A Quantitative Approach to Vaccine Funding

By Open Philanthropy @ 2025-05-30T12:51 (+24)

This is a linkpost to https://www.openphilanthropy.org/research/a-quantitative-approach-to-vaccine-funding/

Introduction

At Open Philanthropy, we often use the Importance, Neglectedness, and Tractability framework when making decisions about how to prioritize spending across cause areas. In this post, we apply the framework to vaccine research and development (R&D) by comparing potential vaccine targets using metrics of importance and neglectedness.

Our main takeaway from this exercise: some diseases that kill many people receive very few resources for the development of cures and vaccines. In relative terms, R&D funding varies by 10 times or more across infectious diseases, often without good reason.[1] This disparity suggests that science funders could find unusually high-impact opportunities by targeting the most neglected diseases.

We use importance-neglectedness analysis like this as a starting point for more specific scientific landscapes and grant investigations focused on tractability. Taken together, these analyses have led us to believe that group A streptococcus (strep A) vaccines, syphilis vaccines, and hepatitis C vaccines are promising areas for philanthropic support of vaccine development. (There are other areas we believe are also high impact, but for reasons that have less to do with field-level neglectedness, and are more to do with importance and tractability.)

 

Past work on vaccine R&D

Since 2016, around 30% (or more than $150 million) of our grantmaking in scientific research and global health R&D has been related to vaccine R&D (inclusive of clinical trials).

We have made five or more grants for vaccine development in:

We have also made grants in other areas of vaccine research when an opportunity seemed particularly strong. Sample areas include influenza, cholera, hepatitis E, schistosomiasis, and cancers (prophylactic).

 

Data sources and methodology

To compare importance and neglectedness, we started by listing 84 prominent infectious diseases and etiologies that prophylactic vaccines could target by combining the list of communicable causes of death or injury from the Global Burden of Disease (GBD) study,[2] the etiologies for which GBD provides estimates,[3] CEPI’s priority pathogens list, and a few other diseases and etiologies we felt were relevant. 

To generate initial estimates of the “importance” for each of these 84 targets, we used the GBD’s forecasts for how many Years of Life Lost and Years Lived with Disability are expected from each. To generate initial estimates of “neglectedness”, we used (1) funding data for poverty-related diseases from the annual G-FINDER survey[4] and (2) information on the number of vaccine products that are currently available to the public, mainly from Saloni Dattani’s vaccine timeline dataset, supplemented by information from the U.S. FDA and the WHO. This list is unlikely to capture every vaccine, but we think it’s a relatively complete starting set.[5]

 

Importance and neglectedness metrics

By connecting these data sources, we can compare different diseases/etiologies across useful indicators of importance and neglectedness. For example, the table below shows the top 25 targets in terms of future disease burden. We specifically look at the estimated burden for the year 2050, since R&D investments today only pay off decades into the future[6] (making future burden a better way to assess the potential health impact of grants today).

After we filter by future burden, we look at a neglectedness metric. In particular, we compare the DALY burden to R&D funding according to G-FINDER[7] to get a ratio of dollars spent relative to the size of the problem. A disease that gets less R&D funding per projected DALY is more neglected, and may be more deserving of new funding (though in practice, we haven’t found this to be a perfect rule; we discuss some exceptions below).

Note: G-FINDER only measures a subset of total funding for each disease/etiology. See more details here, point #4.

Finally, we look at which shortlisted diseases and etiologies do not yet have a vaccine, to get a perspective on what new vaccine R&D could target. While this is a limited perspective — as we explain below (point #7), some diseases have imperfect vaccines in need of improvement — it can still provide useful context.

