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Financing Vaccines for Global Health Security

March 2020

DOI:10.1101/2020.03.20.20039966

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Recent outbreaks of infectious pathogens such as Zika, Ebola, and COVID-19 have underscored the need for the dependable availability of vaccines against emerging infectious diseases (EIDs). The cost and risk of R&D programs and uniquely unpredictable demand for EID vaccines have discouraged vaccine developers, and government and nonprofit agencies have been unable to provide timely or sufficient incentives for their development and sustained supply. We analyze the economic returns of a portfolio of EID vaccine assets, and find that under realistic financing assumptions, the expected returns are significantly negative, implying that the private sector is unlikely to address this need without public-sector intervention. We have sized the financing deficit for this portfolio and propose several potential solutions, including price increases, enhanced public-private partnerships, and subscription models through which individuals would pay annual fees to obtain access to a portfolio of vaccines in the event of an outbreak.

Timeline of a hypothetical EID vaccine development program.

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FinancingVaccinesforGlobalHealthSecurity

JonathanT.Vu,1,2BenjaminK.Kaplan,3ShomeshChaudhuri,3MoniqueK.Mansoura,4and

AndrewW.Lo3,5–8*

1WarrenAlpertMedicalSchool,BrownUniversity,Providence,RI,USA

2BrownCenterforBiomedicalInformatics,Providence,RI,USA

3QLSAdvisorsLLC,Cambridge,MA,USA

4TheMITRECorporation,McLean,VA,USA

5MITSloanSchoolofManagement,Cambridge,MA,USA

6MITLaboratoryforFinancialEngineering,Cambridge,MA,USA

7MITComputerScienceandArtificialIntelligenceLaboratory,Cambridge,MA,USA

8SantaFeInstitute,SantaFe,NM,USA.

*Correspondingauthor:AndrewW.Lo,alo‐admin@mit.edu

RevisionDate:20March2020

Abstract

RecentoutbreaksofinfectiouspathogenssuchasZika,Ebola,andCOVID‐19have

underscoredtheneedforthedependableavailabilityofvaccinesagainstemerging

infectiousdiseases(EIDs).ThecostandriskofR&Dprogramsanduniquely

unpredictabledemandforEIDvaccineshavediscouragedvaccinedevelopers,and

governmentandnonprofitagencieshavebeenunabletoprovidetimelyorsufficient

incentivesfortheirdevelopmentandsustainedsupply.Weanalyzetheeconomic

returnsofaportfolioofEIDvaccineassets,andfindthatunderrealisticfinancing

assumptions,theexpectedreturnsaresignificantlynegative,implyingthattheprivate

sectorisunlikelytoaddressthisneedwithoutpublic‐sectorintervention.Wehave

sizedthefinancingdeficitforthisportfolioandproposeseveralpotentialsolutions,

includingpriceincreases,enhancedpublic‐privatepartnerships,andsubscription

modelsthroughwhichindividualswouldpayannualfeestoobtainaccesstoa

portfolioofvaccinesintheeventofanoutbreak.

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author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the(which was not peer-reviewed) The copyright holder for this preprint .https://doi.org/10.1101/2020.03.20.20039966doi: medRxiv preprint

Introduction

In this study, we examine the economic feasibility of developing and supporting a portfolio

of vaccines for the world’s most threatening emerging infectious diseases (EIDs) as

determined by scientific experts, drawing from the list of targets made by the recently

launched global initiative, the Coalition for Epidemic Preparedness Innovations (CEPI) (1–

3). Our portfolio is composed of the 141 preclinical assets identified by Gouglas etal. to be

targeting the priority diseases.

The risks of EIDs are inherently dynamic and largely unpredictable. New threats persist,

including the recent outbreak of a novel coronavirus COVID-19 emerging from Wuhan, China

(4) Government leaders face formidable decisions about the provision of health security

measures against outbreaks of these threats. Global actors are seeking to diminish the

danger that these pathogens pose to the wellbeing of nations, regions, and the world. Given

the range of potential biological threats, their unpredictability, and the limited resources

available to address them, policymakers must necessarily prioritize their readiness efforts

based on limited knowledge. All too often, they are forced to choose between priorities, and

construct so-called limited lists of treatments, using testimony from teams of experts to

inform these decisions. As history has shown, however, this approach leaves society

vulnerable to unforeseen outbreaks. Therefore, a more rational approach is to develop a

broad portfolio of vaccines in a coordinated manner, mitigating the future risk posed by

unpredictable outbreaks of these diseases.

Uncontrolled outbreaks of EIDs, defined as infections that have “recently appeared within a

population, or those whose incidence or geographic range is rapidly increasing or threatens

to increase in the near future” (5), have the potential to devastate populations globally, both

in terms of lives lost and economic value destroyed. Notable recent outbreaks of EIDs include

the 1998 Nipah outbreak in Malaysia, the 2003 SARS outbreak in China, and the 2014 Ebola

outbreak. In addition to the thousands of lives lost, the economic costs of these outbreaks

are estimated as $671 million, $40 billion, and $2.2 billion, respectively (5–8).

As the world becomes more globalized, urbanized, and exposed to the effects of climate

change, the danger of infectious diseases has become an even greater concern (9), as

emerging and re-emerging strains become more diverse, and outbreaks become more

frequent. While distinct from the emerging infectious diseases, influenza serves as the best

example of the destruction that viruses with pandemic potential can inflict on the modern

world. As a baseline, avian influenza outbreaks in the U.S. since late 2014 have caused

economy-wide losses estimated at $3.3 billion domestically, and have significantly disrupted

trade (10). The 1918 influenza pandemic, however, is estimated to have infected 500 million

people and killed 3-5% of the world’s population. In 2006, Dr. Larry Brilliant stated that 90%

of the epidemiologists in his confidence agreed that there would be a large influenza

pandemic within two generations, in which 1 billion people would sicken, 165 million would

die, and the global economy would lose $1 to $3 trillion (11) (see Supplementary Materials

for further discussion). Controlling EIDs before they have the chance to reach comparable

scale represents a significant opportunity to prevent similar loss.

Despite the threat that these diseases pose to global health and security, however, there are

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few economic incentives for manufacturers to develop preventative vaccines for EIDs, due

to the high costs of R&D and the uncertain future demand. Even if protection against these

emerging diseases were immediately achievable with existing technology, development

costs are significant (12), as they are for any pharmaceutical development program. Pronker

etal.(13)estimate that it costs between $200–900 million for a new vaccine to be created.

Failure to gain approval also poses a substantial risk, as successful passage through clinical

trials only occurs 6–11% of the time (13,14). Regulatory challenges are particularly

prominent in EID vaccine development, as viable candidates are rarely available for

distribution during outbreaks, making safety and efficacy testing difficult. As a result, vaccine

development for EIDs has been reactive and technologically conservative (15).

In spite of these substantial difficulties—or perhaps because of them—new global initiatives

have drawn attention to the need for new approaches to encourage the development of

vaccines against EIDs (16,17). International collaborations like CEPI have drawn extensive

public, private, NGO, and academic attention to the perils of global epidemic unpreparedness

(18).

This crisis-driven expanded interest in vaccines to address epidemic threats is encouraging,

but there is still much work to be done. There needs to be a viable, sustainable business

model that will align the financial incentives of stakeholders to encourage the necessary

investment in vaccine development (19,20). While governments and international agencies

have striven to create incentives to attract additional private sector investment in vaccine

development, these efforts have so far failed in attracting sufficient capital to enhance

preparedness against the world’s most deadly emerging pathogens (21).

Several mechanisms have recently been proposed or implemented to create incentives for

industry to develop vaccines and other medical countermeasures for EIDs (22). Beyond the

“push mechanism” of significant R&D support, these mechanisms provide some measure of

a “pull incentive,” recognizing that traditional market forces are insufficient to secure global

health security aims. These strategies include the direct government acquisition of stockpiles

of vaccines, the use of prizes, priority review vouchers, and the establishment of advance

market commitments, each of which is described in more detail in Supplemental Materials.

However, to date, none of these strategies have been deemed to be effective in addressing

the growing threat of EIDs.

Previous research has demonstrated that a novel ‘megafund’ financing strategy is capable of

generating returns that could attract untapped financial resources to fund the development

of a portfolio of drug development programs (23,24). In this study, we address this

possibility by simulating the financial performance of a hypothetical megafund portfolio of

141 preclinical EID vaccine development programs across 9 different EIDs for which there

is currently no approved prophylactic vaccine. Under current business conditions, we

determine a private sector solution for the comprehensive development of EID vaccines is

not yet feasible, and quantify the gap so as to inform current policy discussions regarding the

need for public-sector intervention.

We conclude with a discussion of three possible solutions to this challenge: 1) establishing a

global acquisition fund for EID vaccines, in which governments around the world

collaborate; 2) raising the price of portfolio vaccines by two orders of magnitude; and

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3) creating a subscription model for vaccines, through which the global at-risk population

pays an annual fee to fund the development of and ensure access to a predefined list of

vaccines for EIDs.

MegafundRationale

To create further incentives for investing in this space, we hypothesize the creation of an EID

megafund based on the model developed by Fernandez etal.(23), which uses portfolio

theory and securitization to reduce investment risk in these assets. In financial engineering,

the practice of securitization requires the creation of a legal entity that issues debt and equity

to investors, using the capital raised to acquire a portfolio of underlying assets—in this case,

vaccine candidates targeting EIDs. These assets subsequently serve as collateral, and their

future cash flows service the debt incurred to acquire them, paying the interest and principal

of the issued bonds. Once the debt has been repaid, equity holders receive the residual value.

If the portfolio’s cash flows are insufficient to meet the obligations to the bondholders, the

collateral will be transferred to bondholders through standard bankruptcy proceedings.

Given the characteristically high risk of default of candidates in the early stages of

development, and the need for increased financial investment in vaccine research as a whole,

securitization in the form of a vaccine megafund offers several key benefits. The

securitization of vaccine research enables investors to reduce their risk of financial loss to a

scale that is not readily achievable under current financing mechanisms, as they can invest

in many vaccine projects at once, thus increasing the likelihood of at least one success. The

normalization of returns created by the construction of an asset portfolio permits the

issuance of debt, which allows fixed-income investors to gain exposure in a space that is

traditionally too risky to represent a compelling opportunity for investment. The ability to

issue debt is critical, because bond markets have much greater access to capital than does

venture capital or the private and public equity markets. This allows the megafund to raise

enough funding to purchase an array of assets and reach its critical threshold of

diversification.

