An optimal mechanism to fund the development of vaccines against emerging epidemics.

We derive the optimal funding mechanism to incentivize development and production of vaccines against diseases with epidemic potential. In the model, suppliers' costs are private information and investments are noncontractible, precluding cost-reimbursement contracts, requiring fixed-price contracts conditioned on delivery of a successful product. The high failure risk for individual vaccines calls for incentivizing multiple entrants, accomplished by the optimal mechanism, a (w+1)-price reverse Vickrey auction with reserve, where w is the number of selected entrants. Our analysis determines the optimal number of entrants and required funding level. Based on a distribution of supplier costs estimated from survey data, we simulate the optimal mechanism's performance in scenarios ranging from a small outbreak, causing harm in the millions of dollars, to the Covid-19 pandemic, causing harm in the trillions. We assess which mechanism features contribute most to its optimality.

Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis.

This study reports on the application of a Portfolio Decision Analysis (PDA) to support investment decisions of a non-profit funder of vaccine technology platform development for rapid response to emerging infections. A value framework was constructed via document reviews and stakeholder consultations. Probability of Success (PoS) data was obtained for 16 platform projects through expert assessments and stakeholder portfolio preferences via a Discrete Choice Experiment (DCE). The structure of preferences and the uncertainties in project PoS suggested a non-linear, stochastic value maximization problem. A simulation-optimization algorithm was employed, identifying optimal portfolios under different budget constraints. Stochastic dominance of the optimization solution was tested via mean-variance and mean-Gini statistics, and its robustness via rank probability analysis in a Monte Carlo simulation. Project PoS estimates were low and substantially overlapping. The DCE identified decreasing rates of return to investing in single platform types. Optimal portfolio solutions reflected this non-linearity of platform preferences along an efficiency frontier and diverged from a model simply ranking projects by PoS-to-Cost, despite significant revisions to project PoS estimates during the review process in relation to the conduct of the DCE. Large confidence intervals associated with optimization solutions suggested significant uncertainty in portfolio valuations. Mean-variance and Mean-Gini tests suggested optimal portfolios with higher expected values were also accompanied by higher risks of not achieving those values despite stochastic dominance of the optimal portfolio solution under the decision maker's budget constraint. This portfolio was also the highest ranked portfolio in the simulation; though having only a 54% probability of being preferred to the second-ranked portfolio. The analysis illustrates how optimization modelling can help health R&D decision makers identify optimal portfolios in the face of significant decision uncertainty involving portfolio trade-offs. However, in light of such extreme uncertainty, further due diligence and ongoing updating of performance is needed on highly risky projects as well as data on decision makers' portfolio risk attitude before PDA can conclude about optimal and robust solutions.

Designing Pull Funding For A COVID-19 Vaccine.

A widely accessible vaccine is essential to mitigate the health and economic ravages of coronavirus disease 2019 (COVID-19). Without appropriate incentives and coordination, however, firms might not respond at sufficient speed or scale, and competition among countries for limited supply could drive up prices and undercut efficient allocation. Programs relying on "push" incentives (direct cost reimbursement) can be complicated by the funder's inability to observe firms' private cost information. To address these challenges, we propose a "pull" program that incentivizes late-stage development (Phase III trials and manufacturing) for COVID-19 vaccines by awarding advance purchase agreements to bidding firms. Using novel cost and demand data, we calculated the optimal size and number of awards. In baseline simulations, the optimal program induced the participation of virtually all ten viable vaccine candidates, spending an average of $110 billion to generate net benefits of $2.8 trillion-nearly double the net benefits generated by the free market.

CEPI: Driving Progress Toward Epidemic Preparedness and Response.