 

Table: Importance and neglectedness metrics of potential high-burden vaccine targets*

TargetDALY forecast (2050, rounded)R&D funding* per year (2019 to 2023 rounded average)R&D$* per year/2050 DALYs# of vaccines widely available (lower bound)*
Malaria33,500,000$703,000,000$21.02
Tuberculosis24,800,000$795,000,000$32.01
Hepatitis B23,000,000$21,000,000$0.93
Hepatitis C21,800,000$18,000,000$0.80
HIV/AIDS18,400,000$1,541,000,000$83.70
Streptococcus pneumoniae17,800,000$48,000,000$2.76
Group A streptococcus17,400,000$14,000,000$1.20
Rotavirus10,600,000$32,000,000$3.04
Staphylococcus aureus8,700,000  0
Influenza8,000,000  11
Pertussis7,800,000  5
Shigella7,100,000$51,000,000$7.30
Klebsiella pneumoniae6,300,000  0
Adenovirus6,200,000 $3.70
Cryptosporidium5,600,000$22,000,000$3.90
Respiratory syncytial virus4,800,000  2
Syphilis4,300,000$9,000,000$2.10
Invasive Non-typhoidal Salmonella (iNTS)4,200,000$10,000,000$2.50
Norovirus4,100,000 $3.70
Enterotoxigenic E. coli4,000,000$9,000,000$2.30
E. coli causing Meningitis/LRI3,500,000  0
Enteropathogenic E. coli3,400,000 $3.70
Typhoid fever3,400,000  2
Cholera3,100,000$39,000,000$12.62
Pseudomonas aeruginosa2,900,000  0

*See footnotes 4 and 6. Also, note that we didn’t count vaccines that were available only in very few countries, and that likely need more studies to become widely available (this was the case for Shigella and Hepatitis E). Similarly, we didn’t count the Adenovirus vaccine, since it’s only available for United States military personnel.

 

We make rough extrapolations where data was lacking. Figures in the table above that rely on these extrapolations are colored grey and in italics.

For example, for simplicity we assume that diseases and etiologies without GBD estimates will decline at the same rate as the average decline projected for diseases whose future impact GBD has estimated. When G-FINDER and GBD categories don’t match well, we use proxies to calculate the ratio of R&D funding to DALY burden (e.g., inputting the R&D$/DALY of diarrheal diseases as a whole for each specific diarrheal etiology). More details are available in the spreadsheet.

 

Takeaways

The analysis above provides a shortlist of potential vaccine R&D areas that may be worth further investigation. Here is a closer look at the top seven targets in terms of burden, each projected to account for more than 15 million DALYs in 2050:

 

Chart: DALY forecast and R&D funding of the most burdensome diseases/etiologies Chart: R&D$ per DALY of the most burdensome diseases/etiologies

(Same units as previous chart)

Note for both charts: G-FINDER only measures a subset of total funding for each disease/etiology. More details here, point #4. Figures were rounded.

 

These figures give us prompts for further investigation. For example:

 

Limitations

While this was a valuable exercise, we do not take these R&D$/DALY numbers “literally” without further context. That is for a few reasons:

 

Conclusion

Even with the above limitations in mind, this exercise reaffirmed a striking pattern: some diseases get much less R&D funding (relative to others) than their future health burden suggests would be beneficial. That gives us a useful place to start when figuring out which disease areas might be productive to investigate for grantmaking. Alongside recommendations from people in the global health community,[12] analysis of this form has led us to investigate — and make — grants related to strep A, syphilis, and hepatitis C vaccine development. Similar analysis led to grantmaking in other neglected areas outside of vaccines such as development of cures for hepatitis B, technical assistance for sickle cell disease screening and treatment, and development of antivenoms for snakebites.

 

Appendix: All data + analysis

Here is the spreadsheet with our data analysis (we suggest you read the limitations section before opening that link). The main tab lists potential diseases and etiologies new vaccines could target, then characterizes each of these “targets” by how many vaccines exist for them, their estimated burden, their R&D funding, etc. The summary tab shows key analyses, including the one that led to the table above. Other tabs serve as input for the main tab.

We appreciate any feedback on the spreadsheet or this post; please share it with rafael.dib@openphilanthropy.org.

  1. ^

    This finding mirrors the 10/90 gap discussed over the previous generation of global health research. Our analysis here is more specific to particular diseases, rather than the disparity between higher-income and lower-income countries per se.

  2. ^

    Institute for Health Metrics and Evaluation (IHME). Global Burden of Disease Study 2021 (GBD 2021). Seattle, WA: IHME, University of Washington, 2024. https://www.healthdata.org/research-analysis/gbd-data. First accessed on Sep 17, 2024.