One notable benefit of our megafund approach is that it hedges against the societal risk that

the world will not have the ‘right’ vaccine it needs for the next EID outbreak. To date, the U.S.

government and CEPI programs have been forced to severely limit their portfolios, due to

funding constraints. This approach allows us to assess the opportunity of addressing 9 of the

world’s most threatening EIDs at once.

While the megafund approach is effective at reducing the development risk of EID vaccines,

it should be emphasized that the success of this technique hinges upon securitizing assets

that have the potential to be profitable individually if the development effort is successful.

This flies in the face of conventional pharma wisdom that vaccines are commercially

challenging, not only because of development risk but also because of the unpredictability of

outbreaks and constraints on pricing when outbreaks occur. However, to quantify the gap

between reality and commercial viability—and in light of global stakeholders’ ongoing

efforts to raise funding to combat these diseases—we suspended belief in this presumption

so as to allow the financial analysis to determine the profitability of the EID portfolio in an

unbiased fashion. Based on available pipeline data, an analysis by Gouglas etal. projects that

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the cost of progressing at least one vaccine candidate through the end of phase 2a against a

comparable portfolio of 11 emerging infectious diseases would cost between $2.8 and $3.7

billion (3). Our approach builds upon this analysis by quantifying the gap between the

estimated costs of development and the sort of returns that would need to be generated by

such expenditure in order to justify investment.

Methods

To apply this portfolio approach to EID vaccine development, we began by analyzing the

hypothetical investment returns of a portfolio of 141 preclinical EID vaccine development

programs across 9 different emerging infections for which there is currently no approved

prophylactic vaccine. Our analysis relies on several assumptions and parameters, including

estimates of the cost of vaccine development, the length of time from preclinical testing to

the filing of a new vaccine license application, the probability of success of each project, and

pairwise correlations of success among the projects in the portfolio. The target diseases were

selected from CEPI’s Priority Pathogen list, which was based in part upon the WHO’s R&D

Blueprint focusing on epidemic prevention (1,2). We drew our portfolio assets from CEPI

pipeline research for each disease on its priority pathogen list (1,3). (See Supplementary

Materials for more details).

The model design is less complicated than that of Fernandez etal.(23). Unlike oncology—a

domain with many approved drugs and even more under development—there are currently

few EID vaccines available on the market, indicating a paucity of data with which to calibrate

our simulations. In setting our simulation parameters, we relied on generic information

about the vaccine development process, specific estimates posited by CEPI (1), and

qualitative input from scientists with domain-specific expertise.

The present value of out-of-pocket development costs for each of the projects in the portfolio

was set to $250 million, based on assumptions made by CEPI about the cost to develop a

preclinical asset through phase 2 (1). CEPI further estimates that it will take five years for

this development to occur (Figure 1). CEPI proposes that assets at this level of development

will justify stockpiling, further development, and conditional usage under emergency

conditions, a plan that some experts believe may be feasible (1,25).

At $250 million per project, a megafund of 141 projects requires $35.25 billion. To determine

the returns generated by such a portfolio, we assumed a 15-year period of exclusivity and a

10% cost of capital to calculate the NPV of future cash flows upon approval in year 5. This

value must be weighed against the possibility of total loss if the vaccine project fails. An

assessment of the megafund’s returns therefore requires estimates of the probabilities of

success of each of the 141 vaccine candidate projects as well as the pairwise correlation of

success of all possible pairs of assets. The probabilities of success are based on estimates of

the compounded probabilities of advancement from preclinical testing to vaccine approval.

The probability of development through phase 2 of a vaccine at the start of preclinical testing

is 32%, based on the transition probabilities provided by CEPI (1). See Supplementary

Materials for details on these estimates as well as the method for assigning pairwise

correlations.

Given the inherent unpredictability of a future EID outbreak, we necessarily made several

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practical assumptions to project revenue. In this model, we assumed that the prophylactic

regimen would consist of a single dose of vaccine. The probability of disease outbreak was

estimated based on historical outbreaks per disease, while regimen demand was projected

using historical outbreak size, potential for pandemic spread, and an assessment of relative

clinical severity. These demand parameters were determined respectively by case estimates

from documented outbreaks, referencing the Woolhouse assessment for pandemic poten tial,

and comparing the clinical presentation and prognosis for each disease (26,27). A perceived

demand multiplier was assigned based on Woolhouse classification and clinical severity on

a five-step scale ranging from mild to severe. The average number of cases and the perceived

demand multiplier were used to calculate the number of regimens sold in an outbreak year

for each disease. This product, the expected chance of outbreak in a given year based on

historic outbreak data, and the expected selling price per vaccine regimen were used to

subsequently calculate the annual expected revenue for each disease. The price per regimen

was estimated based on whether the disease in question typically affected high-, medium- or

low-income countries. The expected price per regimen for each income level was informed

by CDC, GAVI, and PAHO vaccine pricing data, respectively (28–30). Please see

Supplementary Materials for additional details.

Figure1.TimelineofahypotheticalEIDvaccinedevelopmentprogram.

Results

Table 1 provides estimates of the annual expected revenues from direct sales of vaccines to

susceptible populations for the 9 different EIDs considered in the megafund. (Please see

Supplementary Materials for more details on how projected revenues were determined.)

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

Table1.EIDvaccinesales.Annualexpectedrevenuesfromdirectsalesof

vaccinestosusceptiblepopulationsfor9differentEIDs.Allvaluesare

annualized.

The simulated investment performance of an EID vaccine portfolio as a function of the

commercial potential of each individual vaccine project is provided in Table 2 and illustrated

in Figure 2 (please see Supplementary Materials for more information on how returns were

calculated). The commercialization potential of these vaccines is consistently very poor,

orders of magnitude lower than what would be required to make them commercially viable.

The parameter values that are closest to industry averages correspond to the highlighted

row in Table 2, in which the expected annual profits upon FDA approval are $1 million,

resulting in an NPV per successful EID vaccine of $7.6 million. For these values, the vaccine

portfolio’s expected return is 61.1%, with a standard deviation of 4.0%.

For completeness, Table 2 also reports megafund performance statistics for several other

sets of parameters. The breakeven point, where the megafund’s expected 5-year return is

0%, occurs as the NPV of a successful vaccine reaches $772 million, two orders of magnitude

greater than our current estimates using past averages for costs, revenues, probabilities of

success and outbreak, and other information. However, for an NPV of $1 billion, the vaccine

portfolio becomes marginally profitable, and at $10 billion, it is highly profitable. These

results suggest that many of the model parameters would have to change drastically for the

portfolio to be profitable. In fact, holding all else equal, simply breaking even would require

selling vaccines at approximately 100 times the price assumed in our simulations.

Average

Regimens

Sold

Chikungunya 11% 523,600 4x 2,094,400 $5.55 $1,278,600

MERS 40% 400 10x 4,000 $46.12 $73,800

SARS 7% 8,100 12x 97,200 $5.55 $37,800

Marburg 12% 100 10x 1,000 $1.97 $200

RVF 11% 79,400 6x 476,400 $5.55 $290,800

Lassa 100% 300,000 8x 2,400,000 $1.97 $4,728,000

Nipah 16% 100 10x 1,000 $5.55 $900

CCHF 13% 300 10x 3,000 $5.55 $2,200

Zika 4% 500,000 12x 6,000,000 $5.55 $1,332,000

Disease Outbreak

Probability

Average

Cases

PerceivedDemand

Multiplier

Average

Price

AnnualExpected

Revenue

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Figure2.EIDmegafundrisksandreturnstoinvestors.Investmentreturnsandrisksof

aportfolioof141preclinicalEIDvaccinecandidateswhenprojectsarenot

independent(withcorrelation),andwhenprojectsarestatisticallyindependent(no

correlation).Expectedreturnsbreakevenwhentheannualexpectedprofitper

successfulprojectis$772million.CI,confidenceinterval.



Table2.EIDmegafundrisksandreturnstoinvestors.Investmentreturns(%)ofa

portfolioof141preclinicalEIDvaccinecandidateswhenprojectsarenot

independent(withcorrelation),andwhenprojectsarestatisticallyindependent(no

correlation).TheSharperatioisestimatedastheratiooftheexpectedreturntothe

standarddeviation.PV(Profits),presentvalueofprofitspersuccessfulvaccinein

year5;E[R

],expected5‐yearreturnoninvestment;E[R

],expectedannualized

return;SD[R

],annualizedreturnstandarddeviation;CI,confidenceinterval;SR,

Sharperatio.

Megafunds are, of course, not the only business model through which vaccines can be

developed. Traditionally, large pharmaceutical companies have incorporated vaccine

programs into broader and highly diversified portfolios of therapeutics across many

indications. To explore this possibility, we estimated the impact on risk and reward of

incorporating the EID vaccines portfolio into a hypothetical pre-existing and profitable

E[R

]E[R

]SD[R

] 95%CI SR E[R

]SD[R

] 95%CI SR

0.10 –100.0 –83.6 1.7 (–87.4,–80.9) – –83.3 0.4 (–84.2,–82.6) –

1.0 –99.9 –74.1 2.7 (–80.1,–69.7) – –73.6 0.7 (–74.9,–72.4) –

7.60 –99.0 –61.1 4 (–70.2,–54.6) – –60.4 1 (–62.4,–58.5) –

10 –98.7 –58.9 4.3 (–68.5,–51.9) – –58.1 1 (–60.3,–56.2) –

100 –87.0 –34.8 6.8 (–50.0,–23.9) – –33.6 1.6 (–37.0,–30.5) –

772 0.0 –1.9 10.2 (–24.8,14.5) – 0 2.5 (–5.2,4.5) –

1,000 29.7 3.3 10.7 (–20.8,20.6) 0.3 5.2 2.6 (–0.1,10.0) 2.01

10,000 1197.1 63.8 17 (25.6,91.2) 3.8 66.8 4.1 (58.3,74.5) 16.3

WithCorrelatio n NoCor relation

PV(Profits)

in$MM

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pharma company. Table 3 contains the estimated expected returns and volatilities of a

representative top-10, mid-tier, and small-capitalization pharmaceutical company with and

without the base case version of the EID vaccine portfolio. The best-case scenario—in which

big pharma adds this portfolio to its existing products—turns an otherwise profitable

business into an unprofitable one, losing 8.6% per year on average in shareholder value. The

results for mid- and small-cap pharma companies are even worse.

