploration for trade-related non-indigenous. In: Bioinvasions and Globalization: Ecology, Economics, Management, and Policy, C. Perrings, H. Mooney, M. Williamson (editors), Oxford: Oxford University Press, pp. 127-144.Google Scholar
- Stavins, R.N. (2003) Experience with market-based environmental policy instruments. In: Handbook of Environmental Economics, K.-G. Mäler, J.R. Vincent (editors), Amsterdam: Elsevier, pp. 355-435.Google Scholar
- Suhrcke, M., Stuckler, D., Suk, J.E., Desai, M., Senek, M., McKee, M., Tsolova, S., Basu, S., Abubakar, I., Hunter, P., Rechel, B. & Semenza, J.C. (2011) The Impact of Economic Crises on Communicable Disease Transmission and Control: A Systematic Review of the Evidence. PLoS ONE, 6, e20724.PubMedCentralPubMedGoogle Scholar
- Tatem, A.J., Hay, S.S. & Rogers, D.J. (2006b) Global traffic and disease vector dispersal. Proceedings of the National Academy of Sciences 103, 6242–6247.Google Scholar
- Thompson, D., Muriel, P., Russell, D., Osborne, P., Bromley, A., Rowland, M., Creigh-Tyte, S. & Brown, C. (2002) Economic costs of the foot and mouth disease outbreak in the United Kingdom in 2001. Scientific and Technical Review International Office of Epizootics, 21, 675-87.Google Scholar
- Tiensin, T., Chaitaweesub, P., Songserm, T., Chaisingh, A., Hoonsuwan, W., Buranathai, C., Parakamawongsa, T., Premashthira, S., Amonsin, A., Gilbert, M., Nielen, M. & Stegeman, A. (2005) Highly pathogenic avian influenza H5N1, Thailand, 2004. Emerging infectious diseases, 11, 1664-1672.PubMedCentralPubMedGoogle Scholar
- US Department of Agriculture-Animal and Plant Health Inspection Service (2011) Importation of plants for planting: establishment of category of plants for planting not authorized for importation pending a pest risk analysis. Federal Register, 76, 31172-31210.Google Scholar
- US Department of Agriculture-Animal and Plant Health Inspection Service (2013) Restructuring of Regulations on the Importation of Plants for Planting. Federal Register, 78, 24634-24663.Google Scholar
- World Health Organization (2004) Health & environment: tools for effective decision-making The WHO-UNEP Health and Environment Linkages Initiative (HELI) Review of initial findings. Geneva: World Health Organization and United Nations Environment Programme.Google Scholar
- World Health Organization (2006) Nonpharmaceutical interventions for pandemic influenza, national and community measures. Emerging Infectious Diseases, 12, 88-94.Google Scholar
- Yoo B-K, Kasajima M, Bhattacharya J (2010) Public Avoidance and the Epidemiology of Novel H1N1 Influenza A Working Paper 15752. Cambridge, MA: National Bureau of Economic ResearchGoogle Scholar
- Zivin, J.G. & Neidell, M. (2013) Environment, Health, and Human Capital. Journal of Economic Literature, 51, 689-730.Google Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.