Purdue University researchers develop novel framework to guide pandemic mitigation strategies

In a groundbreaking study, researchers from Purdue University and the University of Illinois Urbana-Champaign (U. of I.) have unveiled a novel framework to analyze, evaluate, and dynamically manage epidemic intervention strategies using reproduction number estimates. The findings, validated with real-world data from the COVID-19 pandemic, offer critical insights for policymakers and public health officials seeking to optimize response efforts during future outbreaks.
Traditional epidemic models often struggle with uncertainties stemming from complex transmission behaviors, human factors, and limited data. The proposed framework sidesteps these challenges by focusing on the reproduction number—a key metric indicating the average number of secondary infections caused by an infected individual. Leveraging the basic and effective reproduction numbers as pandemic indicators and control goals, this framework enables data-driven counterfactual analysis, strategy evaluation, and adaptive control of epidemic interventions.
“This framework provides a powerful tool for evaluating intervention strategies, in order to see how effective measures were and what could have happened if nothing was done,” said Philip E Paré, Rita Lane and Norma Fries Assistant Professor in Purdue’s Elmore Family School of Electrical and Computer Engineering. “Further, we offer a tool to enable dynamically adjusting responses based on real-time data. Together this framework bridges the gap between theoretical approaches and real-world applications, offering a path forward for managing uncertainties in epidemic control.”
Inspired by the success of interventions at Purdue and U. of I. during the COVID-19 pandemic, the team reverse-engineered the testing-for-isolation strategies implemented on both campuses. Their framework examined what might have happened had no interventions been deployed. Leveraging insights from this analysis, the researchers proposed a control strategy that uses the effective reproduction number as both a feedback indicator and a control goal. This dual function enables dynamic adjustments to testing-for-isolation strategies, optimizing the intensity of interventions in response to future outbreaks.
Paré says researchers outlined three core innovations in the framework:
- Quantifying Intervention Impact: A method to assess how the "testing-for-isolation" strategy affects the basic reproduction number.
- Reverse Engineering Scenarios: A technique to reconstruct hypothetical scenarios by estimating the effective reproduction number under varying intervention intensities.
- Closed-Loop Feedback Control: An algorithm that uses the effective reproduction number as both feedback and a control target to dynamically adjust intervention measures.
The study applied the framework to COVID-19 data from Purdue and U. of I., addressing three critical questions:
- Impact Without Interventions: What would have been the severity of the outbreak without the implemented strategies?
- Varying Intervention Strengths: How would different levels of intervention intensity have altered the outbreak’s trajectory?
- Dynamic Adjustment: Can intervention strategies be optimized in real-time using feedback from the effective reproduction number?
Counterfactual analyses revealed that the testing-for-isolation strategies significantly reduced the reproduction number, mitigating virus spread on both campuses. The closed-loop control algorithm further demonstrated the potential to outperform fixed strategies by dynamically adapting to changes in the epidemic's severity. By leveraging the reproduction number as a central metric, the framework offers a practical and scalable approach to epidemic management.
Paré says the results are promising, indicating that dynamic adjustment strategies guided by real-time reproduction number feedback could be instrumental in controlling future outbreaks. He says researchers emphasize the importance of refining the framework with diverse datasets and exploring additional intervention strategies. They envision their methodology as a cornerstone for managing future public health crises.
This study highlights the pivotal role of interdisciplinary research in addressing complex global challenges. By combining epidemiology, data science, and control theory, the framework equips policymakers with actionable insights to safeguard communities and mitigate the impact of pandemics.
A paper detailing these findings, entitled “A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates”, was published in the journal PLOS Computational Biology. This work is part of Purdue’s One Health initiative. It was completed with financial support from the Chemical Measurement and Imaging Program in the National Science Foundation Division of Chemistry.