These results are consistent with the biopharma industry’s trend towards fewer companies

willing to engage in vaccine R&D, underscoring the infeasibility of a private-sector EID

vaccine portfolio given current cost and revenue estimates, and the need for some form of

public-sector intervention. A sensitivity analysis of these results to perturbations in our

model’s key parameters is provided in the Supplementary Materials. We find that the EID

vaccine megafund remains financially unattractive even under relatively optimistic cost and

revenue assumptions, implying the necessity for some form of public-sector intervention.

These findings may explain the dearth of EID vaccines developed over the past decade.

One intervention is the use of government-backed guarantees to mitigate the downside risk

of the EID portfolio. In a guarantee structure, a government agency promises to absorb the

initial losses on the portfolio to a predetermined amount, shielding private-sector investors

from substantial negative returns. For example, a guarantee on 50% of the portfolio’s

principal improves the expected annualized return in the base case scenario from −61.1% to

−12.6% (see Table S11 in the Supplementary Materials). While this negative-expected-

return scena rio is still un likely to attr act investors, e xpected returns can be further increased

using mechanisms such as advance market commitments and priority review vouchers. The

guarantee structure—in combination with other existing revenue-boosting mechanisms—

has the potential to transform a financially unattractive portfolio of EID vaccine candidates

into one that could realistically attract private-sector capital.



Table3.Simulatedperformanceofahypotheticalrepresentativetop‐10,

mid‐tierandsmall‐cappharmaceuticalcompanywithandwithouttheEID

vaccineportfolio.Pharmaceuticalcompaniesareclassifiedaccordingto

theirNorthAmericanIndustryClassificationSystem(NAICS)codeandtheir

marketcapitalizationeachyearfrom2005to2016.Returnstatisticsare

averagedwithineachsub‐grouptoformtheexpectedreturnandstandard

deviationestimates.Theperformanceoftheserepresentativecompanies

combinedwiththeEIDvaccineportfolioisestimatedbyassumingno

correlationwithvaccinerevenues.MarketCap,averagemarket

capitalizationinbillionsofdollars;E[R1y],expectedannualizedreturn;

SD[R1y],annualizedreturnstandarddeviation;SR,Sharperatio.

MarketCap

($B) E[R

]SD[R

]SR E[R

]SD[R

]

Top‐10Pharma 94.1 11.10% 23.80% 0.47 –8.6% 17.40%

Mid‐TierPharma 12.9 14.30% 32.90% 0.43 –40.9% 9.30%

Small‐CapPharma 1.6 19.60% 53.20% 0.37 –57.6% 4.50%

WithoutEIDVaccinePortfolio WithEIDVaccine

Portfolio

CompanyType

. CC-BY 4.0 International licenseIt is made available under a



Finally, we consider a subscription model under which the largest governments around the

world would purchase subscriptions to EID vaccines on behalf of their constituents. To fund

the cost of pursuing 141 vaccine targets at $250 million per target (for a total of $35.25

billion), suppose that the governments of the G7 countries agreed to pay a fixed subscription

fee per capita over a fixed amortization period to cover this cost. How much would this

subscription fee be? For an amortization period of 5 years, and an estimated total G7

population of 770,063,285 (as of 2016, according to the World Bank (31)), and a cost of

capital of 10%, the per capita annual payment to cover the total cost of $35.25 billion is

$12.08 per person per year. If we extend the amortization period to 10 years, the

subscription fee declines to $7.45 per person per year. Table 4 contains the per capital

subscription fees as a percentage of the annual per capita healthcare expenditure of each G7

country and as expected, the cost is trivial for all countries, ranging from a high of 0.59% for

Italy to a low of 0.15% for the US using a 5-year amortization period.

Of course, this subscription model considers only the development cost of vaccines. Once

developed, the production and stockpiling of these vaccines would require further funding,

but the subscription model can be applied on an ongoing basis, and at a much lower annual

cost. Access to these vaccines by non-G7 countries must also be considered, but such access

involves political and ethical issues that are beyond the scope of this economic analysis.

These results suggest that a government-led subscription model is financially feasible and

would likely yield significant economic and political benefits to all participating

governments. While the usual challenges of broad multi-national cooperation must be

overcome, early traction from organizations such as Civica Rx suggests that focused,

inclusive collaboration can ensure sustained supplies of life-saving drugs (32).

Table4.Annualtotalcostandpercapitacostofsubscriptionmodelforfundinga$35.25

billionvaccinesdevelopmentfundbyG7countrieswherethepercapitalsubscription

feeis$12.08perpersonperyearovera5‐yearperiodor$7.45perpersonperyear

overa10‐yearperiod.Source:authors’computationsbasedonpopulationand

healthcareexpendituredatafromtheWorldBank(31).



Countr y Population

Current 

PerCapital

Healthcare

Spending

PerCapitaFee

as%ofCurrent

PerCapita

Healthcare 

Spend(5‐year) AnnualTotalCost

PerCapitaFee

as%ofCurrent

PerCapita

Healthcare 

Spend(10‐year) AnnualTotalCost

Canada 37,411,047 3,274$ 0.37% 451,755,252$ 0.23% 278,702,763$

France 65,129,728 3,534$ 0.34% 786,470,817$ 0.21% 485,199,871$

Germany 83,517,045 3,992$ 0.30% 1,008,505,956$ 0.19% 622,180,695$

Italy 60,550,075 2,039$ 0.59% 731,169,443$ 0.37% 451,082,623$

Japan 126,860,301 3,538$ 0.34% 1,531,895,305$ 0.21% 945,076,903$

UnitedKingdom 67,530,172 3,175$ 0.38% 815,457,260$ 0.23% 503,082,567$

UnitedStates 329,064,917 8,078$ 0.15% 3,973,607,167$ 0.09% 2,451,449,747$

5‐YearAm ortizationPeriod 10‐YearAm ortizationPeriod

. CC-BY 4.0 International licenseIt is made available under a

Discussion

Financing global health security against biological threats remains a persistent challenge.

Unfortunately, but not unexpectedly, a weak and uncertain pre-crisis market demand has led

to a relative lack of interest in developing vaccines against EIDs. This has left the global

community increasingly vulnerable to repeated outbreaks of these viruses. The challenges

of EID vaccine development, however, are troubling issues for vaccines more generally. The

situation has been described as a crisis, and perhaps rightly so, as there are only four

remaining major manufacturers that focus on vaccine development (25).

Vaccines only sell for a fraction of their economic value, in some cases for only a few dollars.

They provide myriad benefits, like enabling would-be patients to live longer, healthier lives

(33,34), and bearing yet-undervalued gains in productivity and positive externalities to

society at large (35–37). Although the low price of vaccines is meant to benefit individuals

and regions with lower incomes, in the long run, it has had the opposite effect, causing them

to be medically underserved due to a lack of vaccine investment. Pharmaceutical companies

and investors are directing their resources to projects in which the estimated return on

investment is more predictable and lucrative. Vaccine prices are currently set far below the

prices of drugs that treat other serious conditions, such as cancer, despite the enormous

societal value of vaccines in general, and those to ensure global health security in particular.

The typical expected risk-adjusted net present value (NPV) of a vaccine in our hypothetical

portfolio upon regulatory approval is on the order of only $7.6 million. This is two or three

orders of magnitude lower than the comparable value of an approved cancer drug, yet the

out-of-pocket costs to develop an EID vaccine are not dissimilar.

In addition to pricing, another challenge lies in assessing the future demand for EID vaccines.

Due to the inherent unpredictability in the scale and timing of outbreaks, the future demand

for a specific EID vaccine is typically unclear. An additional factor is geopolitical. Diseases

that are traditionally found in only a few, lower-income countries may not attract as many

R&D dollars because generating a return on investment is more difficult in those limited

markets (25,38). While wealthier governments might issue purchase agreements to assure

vaccine sponsors of returns (38), these commitments are more difficult to secure for EIDs in

lower-income countries or those undergoing economic hardship. However, an increasing

number of stakeholders are realizing the danger of this dynamic for low and high-income

countries alike, as under epidemic outbreak conditions, diseases like Zika and Ebola have the

potential to spread much further than their traditional locales. The Ebola outbreaks in West

Africa in 2014 demonstrate how the absence of vaccine demand prior to an event may result

in a tragic loss of life and a regional economic setback. It is a significant concern that years

after those outbreaks, the demand for Ebola vaccines remains limited and uncertain,

allowing gaps in preparedness to persist (39–41).

Unless these market challenges are addressed, the global population will remain vulnerable

to substantial human and economic losses when epidemics and pandemics arise.

We believe that this represents a significant missed opportunity. Aside from the nuclear

threat and climate change, pandemics represent one of the most significant existential

dangers facing humanity today (42). Nevertheless, investments in preparedness for

biological threats remain underfunded, leaving the world vulnerable to catastrophic

. CC-BY 4.0 International licenseIt is made available under a

infectious disease events. With this in mind, we propose several measures that might move

the mission for EID vaccine readiness toward financial viability.

Our analysis strongly suggests that reliance solely on private sector investment in EID

vaccines is insufficient, given the negative returns achieved by an EID-focused megafund,

and the negative impact such a pool of assets would have on an otherwise profitable

pharmaceutical company. As a result, if EID vaccine candidates are to be developed,

continued private-public cooperation will be imperative, and novel approaches to engage

and attract capital will be needed. While bond markets are capable of providing access to

substantial amounts of capital to help vaccine development efforts, the resources available

to the public sector have great potential as well (43). In 2015, the U.S. spent $9,990 per

person on healthcare (44). If we assume that there are 300 million Americans, just 1.25% of

this amount of spending would yield $37.46 billion dollars, greater than the projected $35.25

billion it would take to fund the entire EID portfolio of vaccines. While achieving such an

allocation of funding would hardly be as simple as this calculation suggests, this thought

experiment illustrates that encouraging the development of vaccines that protect against

EIDs of pandemic potential is well within the means of the global public and private sector

stakeholders, if there is public support and political will. In fact, there is evidence to indicate

that people expect and would support further protection from these threats (45).

