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Looking to the Future: Potential Next Steps for Using Economics to Manage Microbial Threats

Session III, part B, of the workshop considered potential next steps for applying economics to manage microbial threats. The session was moderated by Suerie Moon, director of research at the Global Health Centre of the Graduate Institute of International and Development Studies, Geneva. During the session, workshop organizers asked forum members, speakers, and attendees to break into three groups organized around three themes. A member of the Forum on Microbial Threats or a speaker of the workshop was assigned to moderate and report on the discussions that emerged from each breakout group. Peter Daszak, president of EcoHealth Alliance, reported on modeling the economics of emerging infectious diseases (EIDs) (group 1). Ed Whiting, chief of staff and director of policy at the Wellcome Trust, focused on creating a sustainable economic model to stimulate research and development (R&D) of antimicrobials (group 2). Anas El Turabi, Frank Knox fellow in health policy at Harvard University, reported on incentivizing national governments to invest in preparedness (group 3). This chapter summarizes some of the challenges and suggested actions that emerged from the group dialogue, including reflections by workshop participants during the final synthesis discussion of the workshop. The ideas presented herein should not be construed as collective conclusions or recommendations, and do not necessarily represent the views of the workshop participants, the forum members, or the National Academies of Sciences, Engineering, and Medicine.

MODELING THE ECONOMICS OF EMERGING INFECTIOUS DISEASES

Peter Daszak, president of EcoHealth Alliance, reported for the breakout group on modeling the economics of EIDs. He stated that the group discussed approaches to improve these types of models through incorporating behavioral responses to fear associated with outbreaks as well as filling in knowledge gaps in EIDs, and communicating about models more effectively.

Potential Next Steps to Improve Economic Models of Emerging Infectious Diseases: Incorporating Behavioral Responses to Fear

Daszak stated that many members of the group spoke about the knowledge gaps in understanding the economics of fear associated with outbreaks. Wherever on the spectrum of fear—from excess to lack of fear, he said, different levels of fear at different population levels, including the household, national, and global levels, at different times can affect the course and the costs of an outbreak. The level of fear may also depend on the type of disease, as experienced in Ebola in the United States (high levels of fear) versus Zika (low levels of fear). Therefore, collecting data to gain a deeper understanding of these factors is crucial, he said.

While gathering such data on fear would be helpful, members of the group also recognized the challenge to incorporate this kind of data into models. Some participants pointed to the challenge of synergizing qualitative and social science data into predictive infectious disease models and to model social and biological systems at the same time. He added that data on human behavior, which tend to be context specific, are difficult to incorporate into larger scale, population-level models. For example, not only can individual responses to a single outbreak differ within the same region, but also data are specific to different types of outbreaks (e.g., short-term versus long-term endemic outbreaks and antimicrobial resistance [AMR]). Many members discussed that a key challenge is to identify which existing datasets and findings could be generalizable for global models.

Some types of models, Daszak said, may be more amenable to incorporate such behavioral data. Members of the group discussed the possibility of using game theory and stochastic dynamic approaches, which could incorporate the effect of fear and trade-offs. Other participants thought that behavioral economics could illuminate individual preferences with respect to risk, time, and altruism associated with an outbreak. System dynamics models was another type that a few members thought could provide an intermediate approach between behavior-rich analyses and oversimplified economic models that lack behavioral parameters.

Daszak explained that several members of the group identified stakeholders who should be involved in developing these types of economic models to ensure the behavior component is included. These stakeholders include people with expertise in public health and outbreak response, insurance, economics and finance, trade and travel, and social sciences. Specifically, he noted that outbreak responders could be involved in surveys to evaluate how fear changes over the course of the epidemic. He stated that social scientists could be particularly helpful if they are able to determine what data are generalizable for use in models from the context-specific datasets they usually collect.

