The COVID Emergency is Almost Over—What Did We Learn About Rapid R&D?
This month marks three years since the COVID pandemic took hold of nearly every aspect of American life. In a few more months (May 2023) the coronavirus public health emergency is set to conclude officially, following the Administration’s announcement to wind down the declarations. As the nation grapples with the tragedies and lasting effects of the pandemic, it should also take lessons from the most successful elements of how the government responded to the challenge. The most notable success might be Operation Warp Speed (OWS), the highly successful public-private partnership that produced and distributed millions of live-saving vaccines in record time. Our new memo, How to Replicate the Success of Operation Warp Speed, helps this audience assess how and if they even should attempt to replicate the approach.
The Federation of American Scientists, together with our partners at 1Day Sooner and the Institute for Progress (IFP), convened leadership from the original OWS team, agency heads, Congressional staffers, researchers, and representatives from vaccine manufacturers in November 2022 to reflect on the success of the program and future applications of the model. The memo was developed primarily from notes on presentations, panel discussions, and lively breakout conversations that were both reflective and forward looking. This piece complements other analyses by providing a practical, playbook-style approach. Those looking to replicate the success of OWS should consider the stakeholder landscape and the state of fundamental science before designing a portfolio of policy interventions.
Assess the stakeholder landscape and science surrounding the challenge
A program on the exact scale of OWS will only work for major national challenges that are self-evidently important and urgent. Designers should assess the stakeholder landscape and consider the political, regulatory, and behavioral contexts. The fundamental research must exist, and the goal should require advancing it for a specific use case at scale. Technology readiness levels (TRLs) can help guide technology assessment—a level of at least 5 is a good bet. All decisions about technology readiness should be made using the best available science, data, and demonstrated capabilities.
Design an agile program by selecting a portfolio of interventions
Choose a selection of the mechanisms below informed by the stakeholder and technology assessment. The organization of R&D, manufacturing, and deployment should be inspired by agile methodology, in which planning is iterative and more risk than normal is accepted.
- Establish a leadership team across federal agencies
- Coordinate federal agencies and the private sector
- Activate latent private-sector capacities for labor and manufacturing
- Shape markets with demand-pull mechanisms
- Reduce risk with diversity and redundancy
Operation Warp Speed was a historic accomplishment on the level of the Manhattan Project and the Apollo program, but the unique approach is not appropriate for everything. Read the full memo to understand the mechanisms and the types of challenges best suited for this approach. Even if a challenge does not meet the criteria for the full OWS treatment, the five mechanisms can be applied individually to better coordinate agencies and the private sector toward solutions.
The incoming administration must act to address bias in medical technology at the development, testing and regulation, and market-deployment and evaluation phases.
The incoming administration should work towards encouraging state health departments to develop clear and well-communicated data storage standards for newborn screening samples.
Proposed bills advance research ecosystems, economic development, and education access and move now to the U.S. House of Representatives for a vote
NIST’s guidance on “Managing Misuse Risk for Dual-Use Foundation Models” represents a significant step forward in establishing robust practices for mitigating catastrophic risks associated with advanced AI systems.