The U.S. government’s MCM program has demonstrated a capability to create incentives for

the development of vaccines that would otherwise not be developed, once sufficient market

demand is guaranteed ahead of time. This has been true for anthrax and smallpox as well as

for various strains of pre-pandemic influenza, for which the government provides market

commitments on the order of $100-200 million per year for successful vaccine development

programs (46,47). While challenges exist (e.g., sustained funding commitments), new

initiatives such as CEPI can learn important lessons from these examples (48,49).

Perhaps key to the problem of EID vaccine funding is a deficiency in the pricing of the risk of

infection by EIDs. Although the prevention of epidemics and pandemics saves countless lives

and billions of dollars of economic value, the revenue realized by vaccine manufacturers is

only a very small fraction of this value. With this in mind, an examination of a capitated fee

structure—a subscription model—applied to vaccine development and acquisition is

promising. Under the current model, vaccines are purchased a la carte after outbreaks begin.

However, if stakeholders were to pay in advance to develop and stockpile vaccines, viewing

their payment as a form of insurance that would maintain epidemic response capabilities

and provide protection from EID outbreaks, much like a society-wide immune system, the

amount of capital needed to fund these programs might be easier to raise and keep the price

per regimen l ower. Vaccine developers unde r this mo del would most likely sell subscriptions

to governments, building upon existing infrastructure, such as the U.S. government’s

biodefense and pandemic preparedness programs. To balance the concern that non-

subscribers may require vaccine regimens with the objective of encouraging subscription

ahead of outbreaks, a tiered pricing scheme rewarding early adoption could be implemented.

A private subscription model should also be explored, however, as it would enable

individuals, communities, and corporations to take greater ownership in preparedness.

Determining precisely who should pay the insurance premium, and who is willing to pay, is

essential to this arrangement.

. CC-BY 4.0 International licenseIt is made available under a

Although this model is a departure from the status quo, promising innovation in vaccine

financing is becoming more commonplace. The recent World Bank issue of pandemic bonds

and swaps for a Pandemic Emergency Financing Facility (PEF) suggests that when

structured appropriately, assets geared toward preparedness can be attractive to investors

(50). We believe that our model may shed some light on what will encourage more

comprehensive pandemic preparedness by addressing shortcomings in the EID vaccine

pipeline.

As demonstrated in our simulations, the investment required to reduce the global risk from

EIDs is within reach. Securing these resources, however, will require governments to

strengthen their commitments to supporting EID vaccine markets, in order to allow private

sector stakeholders and untapped capital to engage with these markets substantively. The

recent developments around Sanofi Pasteur’s Zika collaboration highlight the risks of a

variable commitment to preparedness. Due to changing epidemiology and internal disputes

over potential product pricing, BARDA and Sanofi have chosen to halt further development

of their Zika asset, leaving society vulnerable to future outbreaks (51).

As cases like this suggest, government buy-in is integral for long-term pipeline sustainability.

Governments can catalyze outside investments through a range of strategies, including

guaranteed commitments. Fifteen years of guaranteed revenue via purchase commitments,

similar to the U.S. government’s purchase of smallpox and anthrax vaccines, would do well

to encourage development efforts. For example, an annual purchase commitment of $150

million per successful vaccine candidate would represent an NPV of $1.14 billion, exceeding

our modeled breakeven NPV of $772 million. Our results suggest that investment in this

space is highly unattractive to the private sector, requiring commitments of the

aforementioned magnitude for development viability; as highlighted above, either the price

per regimen or the demand from outbreaks would have to increase by orders of magnitude

to have the same effect. We encourage readers to engage with these assumption parameters

critically using our open source software.

While the main focus of this paper is the challenge of financing EID vaccine development, we

realize that there are other concerns that must be considered in parallel before a portfolio of

novel EID vaccine regimens is made available to the public. These issues include, but are not

limited to, preclinical discovery, regulatory approval strategy, and post-approval

procurement and distribution. These are matters of great importance and warrant further

investigation.

It is indisputable, however, that better business models for global health security are

urgently needed. We expect there may be benefits to extending the scope of the megafund

approach beyond the particular EID vaccine assets considered in this study, perhaps to

antibiotics or MCMs for intentional biological threats, an additional global health security

concern. While this would do little to improve the desirability of EID vaccine candidates as

assets, broadening the scope of a fund to address additional threats may create greater

financial viability to global health security more broadly.

As past efforts demonstrate, the key to generating interest in developing vaccine assets is to

offer sufficient financial incentives for would-be developers, such as direct market

commitments or priority review vouchers. Closing the gap between the economic value of

. CC-BY 4.0 International licenseIt is made available under a

epidemic prevention and the financial returns of vaccine assets, whether by encouraging the

market to compensate developers through a capitated vaccine “subscription” model, or by

combining vaccine assets into a large portfolio to normalize investment risk as described

above, will better enable the global health security community to address the dangers of

EIDs.



. CC-BY 4.0 International licenseIt is made available under a

Acknowledgments

We thank Ellen Carlin, Doug Criscitello, Margaret Crotty, Narges Dorratoltaj, Per Etholm,

Jeremy Farrar, Nimah Farzan, Mark Feinberg, Jose-Maria Fernandez, John Grabenstein, Peter

Hale, Richard Hatchett, Peter Hotez, Daniel Kaniewski, Adel Mahmoud, Mike Osterholm,

Kevin Outterson, Chi Heem Wong, CEPI leadership, and two reviewers and the editor for

helpful comments and discussion, and Jayna Cummings for editorial assistance. Research

support from the MIT Laboratory for Financial Engineering and the Warren Alpert Medical

School of Brown University is gratefully acknowledged. The views and opinions expressed

in this article are those of the authors only, and do not necessarily represent the views and

opinions of any institution or agency, any of their affiliates or employees, or any of the

individuals acknowledged above.

FundingandConflictsStatement

Funding support from the MIT Laboratory for Financial Engineering is gratefully

acknowledged, but no direct funding was received for this study and no funding bodies had

any role in study design, data collection and analysis, decision to publish, or preparation of

this manus cript . The a uthor s were personally salaried by their institutions during the period

of writing (though no specific salary was set aside or given for the writing of this

manuscript).

J.V. and B.K. report no conflicts.

S.C. is a co-founder and chief technology officer of QLS Advisors, a healthcare analytics and

consulting company.

M.M. is Executive Director for Global Health Security and Biotechnology at The MITRE

Corporation, a not-for-profit organization working in the public interest as an operator of

multiple federally funded research and development centers (FFRDCs). She is focused on the

sustainability of the biodefense industrial base and the public-private partnerships that are

vital to national and global health security.

A.L. reports personal investments in private biotech companies, biotech venture capital

funds, and mutual funds. A.L. is a co-founder and partner of QLS Advisors, a healthcare

analytics and consulting company; an advisor to BrightEdge Ventures; an advisor to and

investor in BridgeBio Pharma; a director of Roivant Sciences Ltd., and Annual Reviews;

chairman emeritus and senior advisor to AlphaSimplex Group; and a member of the Board

of Overseers at Beth Israel Deaconess Medical Center and the NIH’s National Center for

Advancing Translational Sciences Advisory Council and Cures Acceleration Network Review

Board. During the most recent six-year period, A.L. has received speaking/consulting fees,

honoraria, or other forms of compensation from: AIG, AlphaSimplex Group, BIS, BridgeBio

Pharma, Citigroup, Chicago Mercantile Exchange, Financial Times, Harvard University, IMF,

National Bank of Belgium, Q Group, Roivant Sciences, Scotia Bank, State Street Bank,

University of Chicago, and Yale University. Radius Health is not in the portfolio of any of the

investment funds and is not in any way associated with the companies that the authors are

affiliated with.

. CC-BY 4.0 International licenseIt is made available under a



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36. D. E. Bloom, L. Brenzel, D. Cadarette, J. Sullivan, Moving beyond traditional valuation of

vaccination: Needs and opportunities, Vaccine 35, A29–A35 (2017).

37. The Council of Economic Advisers, MitigatingtheImpactofPandemicInfluenzathrough

VaccineInnovation (2019; https://www.whitehouse.gov/wp-

content/uploads/2019/09/Mitigating-the-Impact-of-Pandemic-Influenza-through-

Vaccine-Innovation.pdf).

38. R. Glennerster, M. R. Kremer, A Better Way to Spur Medical Research and Development,

Regulation 23, 34–39 (2000).

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39. Wellcome Trust and Center for Infectious Disease Research and Policy (CIDRAP),

CompletingtheDevelopmentofEbolaVaccines (2017;

http://www.cidrap.umn.edu/sites/default/files/public/downloads/ebola_team_b_report_

3-011717-final_0.pdf).

40. Gavi, Ebola vaccine purchasing commitment from Gavi to prepare for future outbreaks

(2016) (available at http://www.gavi.org/library/news/press-releases/2016/ebola-

vaccine-purchasing-commitment-from-gavi-to-prepare-for-future-outbreaks/).

41. EBOLA Vaccine (Solicitation #17-100-SOL-00013)FedBizOpps.gov (2017) (available at

https://www.fbo.gov/index?s=opportunity&mode=form&id=8ef263bed8bf48653ae6b682

5c15052a&tab=core&_cview=1).

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at https://www.securityconference.de/en/activities/munich-security-conference/msc-

2017/speeches/speech-by-bill-gates/).

43. P. Hale, S. Wain-Hobson, R. A. Weiss, Research investment: Vaccine research loses out,

Nature 478, 188–188 (2011).

44. Center for Medicare & Medicaid Services, NHE Fact Sheet (2017) (available at

https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-

reports/nationalhealthexpenddata/nhe-fact-sheet.html).

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Polled Think the Government Should Invest More in Biosecurity2016 (available at

https://www.allianceforbiosecurity.org/biosecurity-public-opinion-poll).

46. Office of the Assistant Secretary for Preparedness and Response, ProjectBioShield

AnnualReport:January2014‐December2014 (2014).

47. C. E. Johnson, ReporttoCongress:PandemicInfluenzaPreparednessSpending (2009).

48. K. Hoyt, R. Hatchett, Preparing for the next Zika, Nat.Biotechnol. 34, 384–386 (2016).

49. P. K. Russell, G. K. Gronvall, U.S. Medical Countermeasure Development Since 2001: A

Long Way Yet to Go, BiosecurityBioterrorismBiodefenseStrateg.Pract.Sci. 10, 66–76

(2012).