Other Potential Considerations: Filling in Additional Data Gaps and Effectively Communicating About Models

Besides the fear factor, Daszak reported on other ways to improve these types of models. He said that the group discussed the lack of knowledge on the geography of risk to identify disease reservoirs and regions with potential for EIDs. Daszak noted that geographical disease data can shift rapidly when there are changes in the environment. Some of the group members argued that the gap in geographical data can make it difficult to set up clinical trials to control outbreaks at the right place and time. Additionally, Daszak reported that there is a knowledge gap in understanding the economic damages of EIDs. Some participants suggested that more objectivity and clarity is needed with regard to cost measurements that seem to range from millions to trillions of dollars. Daszak also pointed out to the importance of incorporating the issue of temporality more carefully into models. To better understand infectious diseases that are slow moving or infrequent, modelers could possibly learn and adapt their models from other fields such as climate change, he said.

Finally, Daszak shared the group’s discussions on the difficulty of communicating the results of models to policy makers. He noted that while some policy makers might rely too much on models without understanding their underlying uncertainty, others might suffer from “model fatigue,” and believe that models lack value. Daszak emphasized that the equations underlying models could be shared, but the models should then be described to decision makers in a way that better highlights both their usefulness and uncertainty.

STIMULATING RESEARCH AND DEVELOPMENT FOR ANTIMICROBIALS

Ed Whiting, chief of staff and director of policy at the Wellcome Trust, shared insights from the breakout group that discussed how to stimulate

R&D of antimicrobials. Without incentives and resolving the market failure issues, many companies are likely to withdraw from antimicrobials over the years. With the aim of increasing the number of available antimicrobials to fight infectious disease threats, the group discussed approaches to spur drug development including issues related to incentives, and other considerations including filling data gaps and strengthening coordination between national and global level efforts.

Potential Next Steps to Stimulate Antimicrobial Development: Addressing Challenges of Incentive Design

Whiting shared that many of the participants recognized the need for incentives, particularly pull incentives to accelerate antimicrobial development. He suggested, however, that the specifics on what pull incentives might entail continue to be nebulous. Some participants acknowledged that even though the specific incentives needed by different types of companies and national markets would be distinct, a general degree of consensus on what the incentives may look like would be useful to push this field forward. Whiting noted that the work of the International Federation of Pharmaceutical Manufacturers & Associations has moved the industry closer toward a consensus on this issue.

In terms of specific incentives, the group debated the use of reimbursement incentives and market reward incentives. Many of the members of the group thought that while reimbursement reforms would be helpful, they are not sufficient. Furthermore, a few participants highlighted that cost-plus models may be a dead end, as they do not take into account the riskiness of antimicrobial drug development.¹ Whiting said that many members believed that market entry awards, paid for by transferable exclusivity and intellectual property–based incentives, are critical as well. With this recognition, a member noted that legislation had been recently introduced in the United States to provide for a 1-year transferable exclusivity voucher to incentivize the development of new antibiotics, vaccines and other medical products that could mitigate the effects of AMR.²

To accelerate progress in this area, Whiting noted the need to help policy makers make well-informed decisions and boost political will through transparent information sharing. Some members pointed out that policy makers are confronted with challenging questions and face difficulty in

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¹ A cost-plus model makes products from R&D available at the cost of manufacture plus a small margin to try to ensure the sustainable production of the products.

² As of June 2018, U.S. Representatives John Shimkus (R-IL) and Tony Cardenas (D-CA) introduced the Re-Valuing Antimicrobial Products Act of 2018 (REVAMP Act), which also includes provisions on stewardship and the development of an innovation fund for unmet antimicrobial R&D needs.

understanding the complex and multistage R&D process (e.g., financial flows, intellectual property flows, and licensing arrangements), which stalls progress in the discussion on incentives. Drug noninferiority studies is a particularly challenging issue that is debated among the companies and policy makers. To approve new antimicrobial drugs, he said policy makers have to grapple with tough questions:

Policy makers must balance the cost of higher drug prices against other AMR interventions, Whiting added, underscoring that policy makers as well as decision makers in private-sector companies work with “a finite pie.” That is, if the pie is cut one way and makes one part bigger, another part of the pie will inevitably be smaller; therefore, equipping decision makers with information to make the best choices is a critical yet grand, “human” challenge, said Whiting.