The Coalition for Epidemic Preparedness Innovations (CEPI) was formed in the aftermath of the 2014-2015 Ebola outbreak in west Africa to support the development of vaccines that could improve the world's preparedness against outbreaks of epidemic infectious diseases. Since its launch in 2017, CEPI has mobilized more than US$750 million to support its mission to develop vaccines against agents such as Lassa virus, Middle East respiratory syndrome coronavirus, and Nipah virus, as well as several rapid-response vaccine platforms to accelerate response times to unexpected epidemic threats. CEPI has also played a leading role in fostering institutional partnerships between public- and private-sector organizations to optimize allocation of resources for vaccine development against its priority pathogens. CEPI's priorities include diversification of its current vaccine research and development investment portfolio to include additional pathogens, such as Rift Valley fever and chikungunya; establishment of technical and regulatory pathways for vaccine development across CEPI's portfolio; development of sustainable manufacturing solutions for vaccine candidates nearing completion of safety and immunogenicity testing in humans; and creation of investigational stockpiles of its vaccine candidates for use in emergency situations. This commentary provides an overview of the global health challenges CEPI was established to address and its achievements to date, and indicates priorities for funding and coordination in the coming years.

A review of Lassa fever vaccine candidates.

Lassa fever is a zoonotic disease caused by the Lassa virus, a rodent-borne arenavirus endemic to West Africa. Recent steady increase in reported cases of the disease in Nigeria, where 123 deaths occurred in 546 confirmed cases in 2019 has further underlined the need to accelerate the development of vaccines for preventing the disease. Intensified research and development of Lassa fever medical countermeasures have yielded some vaccine candidates with preclinical scientific plausibility using predominantly novel technology. The more advanced candidates are based on recombinant measles, Vesicular Stomatitis Virus or Mopiea and Lassa virus reassortants expressing Lassa virus antigens, and the deoxyribonucleic acid platform. However, the Lassa fever portfolio still lags behind other neglected tropical diseases', and further investments are needed for continued development and additional research, such as the safety and efficacy of these vaccine candidates in special populations.

Estimating the cost of vaccine development against epidemic infectious diseases: a cost minimisation study.

BACKGROUND: The Coalition for Epidemic Preparedness Innovations was established in 2016, to develop vaccines that can contribute to preparedness for outbreaks of epidemic infectious diseases. Evidence on vaccine development costs for such diseases is scarce. Our goal was to estimate the minimum cost for achieving vaccine research and development preparedness targets in a portfolio of 11 epidemic infectious diseases, accounting for vaccine pipeline constraints and uncertainty in research and development preparedness outcomes. METHODS: We assembled a pipeline of 224 vaccine candidates from preclinical through to phase 2 for 11 priority epidemic infectious diseases. We used a linear regression model to identify drivers of development costs from preclinical through to end of phase 2a. Drawing from published estimates of vaccine research and development probabilities of success, we simulated costs for advancing these 224 vaccine candidates through to the end of phase 2a. We combined these findings to determine minimum costs for progressing at least one vaccine through to the end of phase 2a per epidemic infectious disease by means of a stochastic optimisation model. FINDINGS: The cost of developing a single epidemic infectious disease vaccine from preclinical trials through to end of phase 2a is US$31-68 million (US$14-159 million range), assuming no risk of failure. We found that previous licensure experience and indirect costs are upward drivers of research and development costs. Accounting for probability of success, the average cost of successfully advancing at least one epidemic infectious disease vaccine through to the end of phase 2a can vary from US$84-112 million ($23 million-$295 million range) starting from phase 2 to $319-469 million ($137 million-$1·1 billion range) starting from preclinical. This cost includes the cumulative cost of failed vaccine candidates through the research and development process. Assuming these candidates and funding were made available, progressing at least one vaccine through to the end of phase 2a for each of the 11 epidemic infectious diseases would cost a minimum of $2·8-3·7 billion ($1·2 billion-$8·4 billion range). INTERPRETATION: Our analysis provides new evidence on vaccine research and development pipelines and associated costs for 11 epidemic infectious diseases, highlighting both funding needs and research and development gaps for achieving vaccine research and development preparedness targets. FUNDING: This work was partly supported by the Research Council of Norway through the Global Health and Vaccination Programme GLOBVAC.

Gavi's policy steers country ownership and self-financing of immunization.

This commentary examines the 2014 NIPH evaluation of Gavi's co-financing policy and comments on the appropriateness of the subsequent and most significant policy changes taking effect in 2016.