50. World Bank launches “pandemic bond” to tackle major outbreaksReuters (2017)

(available at https://www.reuters.com/article/us-global-pandemic-insurance-

idUSKBN19J2JJ).

51. E. Sagonowsky, Sanofi pulls out of Zika vaccine collaboration as feds gut its R&D

contractFiercePharma (2017) (available at

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government-to-wind-down).

. CC-BY 4.0 International licenseIt is made available under a

FinancingVaccinesforGlobalHealthSecurity:

SupplementaryMaterials

JonathanT.Vu,1,2BenjaminK.Kaplan,3ShomeshChaudhuri,3MoniqueK.Mansoura,4and

AndrewW.Lo3,5–8*

1WarrenAlpertMedicalSchool,BrownUniversity,Providence,RI,USA

2BrownCenterforBiomedicalInformatics,Providence,RI,USA

3QLSAdvisorsLLC,Cambridge,MA,USA

4TheMITRECorporation,McLean,VA,USA

5MITSloanSchoolofManagement,Cambridge,MA,USA

6MITLaboratoryforFinancialEngineering,Cambridge,MA,USA

7MITComputerScienceandArtificialIntelligenceLaboratory,Cambridge,MA,USA

8SantaFeInstitute,SantaFe,NM,USA.

*Correspondingauthor:AndrewW.Lo,alo‐admin@mit.edu



RevisionDate:20March2020



PriorWork

Several mechanisms have recently been proposed or implemented to create incentives for

industry to develop vaccines and other medical countermeasures for EIDs (1). These strategies

include the direct government acquisition of stockpiles of vaccines, the use of prizes, priority

review vouchers, and the establishment of advance market commitments, we describe in more

detail below.

GovernmentResearch,Development,andAcquisition

It is clear that direct, non-dilutive funding for R&D will continue to be integral to future vaccine

development efforts. Governments, nonprofit organizations such as the Gates Foundation and

the Wellcome Trust, and the recently established CEPI are committed to provide this funding.

These entities offset the exceptional risk faced by vaccine developers beyond the traditional

scientific risk. The operational, regulatory, and market risks of vaccine development remain

extraordinary. Without robust and sustained R&D funding, many early-stage assets cannot

succeed. While R&D funding “push” mechanisms are necessary, however, they alone are

typically not sufficient.

In response to the anthrax attacks of 2001 and the outbreaks of SARS and H5N1 avian influenza

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in 2003 and 2004, the U.S. government established two programs to address biodefense and

pandemic threats. These programs, Project BioShield (2) and Pandemic Influenza

Preparedness (3), mandated that the U.S. government acquire stockpiles of medical

countermeasures (MCMs) against these threats. Each of these preparedness programs was

funded with an approximately $6 billion, multi-year appropriation. Central to both programs

was the establishment of guaranteed markets to purchase vaccines and other MCMs. This was

necessary to mitigate the pandemic threat for the U.S. population, but as importantly, to

establish viable public-private partnerships, as the U.S. government does not have licensed

vaccine-manufacturing capabilities itself. Stockpiles have since been established for a range of

MCMs, and are in progress for others, including vaccines for the Ebola virus (2,4–6).

Prizes

Historically, prizes have often been used as an incentive for technological innovation. For

example, the first Kremer Prize of £50,000 was awarded in 1977 for the invention of the “first

substantial flight of a human-powered airplane” (7). While some experts believe that this

approach might create sufficient incentives for research and development in less commercially

attractive diseases (8,9), there is substantial difficulty in applying this structure to EID

vaccines, as the prize pool would have to be large enough to offset the high development costs.

As a result, several experts have proposed market-based approaches instead (10,11). Most

recently, a prize model has been proposed to incentivize the development of novel antibiotics

to address the increasing global problem of antibiotic resistance (12). Importantly, the price is

delinked from the volume of sales (as with U.S. government acquisition programs), a key issue

for EIDs, where volumes are often insufficient to drive viable markets (13).

PriorityReviewVouchers

Another mechanism is the FDA priority review voucher program, currently implemented by

the U.S. government. Under this program, first proposed by Ridley etal.(11), companies

developing a therapy for a traditionally “neglected” disease can apply for an FDA priority

review voucher. Such vouchers can be used by the company for the accelerated review of

another, potentially more lucrative asset, or sold to another firm for review of one of their own

assets. Extending this program to medical countermeasures has been under consideration for

years (1), and the U.S. 21st Century Cures Act expanded the scope of the program to MCMs for

material threats (e.g., smallpox), now including Ebola and Zika (14,15). An analysis by Berman

and Radhakrishna suggests that these vouchers have tremendous value, with one selling for as

much as $350 million on the open market (16). However, their value may be waning as more

become available, as acquisition prices have decreased over the last few years (15). Even so,

the idea has garnered significant attention, and a European equivalent overseen by the

European Medicines Agency (EMA) has been proposed (17).

While some see priority review vouchers as a step in the right direction, vouchers are not

without potential drawbacks. For example, vouchers do little to ensure that subsequent

vaccine development will be pursued once the first candidate has been approved (8,15). It is

also unclear that the resultant vaccines will ultimately reach patients after approval, after the

vouchers have been assigned, once market realities are taken into account (8). They provide

one-time revenues to a firm, and do little to ensure sustained manufacturing capability or

availability of a vaccine. It should also be noted that the FDA priority review may result in a

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rejection, making the value of the voucher to a firm more variable than it first appears (15).

AdvanceMarketCommitments

The final mechanism under consideration is the advance market commitment. This concept is

similar to the advance purchasing commitments that the U.S. government can make under

Project BioShield for MCMs up to eight years in advance of their licensure (18). Advance market

commitments allow vaccine developers to assess the potential demand for their product if

approved, and provide some guarantee of expected compensation for their efforts. Levine et

al.(10) describe how such a structure would operate. Essentially, stakeholders from wealthy

countries would agree to pay a certain price per dose for a successful vaccine against a target

disease, subsidizing the amount that a poorer country would pay, should the development

project prove successful. While the risk of scientific failure would still be present, some of the

potential demand and revenue would be quantified before the project would be undertaken,

serving as encouragement to prospective developers. However, this approach assumes that

wealthier entities will still be interested in purchasing vaccines for relatively rare diseases that

might not have a direct impact on their constituents (19) unless a significant outbreak emerges.

While the methods described here may help mitigate the shortcomings of vaccine investment,

they also suggest that a more sustainable long-term solution lies in aligning the incentives for

wealthier stakeholders with the incentives of those people most vulnerable to EIDs. Indeed,

this alignment is prudent for the former group, as under outbreak conditions their health

security may be at risk, even in places where EIDs are unlikely to emerge (19), as the recent

Zika and Ebola outbreaks illustrate.



FluPandemics

Recent work by Fan etal.(20) calculated that the global expected loss due to pandemic

influenza would be approximately $570 billion annually. In 2015, the WHO noted the

emergence of many novel influenza viruses, resulting in an “especially volatile” gene pool,

left the consequences to human health “unpredictable yet potentially ominous” (21). World

Bank projections give a sense of the cost of inaction: a worldwide influenza epidemic would

reduce global wealth by an estimated $3 trillion (22). Even with diligent containment efforts

and antiviral therapy, Colizza etal.suggest that a particularly infectious strain might still

infect 30-50% of the global population (23), making prophylactic vaccines essential in

mitigating pandemic risk (24).

PortfolioSimulationAnalysis

We present the details of our simulation analysis of the expected risks and returns of a

portfolio of 141 preclinical emerging infectious disease (EID) vaccine candidate projects, as

well as the assumptions used to estimate the annual expected revenues from direct sales of

vaccines to susceptible populations for the 9 different EIDs addressed in our megafund. In

our portfolio, we utilize CEPI’s (25) targeted EIDs and pipeline research (26), which is based

upon the World Health Organization’s R&D Blueprint for epidemic prevention (27) (see

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Table S1). Diseases for which efficacious vaccines have already been approved outright, such

as Dengue fever, or provisionally in emergency situations, such as Ebola, were excluded.

Disease n

Chikungunya 20

MERS 14

SARS 6

Marburg 19

Rift Valley Fever 15

Lassa Fever 22

Nipah Virus 11

Crimean Congo Hemorrhagic Fever 6

Zika Virus 28

TableS1.Portfolioofassets9targetEIDsandthenumberofprojectsforeachincludedinthe

hypotheticalportfolio.

We begin with a discussion of the correlation assumptions underlying the Monte Carlo

simulation of our EID megafund portfolio’s performance, and provide details regarding our

projected development costs and phase-transition assumptions. We then turn to how

investment returns are defined, and conclude by describing our projected revenue estimates

for EID vaccines.

1. SimulatingCorrelatedVaccineCandidateProjects

While there are a number of methods for modeling the outcome of clinical trials with certain

scientific elements in common, we numerically estimate the performance of our EID vaccine

portfolio by modeling projects as pairwise correlated Bernoulli trials. Our methods are

similar to Lo etal. (28).

Denote by 𝝐 ≡ 𝜖1 𝜖2 ⋯ 𝜖𝑛′ a column-vector of random multivariate standard normal

variables. Then for any positive-definite matrix Σ, the new vector of random variables

𝑍  Σ1/2𝝐 is multivariate normal with covariance matrix Σ, where Σ1/2 denotes the Cholesky

factorization or matrix square root of Σ. Once the success probability, 𝑝𝑖, for each Bernoulli

trial random variable 𝐵𝑖 is defined, 𝐵𝑖 can be simulated as

𝐵

0if 𝑍

𝛼



1if 𝑍

𝛼



where we define 𝛼𝑖  Φ11  𝑝𝑖 and Φ1∙ is the inverse of the standard normal

cumulative distribution function.

For our purposes, pairwise correlations are meant to capture commonalities among

translational vaccine development programs, so that success or failure in one program has

predictive power for the success or failure of another program. In addition to specifying

values for each entry in Σ that are based on domain-specific knowledge of the underlying

science, we must also ensure that Σ is a valid positive-definite covariance matrix.