To better inform policy makers, a member suggested that companies could commit to providing the profit margins they need and outline what the market rewards need to be in order to dispel concerns about unjustified rewards. Another member suggested that economists need to be more involved in building the case on why governments should invest in incentives. Others pointed out that this issue should be seen as a risk-shared benefit and not count only on governments and the industry to bear the burden to solve the problem.

Other Potential Considerations: Filling in Data Gaps and Coordinating National and Global Efforts

Whiting reported that the group discussed data gaps in this field. Some members argued for the need of real-time monitoring for the development of new antimicrobials, incentives, and degree of access to antimicrobials. Others called for the incorporation of patient voices in filling knowledge gaps related to AMR. He specified that adding a human face to the issue could bring more trust and empathy to the debate over incentives. Whiting reported that a few group members also suggested that experiences from outside the human health sector could direct efforts aimed at understanding what further measures and analyses could be examined to enhance conservation of currently available drugs. He noted that the plant community has demonstrated lessons related to the preservation of fungicides,

and the agricultural community has led efforts to reduce antimicrobial use for animal husbandry.

As more data are identified, he said, some members recognized the importance of data sharing. He clarified that sharing means not only the cost of research and data, but also what the research and interventions are achieving and not achieving, and the new products that are in the pipeline. Whiting noted that The Pew Charitable Trusts has helped to partially fill these gaps by tracking the pipeline of antimicrobials in development. This type of sharing and real-time monitoring could build transparency and trust and positively affect other global health challenges, he said.

Finally, he emphasized the need for better coordination between national and global efforts on AMR. Some group members thought there needs to be more opportunities and support for countries to engage in national experiments and trials in R&D of antimicrobials that would allow them to succeed in this arena. Whiting also noted the importance of documenting and linking these national experiments to global efforts in AMR. He stated:

Whiting suggested that such organizations as The Global Fund to Fight AIDS, Tuberculosis and Malaria and the United Nations Children’s Fund could play a role in ensuring access of antimicrobials to populations who need it the most.

INCENTIVIZING NATIONAL GOVERNMENTS TO INVEST IN PREPAREDNESS

Anas El Turabi, Frank Knox fellow in health policy at Harvard University, reported for the breakout group focused on incentivizing national governments to invest in preparedness. El Turabi stated that the group discussed specific strategies including synergizing preparedness investments with existing health expenditures and connecting disease risk to foreign direct investment. Crosscutting considerations were also discussed including enhancing local capacity and political will.

Potential Next Steps to Invest in Outbreak Preparedness: Synergizing with Existing Health Expenditures and Linking Disease Risk to Foreign Direct Investment

El Turabi reported that many of the group members recognized the current language on outbreak preparedness is not working to convince governments to invest in this issue. Some members pointed out that governments still do not understand the actual risks of infectious disease outbreaks, and even if they do, they recognize that preparedness is a long-term issue that may not coincide with political terms and cycles and is not worth investing in. Preparedness is a long-term issue that requires short-term investments, El Turabi stated, so unless this fact is not made more salient to governments and the development partners, preparedness efforts will continue to be inadequate.

To convince policy makers, many group members discussed that outbreak preparedness could be connected to investments that are needed in the short term. Specifically, technical assessments on outbreak preparedness could align with short-term, local priority needs, El Turabi said. Rather than start with a blank piece of paper to understand the future cost of pandemic and pandemic preparedness investments, some participants discussed that existing initiatives that already invest in disease control activities could be leveraged. He noted that the group discussed if it were possible to calculate the added spend, or the built-on expenditure that would be needed for an existing health program to also start meeting some of the specific day-to-day preparedness functions required by the Joint External Evaluation (JEE). As an example, he noted that The Global Fund to Fight AIDS, Tuberculosis and Malaria could make a variety of preparedness investments that synergize with its disease-specific programs. Building on existing health expenditure streams would require a different type of economic and cost analysis than what has been previously done, but he said it could be an important shift to encouraging governments to invest in pandemic preparedness.

