New research from Johns Hopkins Applied Physics Laboratory (APL) demonstrates the critical role of testing in pandemic preparedness. It estimates that public-private efforts to produce and distribute COVID-19 tests saved 1.4 million lives and prevented 7 million hospitalizations in the U.S. The findings were published in The Lancet Public Health.
Key findings and tools
In collaboration with federal agencies and the MITRE Corporation, APL used a digital twin prototype — a virtual simulation tool — to model the COVID-19 testing and diagnostic supply chain. This tool simulated baseline scenarios and assessed interventions, quantifying the impact of disruptions and policies on test availability.
“The digital twin helps us quantitatively understand the impact and consequences of disruptions and changing infection levels on test availability,” said Elizabeth Currier, the APL digital twin project manager. “It can also evaluate the impact of policies and investments and be used in planning and evaluating supply needs, aiding in response and ensuring a secure supply chain for future medical crises.”
- Insights: The model integrated diverse data (for example, manufacturing, retail, government stockpiles, and wastewater) to simulate demand, production, and distribution of tests.
- Output: Between January 2020 and December 2022, U.S. efforts produced over 6.7 billion COVID-19 tests, with 2.7 billion performed in labs, healthcare facilities, or at home.
Preparing for future threats
The study emphasizes the importance of coordinated, rapid test development and distribution for future public health crises. APL’s digital twin framework has since expanded to include monitoring for other diseases, such as influenza and RSV, providing a scalable tool for public health response.
“The findings underscore the importance of robust and rapid test development, production, and distribution to address future public health threats,” Currier said. “The insights gained from integrating data go beyond responding to COVID-19: They prepare us for future pandemics with a scalable framework to allocate resources effectively.”