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In our simulations, we adopt a three-step process in which all pairwise correlations

between projects are first evaluated qualitatively as “low” or “high.” These assessments

are then translated into numerical values of 10% for “low” and 50% for “high.” The outline

of the dimensions used to assign correlation levels is displayed in Table S2 below. Figure

S1 shows a heat map of these assumed correlations.



FigureS1.Correlation between vaccine projects. Heat-map representation of qualitatively

determined pairwise correlation of success among 141 EID vaccine development projects. Orange

cells indicate 50% and green cells indicate 10%

The third step is to apply the numerical algorithm developed by Qi and Sun (29) to

compute the closest positive-definite matrix to the one specified manually. In this case, the

manually defined correlation matrix shown in Figure 2 in the main text was already

positive-definite, indicating that the Qi and Sun algorithm had no impact.

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TableS2.PairwisecorrelationassignmentsbasedonTargetedDisease

2. DevelopmentTimes,TransitionProbabilities,andResearchCosts

We use the CEPI estimates of phase-transition probability and development time at each

phase in our simulation (shown in Table S3), seeking to develop each asset through phase 2

(25). CEPI assumes that the measures taken by global actors in response to the recent Ebola

outbreaks indicate that phase 2 development would justify the stockpiling and conditional

use of these candidate vaccines in an actual outbreak setting, one that could support later

vaccine approval and distribution (25). We use CEPI’s estimate that the cost to develop each

preclinical asset through phase 2 is $250 million.

Our simulation assumes trials with a standard progression from phase to phase. If earlier

stages of R&D are included, or if a trial must be repeated, the costs and duration w ill in crease,

and the post-approval patent life of the asset will decrease. On the other hand, because we

have not modeled the transition from one clinical phase to the next, the realized out-of-

pocket cost of a typical project could be less than the assumed $250 million because of the

early termination of failed projects. CEPI’s assumption of $250 million of out-of-pocket costs

falls well within industry estimates of the vaccine development costs through phase 2 with

limited manufacturing scale. Though not analyzed here, the inclusion of phase 3 and

manufacturing facility maintenance and surge/scale-up can be factored into the model using

our open-source software.

PhaseDevelopment

Time(Years)

Transition

Probability(%)

Preclinical

1.5

Phase 1

2.0

Phase 2

1.5

TableS3.CEPI’s(25) estimatedphase‐transitionprobabilitiesanddevelopmenttimeateachphase.



DiseaseCorrelation

Same High (50%)

Different Low (10%)

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3. ComputingReturns

An investment rate of return, 𝑅, where an initial investment of 𝐼 yields a single payoff 𝑋 is

defined as 𝑅  𝑋/𝐼  1. If the investment is over a duration 𝑇  1 year, the return is often

annualized to simplify comparisons with other investments of different durations. This

geometric compounding assumes that interim gains are reinvested, and hence additional

interest is paid on the interest earned. The annualized return, 𝑅𝑎, is defined as

𝑅𝑋

𝐼

1 .

This definition is relatively straightforward. However, a question arises in the computation of

expected returns and standard deviations for multi-year returns, which require

annualization: should the moments be computed before or after annualization? In the main

text, we annualize realized returns before calculating statistics such as expectation and

standard deviation. While there is no clear argument for using one method over the other in

all contexts, we have chosen to annualize first to calculate the realized internal rate of return

(IRR), and then to compute the expected IRR and standard deviation of IRR, which are the

more traditional summary statistics.

4. ProjectedRevenues

It is well recognized that predicting the type, frequency, and scale of any future EID outbreak,

epidemic, or pandemic with accuracy is not possible, and therefore certain practical

assumptions were necessary to project revenues. In our model, the probability of a given

disease having an outbreak in a given year is given by the ratio of the number of historical

outbreaks to the number of years since the disease was first reported or since the first

notable outbreak. Respective probabilities are listed in Table S4 below. This represents a

pragmatic approach, and is not expected to reflect actual future epidemiological patterns.

While more sophisticated models are available and have been used to support other

pandemic financing programs (30), this approach is intended to provide a baseline

assessment of megafund financing (31).

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VirusOutbreaks

Outbreak

Total

Years

Annual

Probability



Average

Casesper

Outbreak



Chikungunya (32–36) 2005−’07/’09/’14−’16

65 10.8% 523,600

MERS (37,38) 2012/’15

40% 436

SARS (39) 2003

14 7.1% 8,098

Marburg (40) 1967/1998−2000/’05/’12

50 12% 75

Rift Valley Fever (41) 1978/1989/1998/2000/’01/

86 10.5% 79,414

’07−’09/’11

Lassa Fever (42) Occur annually

−

100% 300,000

Nipah Virus (43) 1998/2001/’04

19 15.8% 136

Crimean Congo 1945/2002−’08/’10

72 12.5% 320

Hemorrhagic Fever (44)

Zika Virus (45,46) 2007, 2015−’16

70 4.3% 500,062

TableS4.HistoricalOutbreakDataforPortfolioDiseases(32,33,42–46,34–41).

The number of vaccine regimens sold in response to an outbreak is a function of both the

actual and perceived risk to populations. This is subject to significant uncertainty, due in

part to gaps in knowledge at the onset of an outbreak about its transmission patterns and

its medical and public health impact (19,47–49). We based our projections of the number

of vaccine regimens sold on the average number of infections observed per outbreak, and

further modulated by three factors: Woolhouse Potential for Pandemic Spread, Severity of

Clinical Symptoms, and Mortality Rate. The number of vaccine regimens sold is given by

the average number of cases per outbreak multiplied by Woolhouse weighting and clinical

severity rating as described below. This is used as a crude proxy for demand extending

beyond those immediately affected, e.g., the so-called ‘worried well’.

By the nature of the methodology that CEPI used to establish their priority list of vaccines,

all of the viruses addressed in our portfolio are known to be potent, contagious pathogens.

However, the transmissibility between humans will vary. Woolhouse etal. (50,51)

categorize EIDs into four levels, described in Table S5 below. Consistent with the criteria

used by WHO and CEPI to identify priority pathogens, all of the diseases in our EID

portfolio are either Level 3 or Level 4. We assigned Level 3 diseases a weight of 1.0, and

Level 4 diseases a weight of 2.0, essentially doubling the vaccine regimens sold relative to

those for Level 3 viruses.

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WoolhouseLevelInterpretation

Level 1 Humans exposed but not infected

Level 2 Humans infected

Level 3 Human-to-human transmission

Level 4 Increased potential for epidemics/persist as

endemic infection

TableS5.LevelsoftransmissibilityascategorizedbyWoolhouseetal.(50,51),whichareusedinthe

calculationofthenumberofvaccinessold.AllEIDsintheportfolioarelevel3or4;level3diseasesin

theportfolioareassignedaweightof1.0andlevel4diseasesintheportfolioareassignedaweightof

2.0.

Risks to those not involved in the initial outbreak, both real and perceived, will also drive the

demand for these vaccine regimens. As can be seen by the ongoing discussions between the

U.S. government and Sanofi regarding licenses of Zika vaccines, any projections of future

demand are theoretical at best (52). However, it is difficult to discount the effect of public

perception on the willingness of people and policymakers to take action against these

pathogens, as illustrated by the discovery of the connection between Zika and congenital

microcephaly.

Though all the EIDs studied are a significant threat to human health, each disease presents

itself with different symptoms and a unique prognosis. These differences in presentation and

outcome may affect the way in which the public responds to outbreaks. We rate each disease

by clinical presentation and mortality rate as mild, mild-moderate, moderate, moderate-

severe or severe in Table S6, and assign a corresponding multiplier in Table S7. The

multipliers in Table S7 were informed by recent developments about a promising new

vaccine candidate for Ebola. According to the most recent reports, Merck has promised to

produce 300,000 doses of the vaccine (53), while the 2015 outbreak totaled approximately

30,000 cases (54). Given the severity of clinical symptoms and high mortality rate associated

with Ebola infection, we assign a multiplier of 10 to our “severe” category of diseases, and

adjust our multiplier accordingly based on clinical severity. While each of these diseases has

the potential to cause severe illness, some are asymptomatic in most patients, and thus less

likely to elicit high demand for a resulting vaccine. In assigning ratings, we also assumed that

the potential for certain sequelae will increase demand for certain vaccines; while the

presentation of Zika is generally mild, the possibility of birth defects resulting from infection

in pregnant women will likely boost the demand for this vaccine, increasing its relative

rating.

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DiseaseClinicalSeverityRating

Chikungunya Mild

MERS Severe

SARS Moderate

Marburg Severe

Rift Valley Fever Moderate

Lassa Fever Mild-Moderate

Nipah Virus Severe

Crimean Congo Hemorrhagic Fever Severe

Zika Virus Moderate

TableS6.Ratingofeachdiseasebyclinicalpresentationandmortality.



ClinicalSeverityRatingMultiplier

Mild 2x

Mild-Moderate 4x

Moderate 6x

Moderate-Severe 8x

Severe 10x

TableS7.Correspondingdemandmultipliersforeachclinicalseverityrating.

The price per dose of vaccine was estimated by taking the average of prices listed in the CDC

Adult vaccine price list 2016 (55); UNICEF’s 2016 product menu for Gavi, the Vaccine

Alliance (56); and the Pan American Health Organization (PAHO) expanded program of

immunization vaccine prices for 2016 (57). These three averages serve as our vaccine price

for high, low, and middle income countries respectively. We then use these to price each

vaccine based on the income level of the countries most likely to have an outbreak of a

particular disease. (The endemic country/disease/pricing per dose pairings are listed in

Table S8 below.) It should be noted that we took a conservative stance on pricing in our

model, opting to model the lower income country price for diseases that have historically

emerged in nations with differing ability to pay. Our pricing is also likely conservative due to

our inclusion of each vaccine on each menu in our mean calculations, including older

vaccines that are apt to be produced and sold at lower cost than new vaccines.

The annual expected revenue for each vaccine candidate is then given by the price per dose

of vaccine times the expected number of vaccines sold in an outbreak weighted by the

probability of an outbreak occurring in a given year.

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DiseaseCountryIncome

Level



Vaccine

Price/Regimen



Chikungunya Middle Income

$5.55

MERS High Income $46.12

SARS Middle Income

$5.55

Marburg Low Income

$1.97

Rift Valley Fever Middle Income

$5.55

Lassa Fever Low Income

$1.97

Nipah Virus Middle Income

$5.55

Crimean Congo Hemorrhagic Fever Middle Income

$5.55

Zika Virus Middle Income

$5.55



TableS8.Vaccinepriceperdoseassumptionsbasedontheincomelevelofthecountriesmostlikelyto

haveanoutbreakofeachdiseaseincludedintheEIDportfolio.