Another strategy raised by some participants involves the private sector, which can be substantially exposed to economic risks from infectious disease threats yet are often excluded as partners from these conversations. A few group members discussed that connecting the effects of outbreaks on commercial and investment activity, particularly foreign direct investment and country credit rating assessments, could motivate governments to pay more attention to outbreak preparedness to protect their self-interests. This could also mobilize the private sector as a partner, holding governments who do not invest in preparedness accountable for their inaction. One member suggested that a potential avenue for analysis could be to assess the response in foreign direct investments in Paraguay, as a result of it declaring

malaria elimination. However, several members of the group cautioned that this tactic could work for countries where foreign direct investment is a major political lever; thus, this may not work for low-income countries. For these situations, the Global Heath Security Index,³ which is in the process of being created by the Nuclear Threat Initiative, Johns Hopkins Center for Health Security, and Economist Intelligence Unit, might be particularly helpful, El Turabi said.

Other Potential Considerations: Strengthening Local Capacity and Political Will

El Turabi highlighted crosscutting considerations that could incentivize national-level preparedness. Many members called for more microlevel data at the local level to refine the technical assessments of outbreak preparedness and help local and country decision makers evaluate preparedness investments. A few members also called for the need for better data to understand the logic underlying how policy makers perceive issues and make choices to help frame the argument for the economics of EIDs and outbreaks at the local and national level. He also commented on the need for greater local capacity development and the importance of local and national government partnerships to ensure sustainability of preparedness efforts. He stated that the recent establishment of the Africa Centers for Disease Control and Prevention was a positive step forward in the development of regional public health capacity. He hoped these types of investments in public health infrastructure also allow the capacity to perform economic analyses.

Finally, El Turabi emphasized the importance of political will. Some group participants had raised the need to capitalize on the political interest that typically follows outbreaks to build sustainable solutions that become engrained in institutions in a way that outlives political leadership. As a success story, a group member cited the severe acute respiratory syndrome epidemic in China, which was followed by a variety of improved surveillance initiatives and sustained workforce changes that conferred long-term, broader health benefits (Braden et al., 2013). El Turabi concluded:

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³ The Global Health Security Index will assess countries’ capabilities to prevent, detect, and respond to high-consequence biological events. This index will rely on publicly available data and be measured by an independent entity. The first Global Health Security Index is expected to be released in 2019.

SYNTHESIS AND GENERAL DISCUSSION

After the panelists reported on the discussions that emerged from the breakout groups, several workshop participants provided reactionary comments during the final discussion of the workshop. The final discussion focused on three areas: communicating about and building models, filling in data gaps, and addressing issues related to R&D of drugs to counter microbial threats.

First focusing the discussion on communicating about models, Jay Siegel, former head of scientific strategy and policy for Johnson & Johnson, agreed with Daszak’s summary on the challenges of presenting models, especially to avoid the dual risks of overconfidence in and underacceptance of economic models among decision makers. From Siegel’s perspective, it is not only important to communicate uncertainty in models in a way that users understand, but also to convey the sensitivities around changing assumptions in models. He argued that it is key to understand the assumptions driving a model. A shift in assumptions can lead to minor or massive changes in results, so Siegel said communicating those would help decision makers interpret results more accurately and increase their confidence in the model’s utility.

Carlos Castillo-Chavez, professor of mathematical biology at Arizona State University, also illustrated the importance of communicating models, even within research communities, with an example of Nobel Laureate Ronald Ross’s discoveries on mosquito control effects on malaria’s incidence at the population level. Ross had devised conceptual models that he shared in his publication’s appendix, detailing his questions, descriptions, conclusions and their underlying equations, so that other researchers and the broader community could learn from those models (Ross, 1915). As more types of models are being applied to the field of public health, Castillo-Chavez said this type of communication is critical.