  3. ^

    Etiology is basically equivalent to pathogen in this context. We kept etiology as it’s the language used by GBD. Note that this does not include all communicable diseases and etiologies in GBD. COVID-19 was excluded because it’s hard to compare to other diseases, given its sudden peak and subsequent declines. Encephalitis wasn’t included as a whole because it seemed more useful to look at particular types of encephalitis. Similarly, we didn’t include meningitis or otitis media as diseases, but we did include most of their etiologies. Guinea worm disease wasn’t included because GBD estimated it only had ~1 DALY in 2021. “Chlamydial infection” disease from GBD was excluded because we already are looking at chlamydial spp. “Fungus”, “polymicrobial”, “other bacterial pathogen”, “viral etiologies of meningitis”, and other GBD etiologies of lower respiratory infections and meningitis were excluded because they are too generic for this exercise. “Non-typhoidal Salmonella” GBD etiology was excluded because it could have an overlap with iNTS, which was included.

  4. ^

    Impact Global Health, G-FINDER data portal 2024, https://gfinderdata.impactglobalhealth.org. Accessed on Nov 25, 2024.

  5. ^

    We looked at this information in November 2024 and relied heavily on Saloni Dattani’s dataset, which “shows when each vaccine was introduced for humans, for the first time. […] The dataset also shows new versions of some vaccines and when they were made available. This hopefully covers most of them but is likely incomplete, and it was harder to track down the exact years for several historical ones.” Note that vaccines are not included if they were withdrawn or never made widely available.

  6. ^

    A good rule of thumb is that it takes ~20 years to go from science to technology. Once a new vaccine is approved, it also takes a number of years to be integrated into health systems to provide maximal benefit.

  7. ^

    Note that G-FINDER includes only a subset of total funding; see more details here, point #4.

  8. ^

    For example, while the reference estimate of the malaria burden in 2040 is ~36 million DALYs, its 95% confidence interval ranges from ~12 million to ~84 million DALYs.

  9. ^

    The pharmaceutical industry has made substantial investments into drug development for hepatitis C, but many of those investments are excluded from G-FINDER because they target high-income country markets. However, some of those investments are starting to benefit LMICs too, as drugs developed in high income countries become available in lower income countries.

  10. ^

    For example, vaccinating pregnant women is expected to not only provide personal protection but to reduce the risk of fetal mortality or birth defects caused by the syphilis bacterium, which can cross the placental barrier.

  11. ^

    Immunization efforts are often integrated with maternal and child health services. In addition, adults don’t have as many routine contact points with health systems.

  12. ^

    A tip from George Rugarabamu for rheumatic fever, and from Damian Walker for syphilis, to name a couple. Thank you!


SummaryBot @ 2025-06-03T16:18 (+1)

Executive summary: Open Philanthropy presents a quantitative framework for identifying promising vaccine R&D targets by analyzing disease burden and funding gaps, concluding that diseases like group A streptococcus, syphilis, and hepatitis C are particularly neglected relative to their projected future impact and merit greater philanthropic investment.

Key points:

  1. Framework and main finding: Applying an importance-neglectedness framework to 84 infectious diseases reveals significant disparities—some high-burden diseases receive up to 10x less R&D funding than others, suggesting overlooked opportunities for impact.
  2. Top neglected targets: Group A streptococcus, hepatitis C, and syphilis have among the lowest R&D funding per projected DALY in 2050 and no widely available vaccines, making them compelling candidates for philanthropic support.
  3. Methodology and limitations: Estimates are based on GBD projections, G-FINDER funding data, and vaccine availability, with acknowledged gaps (e.g. data exclusions, inconsistent disease groupings, and uncertainty in burden forecasts).
  4. Insights into specific diseases: Some high-burden diseases like malaria and TB already receive substantial funding but still lack effective vaccines; others, like hepatitis B, appear underfunded due to data exclusions (e.g. high-income country focus).
  5. Grantmaking implications: This analysis has informed Open Phil's vaccine grant portfolio and led to investments in other neglected areas such as hepatitis B cures, syphilis testing, and antivenom development.
  6. Broader cautions: The authors emphasize that quantitative metrics alone are insufficient; tractability, implementation context, and expert judgment are essential for effective prioritization.

 

 

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