5. SensitivityAnalysis

This simulation parameterizes several assumptions about the cost and duration of vaccine

development, the probability of success, and pairwise correlations of success between the

projects. These estimates were based on the published literature on vaccine development and

qualitative input from scientists with domain-specific expertise.

In this section, we investigate the robustness of our results to the parameterized assumptions

of our model. We update the investment return statistics of the EID vaccine portfolio as we vary

the development cost and probability of success of each project. The expected return and

return standard deviation associated with the perturbed parameters are given in Tables S9 and

S10.

In Table S9, we find that the expected return of the portfolio increases as the cost per project

decreases. Similarly, Table S10 reports that the expected return of the portfolio increases as

the probability of success of each project increases. However, even under more optimistic

assumptions, the expected annualized return of the megafund for the base case remains

significantly negative, increasing from –61.1% to only –57.4% when the probability of success

is increased by 150%. This sensitivity analysis underscores the robustness of our results, and

demonstrates that an EID vaccine portfolio remains economically unviable even under

relatively optimistic cost and revenue assumptions.

Finally, Table S11 considers the performance of the EID vaccine portfolio under the scenario

where a government agency or philanthropic organization agrees to absorb the initial losses

on the portfolio for a predetermined amount, which we specify as 25% and 50% of our

simulated megafund’s principal. We find that, under the base case scenario, the expected

return increases from –61.1% to –23.6% and –12.6%, respectively. While these scenarios

remain unprofitable, it demonstrates that if combined with other revenue-boosting

mechanisms such as such as advance market commitments and priority review vouchers, the

guarantee structure has the potential to transform an unattractive portfolio of EID vaccine

. CC-BY 4.0 International licenseIt is made available under a

candidates into one that could realistically attract private-sector capital.

Cost per project PV(Profits) E[R5y] E[R1y] SD[R1y] 95% CI SR

$125MM

$0.1MM –100.0 –81.2 2.0 (–85.6, –78.0) –

$1MM –99.7 –70.2 3.1 (–77.1, –65.2) –

$7.6MM (Base) –98.0 –55.3 4.6 (–65.7, –47.8) –

$10MM –97.4 –52.8 4.9 (–63.8, –44.8) –

$100MM –74.1 –25.1 7.8 (–42.6, –12.4) –

$772MM 100.4 12.7 11.7 (–13.6, 31.6) 1.09

$1B 159.3 18.7 12.3 (–9.0, 38.6) 1.52

$10B 2495.0 88.1 19.5 (44.2, 120.1) 4.52

$250MM

$0.1MM –100.0 –83.6 1.7 (–87.4, –80.9) –

$1MM –99.9 –74.1 2.7 (–80.1, –69.7) –

$7.6MM (Base) –99.0 –61.1 4.0 (–70.2, –54.6) –

$10MM –98.7 –58.9 4.3 (–68.5, –51.9) –

$100MM –87.0 –34.8 6.8 (–50.0, –23.9) –

$772MM 0.0 –1.9 10.2 (–24.8, 14.5) –

$1B 29.7 3.3 10.7 (–20.8, 20.6) 0.31

$10B 1197.1 63.8 17.0 (25.6, 91.2) 3.76

$375MM

$0.1MM –100.0 –84.9 1.6 (–88.4, –82.4) –

$1MM –99.9 –76.1 2.5 (–81.7, –72.1) –

$7.6MM (Base) –99.3 –64.1 3.7 (–72.5, –58.1) –

$10MM –99.1 –62.1 3.9 (–70.9, –55.7) –

$100MM –91.4 –39.9 6.2 (–53.9, –29.7) –

$772MM –33.2 –9.5 9.4 (–30.6, 5.9) –

$1B

–13.5 –4.7 9.9 (–27.0, 11.2) –

$10B 764.5 51.0 15.7 (15.8, 76.3) 3.26

TableS9.Sensitivityoftheinvestmentreturns(%)ofaportfolioof141preclinicalEIDvaccine

candidatestoprojectdevelopmentcosts.Thetablereportstheresultsfor[50%,100%,150%]ofthe

$250MMcostperprojectproposedinthestudy.TheSharperatioisestimatedastheratioofthe

expectedreturntothestandarddeviation.PV(Profits),presentvalueofprofitspersuccessfulvaccinein

year5;E[R5y],expected5‐yearreturnoninvestment;E[R1y],expectedannualizedreturn;SD[R1y],

annualizedreturnstandarddeviation;CI,confidenceinterval;SR,Sharperatio.

. CC-BY 4.0 International licenseIt is made available under a

PoS per project PV(Profits) E[R5y] E[R1y] SD[R1y] 95% CI SR

16.2%

$0.1MM –100.0 –86.1 2.3 (–91.1, –82.4) –

$1MM –99.9 –77.9 3.6 (–85.9, –72.2) –

$7.6MM (Base) –99.5 –66.9 5.4 (–78.8, –58.2) –

$10MM –99.4 –65.0 5.7 (–77.6, –55.9) –

$100MM –93.5 –44.5 9.0 (–64.5, –30.1) –

$772MM –50.0 –16.5 13.6 (–46.5, 5.3) –

$1B

–35.2 –12.1 14.3 (–43.7, 10.9) –

$10B 548.2 39.4 22.6 (–10.7, 75.7) 1.74

32.4%

$0.1MM –100.0 –83.6 1.7 (–87.4, –80.9) –

$1MM –99.9 –74.1 2.7 (–80.1, –69.7) –

$7.6MM (Base) –99.0 –61.1 4.0 (–70.2, –54.6) –

$10MM –98.7 –58.9 4.3 (–68.5, –51.9) –

$100MM –87.0 –34.8 6.8 (–50.0, –23.9) –

$772MM 0.0 –1.9 10.2 (–24.8, 14.5) –

$1B 29.7 3.3 10.7 (–20.8, 20.6) 0.31

$10B 1197.1 63.8 17.0 (25.6, 91.2) 3.76

48.6%

$0.1MM –100.0 –82.1 1.3 (–85.2, –80.0) –

$1MM –99.8 –71.6 2.1 (–76.6, –68.4) –

$7.6MM (Base) –98.5 –57.4 3.2 (–64.8, –52.5) –

$10MM –98.1 –55.0 3.3 (–62.8, –49.8) –

$100MM –80.6 –28.7 5.3 (–41.1, –20.5) –

$772MM 50.2 7.4 8.0 (–11.4, 19.7) 0.93

$1B 94.6 13.1 8.4 (–5.9, 26.0) 1.56

$10B 1845.4 79.2 13.3 (48.0, 99.7) 5.95

TableS10.Sensitivityoftheinvestmentreturns(%)ofaportfolioof141preclinicalEIDvaccine

candidatestodevelopmentsuccessrates.Thetablereportstheresultsfor[50%,100%,150%]ofthe

32.4%probabilityofsuccess(PoS)estimateforeachprojectproposedinthestudy.TheSharperatiois

estimatedastheratiooftheexpectedreturntothestandarddeviation.PV(Profits),presentvalueof

profitspersuccessfulvaccineinyear5;E[R5y],expected5‐yearreturnoninvestment;E[R1y],expected

annualizedreturn;SD[R1y],annualizedreturnstandarddeviation;CI,confidenceinterval;SR,Sharpe

ratio.

. CC-BY 4.0 International licenseIt is made available under a

%GuaranteePV(Profits)E[

5y]E[

1y]SD[

1y]95%CIS



$0.1MM –100.0 –83.6 1.7 (–87.4, –80.9) –

$1MM –99.9 –74.1 2.7 (–80.1, –69.7) –

$7.6MM (Base) –99.0 –61.1 4.0 (–70.2, –54.6) –

$10MM –98.7 –58.9 4.3 (–68.5, –51.9) –

$100MM –87.0 –34.8 6.8 (–50.0, –23.9) –

$772MM 0.0 –1.9 10.2 (–24.8, 14.5) –

$1B 29.7 3.3 10.7 (–20.8, 20.6) 0.31

$10B 1197.1 63.8 17.0 (25.6, 91.2) 3.76

25%

$0.1MM –75.0 –24.2 0.0 (–24.2, –24.2) –

$1MM –74.9 –24.1 0.0 (–24.2, –24.1) –

$7.6MM (Base) –74.0 –23.6 0.3 (–24.1, –23.1) –

$10MM –73.7 –23.5 0.3 (–24.0, –22.7) –

$100MM –62.0 –17.8 2.5 (–22.4, –12.7) –

$772MM 10.9 1.2 6.7 (–13.3, 14.8) 0.18

$1B 36.2 5.2 8.0 (–10.9, 20.6) 0.65

$10B 1197.4 63.8 16.9 (25.6, 91.2) 3.78

50%

$0.1MM –50.0 –12.9 0.0 (–12.9, –12.9) –

$1MM –49.9 –12.9 0.0 (–12.9, –12.9) –

$7.6MM (Base) –49.0 –12.6 0.2 (–12.9, –12.3) –

$10MM –48.7 –12.5 0.2 (–12.8, –12.1) –

$100MM –37.0 –8.9 1.7 (–11.9, –5.5) –

$772MM 16.5 2.6 5.0 (–5.8, 14.5) 0.52

$1B 39.5 6.0 6.9 (–4.1, 20.6) 0.87

$10B 1196.7 63.8 16.9 (25.6, 91.2) 3.78

TableS11.Sensitivityoftheinvestmentreturns(%)ofaportfolioof141preclinicalEIDvaccine

candidatesunderagovernment‐backedguaranteestructure.Thetablereportstheresultsfora

guaranteeon[0%,25%,50%]oftheportfolio’sprincipalproposedinthestudy.TheSharperatiois

estimatedastheratiooftheexpectedreturntothestandarddeviation.PV(Profits),presentvalueof

profitspersuccessfulvaccineinyear5;E[R5y],expected5‐yearreturnoninvestment;E[R1y],expected

annualizedreturn;SD[R1y],annualizedreturnstandarddeviation;CI,confidenceinterval;SR,Sharpe

ratio.