Martin Meltzer, senior economist and distinguished consultant at the U.S. Centers for Disease Control and Prevention, elaborated on communicating realistic expectations of model results and the role of proxies. In communicating with the multidisciplinary stakeholders involved in infectious disease response and pandemic preparedness, Meltzer asserted that simple models could be better to avoid creating “black boxes” of information. He noted that although a simple model would not capture all of the social interactions and utilities that policy makers in particular are interested in capturing, some proxies for human behavior, such as compliance to public health measures, could be programmed as a variable changing over time in a simple model, while capturing multiple aspects of behavior. Meltzer posited that the value of models to public health policy is not from actual number results but rather from identifying which variables will produce changes that decision makers should consider priorities. Because pub-

lic health responses can take broad approaches, he argued that improved accuracy of models do not necessarily improve policy, while getting a better ballpark view may be more important for improving models as prioritization tools for decision making. He emphasized that models should be used as a teaching tool.

Castillo-Chavez agreed that models can be used for teaching, but he added they can also reveal a way of understanding science, whether that means to understand the interactions of macro- or micro-level phenomenon. As Meltzer observed, Castillo-Chavez also pointed to the problem of thinking about models as a direct reflection of reality. Models that aim to do that tend to become complicated, and can be more complicated than reality, Castillo-Chavez said; consequently, he added, they tend to not necessarily increase understanding of the real world. He reiterated that models have to be tied to specific questions. He said, “There is no model that is right, but it can help us understand a question. In particular, it adds understanding or generates new hypotheses.”

El Turabi also emphasized that models should be fitted for their purposes, whether to predict the probabilities of future outcomes or inform policy decisions, and should focus on actual decision-making functions and needs. This requires concerted efforts to better understand the “customers” of these models, he noted, which include decision makers who allocate resources to manage microbial threats. He said that researchers need to understand the customers’ inputs in the decision-making process and produce models that allow for analyses that will influence those inputs.

Daszak highlighted that there is a range of customers and vested interests that modelers need to tailor to. In global health, he said, customers of models can run the gamut of governments, drug companies, and insurance companies, among others. While there are a range of customers, Daszak posited that decision makers are the ultimate end users of these types of models, but modelers in academia might be developing a model to an interesting question that is not the exact question that the ultimate decision maker needs to have addressed. This mismatch, he said, may result in a problem because the decision maker wants to make decisions based on that model, which may not be appropriate. To avoid this from happening, he suggested that customers should engage with modelers to ensure that the appropriate models are developed. Daszak specified that customers could prepare requests for proposals to bring in modelers who can do work specific to their questions. He added that it is necessary for modelers to work with the customer in an iterative process that allows adaptation and refinement of models, in order to adequately answer key questions asked by the customers.

Mukesh Chawla, advisor for health, population, and nutrition at the World Bank, pointed to his experience managing their Pandemic Emergency

Financing Facility (PEF),⁴ where he argued that precision in modeling is, in fact, crucial in certain situations, particularly for funding decisions. Chawla explained the facility holds $500 million, and when a qualifying outbreak reaches a threshold set by the World Bank, the insurance is triggered, and he is responsible for disbursing funds to resource-constrained countries based on certain formulas and parameters for disbursement. He illustrated the need for precision for PEF by laying out a case for modeling the probability of the event reaching a particular threshold, such as 100 deaths from Lassa fever. He said the modelers have to figure out how to model the probability that there would be Lassa fever anywhere in the world in the next year that gets to the level of 100 deaths; this is multiplied by the expected payout in order to arrive at the premium. Thus, a lack of precision by 0.1 percent probability can translate to multiples of millions of dollars at stake, according to Chawla. He reflected that to develop PEF, he was struck by the lack of development and accessibility of these models, with the exception of influenza models. He said, however, that the World Bank has been trying to incubate this kind of work and hoped that universities and development partners take the lead on socializing and demystifying these models for pandemic response, possibly mirroring how the automobile insurance industry has demystified the calculations for the probability of an individual’s next automobile collision.