. CC-BY 4.0 International licenseIt is made available under a

Acknowledgments

We thank Ellen Carlin, Doug Criscitello, Narges Dorratoltaj, Per Etholm, Jeremy Farrar,

Nimah Farzan, Mark Feinberg, Jose-Maria Fernandez, John Grabenstein, Peter Hale, Richard

Hatchett, Peter Hotez, Daniel Kaniewski, Adel Mahmoud, Kevin Outterson, Chi Heem Wong,

and CEPI leadership for helpful comments and discussion, and Jayna Cummings for editorial

assistance. Research support from the MIT Laboratory for Financial Engineering is gratefully

acknowledged. The views and opinions expressed in this article are those of the authors only,

and do not necessarily represent the views and opinions of any institution or agency, any of

their affiliates or employees, or any of the individuals acknowledged above.

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Vaccine Research and Development (R&D) in the Asia-Pacific: The economics of vaccine R&D and policy recommendations to overcome market failures and promote R&D cooperation

Preprint

Full-text available

Oct 2022

Antonio Postigo

The COVID-19 pandemic has highlighted both the strengths and weaknesses of national, regional, and global vaccine research and development (R&D) systems. Translating public and private R&D efforts into effective vaccines in a timely manner requires not only sufficient financial and scientific resources but also a policy-driven R&D ecosystem that fosters innovation, public-private partnerships, and international cooperation. This paper outlines several supply-side and demand-side factors behind vaccine R&D that generate economic disincentives for pharmaceutical firms to invest in vaccine R&D and can lead to a market failure for vaccines targeting diseases in low-income countries. Most developing countries in Asia-Pacific not only lack the financial and technological resources to invest in vaccine R&D, but it is also not sensible to develop and replicate R&D capabilities in each country. Consequently, low-income countries are dependent on vaccines researched, developed, and manufactured by other nations that they must obtain through trade and international cooperation. The Asia-Pacific region accounts for the largest share of global R&D spending and large shares in publications and patents on vaccine R&D. The region is home to dozens of state-owned and private pharmaceutical firms and contract research organizations that conduct vaccine R&D. Global pharmaceutical firms have not only offshored part of their vaccine manufacturing to Asia-Pacific but also transferred some of their R&D activities. Countries in Asia-Pacific have used several supply-side and demand-side approaches to incentivize investments in vaccine R&D. For instance, high-income countries are major contributors to product development partnerships and philanthropic foundations and have launched programs to boost university-industry R&D ties. During the COVID-19 pandemic, many high- and middle-income countries in the region established advanced market commitments for vaccine doses. The COVID-19 pandemic also showed the possibilities and challenges of international cooperation in vaccine R&D. Pharmaceutical firms in some developing countries built their vaccine R&D capabilities through technological transfer from highincome countries. Regional institutions and intergovernmental organizations in AsiaPacific have also helped promote and coordinate regional cooperation in vaccine R&D. This paper proposes policy actions to stimulate investments in vaccine R&D and promote regional cooperation along four dimensions, namely a) on the prioritization of targets in the vaccine R&D pipeline; b) on how to overcome market failures in vaccine R&D; c) on fostering partnerships between relevant stakeholders at the national and regional levels; and d) on increasing the preparedness and response of national and regional vaccine R&D systems.

Global urbanization and the neglected tropical diseases

Article

Full-text available

Feb 2017
PLOS NEGLECT TROP D

Peter J. Hotez

1918 Influenza: the Mother of All Pandemics

Article

Full-text available

Jan 2006
EMERG INFECT DIS

Assessing the epidemic potential of RNA and DNA viruses

Article

Full-text available

Nov 2016

Many new and emerging RNA and DNA viruses are zoo-notic or have zoonotic origins in an animal reservoir that is usually mammalian and sometimes avian. Not all zoonotic viruses are transmissible (directly or by an arthropod vector) between human hosts. Virus genome sequence data provide the best evidence of transmission. Of human transmissible virus, 37 species have so far been restricted to self-limiting outbreaks. These viruses are priorities for surveillance because relatively minor changes in their epidemi-ologies can potentially lead to major changes in the threat they pose to public health. On the basis of comparisons across all recognized human viruses, we consider the characteristics of these priority viruses and assess the likelihood that they will further emerge in human populations. We also assess the likelihood that a virus that can infect humans but is not capable of transmission (directly or by a vector) between human hosts can acquire that capability.

The Tropical Disease Priority Review Voucher: A Game-Changer for Tropical Disease Products

Article

Full-text available

Aug 2016

The neglected tropical disease priority review voucher (PRV) program ("tropical disease voucher," "voucher") is a U.S. government program intended to enlarge the number of products approved for tropical diseases in the United States. Ridley and others noted that "Infectious and parasitic diseases create enormous health burdens, but because most of the people suffering from these diseases are poor, little is invested in developing treatments." In 2006, these academicians proposed, and in 2007, the U.S. Congress enacted a new section 524 to the Federal Food, Drug, and Cosmetic Act (21 U.S.C. 360n) that provides financial incentives for sponsors of tropical disease products. If a sponsor achieves approval of a new drug application (NDA) or biologics licensing application (BLA) for a new chemical entity (NCE) that constitutes a significant improvement for one of at least 16 listed tropical diseases, the sponsor receives a PRV which can be used for priority review of any subsequent NDA/BLA. "Priority review" means that the Food and Drug Administration (FDA) review occurs with a target of 6 months rather than the standard review period of 10 months. The PRV is transferable and can be sold for use with any other product. An approximately 4-month shorter FDA review time for a future NDA/BLA has clear monetary value providing sufficient financial incentives to develop novel tropical disease products. Note that the creation of this financial incentive costs the U.S. taxpayer essentially nothing: use of a voucher puts drug "X" supported by the voucher at the front of the FDA review line, with the extra voucher user fee paid by the sponsor of drug X compensating the FDA for the extra review effort.

Vaccines and global health: In search of a sustainable model for vaccine development and delivery

Article

Jun 2019

Most vaccines for diseases in low- and middle-income countries fail to be developed because of weak or absent market incentives. Conquering diseases such as tuberculosis, HIV, malaria, and Ebola, as well as illnesses caused by multidrug-resistant pathogens, requires considerable investment and a new sustainable model of vaccine development involving close collaborations between public and private sectors.

Emerging infectious diseases: A proactive approach

Article

Apr 2017

Infectious diseases are now emerging or reemerging almost every year. This trend will continue because a number of factors, including the increased global population, aging, travel, urbanization, and climate change, favor the emergence, evolution, and spread of new pathogens. The approach used so far for emerging infectious diseases (EIDs) does not work from the technical point of view, and it is not sustainable. However, the advent of platform technologies offers vaccine manufacturers an opportunity to develop new vaccines faster and to reduce the investment to build manufacturing facilities, in addition to allowing for the possible streamlining of regulatory processes. The new technologies also make possible the rapid development of human monoclonal antibodies that could become a potent immediate response to an emergency. So far, several proposals to approach EIDs have been made independently by scientists, the private sector, national governments, and international organizations such as the World Health Organization (WHO). While each of them has merit, there is a need for a global governance that is capable of taking a strong leadership role and making it attractive to all partners to come to the same table and to coordinate the global approach.

Disease and economic burdens of dengue

Article

Feb 2017

The burden of dengue is large and growing. More than half of the global population lives in areas with risk of dengue transmission. Uncertainty in burden estimates, however, challenges policy makers' ability to set priorities, allocate resources, and plan for interventions. In this report, the first in a Series on dengue, we explore the estimations of disease and economic burdens of dengue, and the major estimation challenges, limitations, and sources of uncertainty. We also reflect on opportunities to remedy these deficiencies. Point estimates of apparent dengue infections vary widely, although the confidence intervals of these estimates overlap. Cost estimates include different items, are mostly based on a single year of data, use different monetary references, are calculated from different perspectives, and are difficult to compare. Comprehensive estimates that decompose the cost by different stakeholders (as proposed in our framework), that consider the cost of epidemic years, and that account for productivity and tourism losses, are scarce. On the basis of these estimates, we propose the most comprehensive framework for estimating the economic burden of dengue in any region, differentiated by four very different domains of cost items and by three potential stakeholders who bear the costs. This framework can inform future estimations of the economic burden of dengue and generate demand for additional routine administrative data collection, or for systematic incorporation of additional questions in nationally representative surveys in dengue-endemic countries. Furthermore, scholars could use the framework to guide scenario simulations that consider ranges of possible values for cost items for which data are not yet available. Results would be valuable to policy makers and would also raise awareness among communities, potentially improving dengue control efforts.

Moving beyond traditional valuation of vaccination: Needs and opportunities

Article

Dec 2016

Economic evaluations of vaccination traditionally focus on a relatively narrow set of vaccine benefits, such as averted medical care costs among those who are immunized. In recent years, researchers have identified additional vaccination benefits that should be incorporated into economic evaluations in order to reflect vaccination’s full value. Early efforts to estimate the magnitude of these broader benefits suggest that vaccination has been substantially undervalued, which has important implications for public and private vaccine policy and human health and welfare. More and better data will be required to advance this emerging line of research on the value of vaccination. The article discusses promising data sources and methods and research questions needing to be addressed.

Preparing for the next Zika

Article

Apr 2016
NAT BIOTECHNOL

Lessons from the US Biodefense program should inform international efforts to build a medical countermeasure enterprise for emerging infectious diseases.

Antibiotic reimbursement in a model delinked from sales: A benchmark-based worldwide approach

Article

Apr 2016

Despite the life-saving ability of antibiotics and their importance as a key enabler of all of modern health care, their effectiveness is now threatened by a rising tide of resistance. Unfortunately, the antibiotic pipeline does not match health needs because of challenges in discovery and development, as well as the poor economics of antibiotics. Discovery and development are being addressed by a range of public–private partnerships; however, correcting the poor economics of antibiotics will need an overhaul of the present business model on a worldwide scale. Discussions are now converging on delinking reward from antibiotic sales through prizes, milestone payments, or insurance-like models in which innovation is rewarded with a fixed series of payments of a predictable size. Rewarding all drugs with the same payments could create perverse incentives to produce drugs that provide the least possible innovation. Thus, we propose a payment model using a graded array of benchmarked rewards designed to encourage the development of antibiotics with the greatest societal value, together with appropriate worldwide access to antibiotics to maximise human health.

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