Anna Vassall, professor of health economics at the London School of Hygiene & Tropical Medicine, emphasized the importance of building capacity in mathematical modeling and health economics in low- and middle-income countries and remarked that not enough progress has been made in the past 20 years. She said the main challenge is that while efforts to build capacity are occurring, methods for models continue to change, and most modeling, which is still done in European universities, will be rolled out to countries as user-friendly models that ultimately undermine local capacity. Vassall urged the global health community to rethink their approach to modeling and learn from the mistakes from the health economics field. El Turabi concurred with Vassall on the risk of tools used by local workforces becoming irrelevant by the time that capacity is functional. El Turabi called for programs that cultivate more health economists and computational data scientists specifically for pandemic security and become mainstream in existing development activities.

The discussion transitioned to the need for more data, with El Turabi mentioning more efforts are needed to collect more microlevel data to make

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⁴ PEF is a quick disbursing financing mechanism that provides surge funds to enable a rapid response to a major infectious disease outbreak. For more information on PEF, see http://www.worldbank.org/en/topic/pandemics/brief/pandemic-emergency-facility-frequently-askedquestions (accessed August 16, 2018).

better economic cases to governments. He suggested the need to think through how to leverage and pull together existing datasets. The World Bank gathers a wealth of granular data, he added, but researchers could curate the data better and make the data more accessible to groups that are engaging in local level analyses. El Turabi also highlighted the lack of empirical data on causal relationships among investments, trade outcomes, and disease activity more broadly.

Nita Madhav, vice president of data science at Metabiota, focused on the specific gap of analyses in low-frequency, high-consequence events. She noted that analyses of infectious disease events are easier to conduct when there is data to inform it, such as for high-frequency events like influenza. For high-frequency events, she said there tends to be existing datasets where the disease is well characterized, especially in well-resourced areas and would encourage more types of analyses to be done leveraging the data. However, low-frequency, high-consequence events such as novel coronavirus outbreaks, which have occurred twice in the past 15 years, rely heavily on assumptions and limited datasets, Madhav said. Thus, to model these types of events, she stated that modelers have to make general assumptions and make the most of the scant data that are out there until more data are collected.

Daszak agreed on the insufficient data and weak analyses for sporadic disease outbreaks and mentioned that the response for emerging diseases is not focused on understanding in depth the drivers of its emergence, geographical origins, and triggers at various points of disease spillover that may lead to global spread—all of which have different parameters involved in modeling. Daszak said that there are lessons to be learned from more common individual spillover events, which are generally poorly understood but ultimately feasible for data collection. He suggested for groups to gather more data on emerging diseases in order to stop them before they reach pandemic potential, not only in other countries, but also in the United States where the focus can be on a better understanding of where foodborne infections and drug-resistant strains originate, for policy makers to allocate resources accordingly. Daszak emphasized that next steps should center on gathering different types of data to support modeling—both disease data on individual spillover events that will inform different scale outbreaks, and social and behavioral science data that can be generalized using appropriate proxies.

The discussion ended with a conversation about the economics and costing issues of medical products to counter infectious diseases. Vassall raised ongoing challenges she has observed with predicting the market price of tuberculosis (TB) drugs. To predict the market price, she said several difficult questions need to be addressed. For example, how might a new drug or regimen affect the health system costs, and how do these entities

interact with one another? How does spending on a new drug affect spending on other areas of health improvement? What is the opportunity cost of investing in TB drugs and forgoing other health interventions? And to what extent should other complementary health interventions be developed, whether that means drug susceptibility testing or the introduction of a new vaccine, and how does that affect drug pricing commitments? Vassall noted that there are not enough cost data yet to robustly model these questions.

Whiting commented on the role of industry in the world of AMR. He hoped that the pharmaceutical industry continues to generate and refine its consensus on the resources needed to develop new antimicrobials and make this consensus as public as possible. In doing so, he noted industry stakeholders and policy makers might have a better understanding of the decisions that need to be made. Whiting also added the need to have granular analysis on the choices decision makers face in order to provide them with more detailed guidance. Finally, he suggested that more information is needed in the areas of antimicrobial stewardship and access initiatives. He acknowledged the growing literature and community of practice in these areas and believed that it would help generate new solutions about the economic case of antimicrobials in the future.

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