Healthcare Resilience
The resilience of healthcare delivery systems—how they adapt to and recover from crises—remains a critical yet underexplored field. This work advances the mission of generating actionable evidence to strengthen quality, equity, and resilience in healthcare. Using performance- and network-based approaches supported by long-term data, we evaluate system behavior under diverse disruptions, including pandemics and extreme events.
Through data-driven methodologies and AI-powered analytical tools, we develop mechanistic models that capture the complex dynamics and interdependencies of healthcare systems. These models yield insights into structure and function, informing strategies to optimize performance, ensure equity, and enhance resilience in the face of future crises.
Fund
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Funded by COVID-19 Research Accelerator Grants (Bill & Melinda Gates Foundation)
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Title: Harness network science to reduce treatment resource disparities and social disadvantages
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People: Lu Zhong (PI), Jianxi Gao (PI)
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Assess the impact of disruptions (e.g., the COVID-19 pandemic) on healthcare systems, examine how the disruption contributes to health disparities, and develop evidence-based strategies for improving access to healthcare services and resources, and for building more resilient healthcare systems by leveraging analysis on electronic health record (EHR) data analysis, modeling, and simulations.
Publications
Healthcare System Resilience and Adaptability to Pandemic Disruptions in the United States.
Nature Medicine (2024).
Zhong, L., Lopez, D., Pei, S., Gao, J.
Patient flow networks absorb healthcare stress during pandemic crises.
(Under review) 2024.
Zhong, L., Pei, S., Gao, J.
[paper] [code]
Examining inequality in healthcare utilization during pandemic disruptions.
(under review) 2025.
Lian J., Pei, S., Gao, J., Zhong, L.
[paper] [code]
Reinforcement learning enhances system adaptation to recurrent disruptions from crisis.
(working paper) 2025.
Zhong, L., et al.
[paper] [code]
Human Mobility & Resilience
Human mobility plays a pivotal role in shaping resilience to public health crises and other systemic disruptions. Analyzing mobility data—from fine-grained individual trajectories to commuting networks and global air travel flows—enables the development of models that capture how population movement influences disease transmission. Such models support the simulation of epidemic dynamics, the evaluation of intervention strategies, and the identification of critical leverage points for mitigation.
The resulting evidence base informs the design of more effective public health policies and resilience frameworks, strengthening societal capacity to anticipate, manage, and recover from infectious disease outbreaks and mobility-related challenges.
Selected Publications
Universal expansion of human mobility across urban scales
Nature Cities (2025).
Zhong, L., Dong, L., Wang, Q., Song, C., Gao, J.
Switching exploration modes in human mobility
(Under review) 2025.
Zhong, L., Dong, L., Wang, Q., Song, C., Gao, J.
[paper] [code]
Identifying the shifting sources predicts the dynamics of COVID-19 in US
Chaos: An Interdisciplinary Journal of Nonlinear Science 32.3 (2022).
Wang, Y., Zhong, L., Du, J., Gao, J., Wang, Q.
[paper] [code]
Vaccination and three non-pharmaceutical interventions determine the dynamics of COVID-19 at 381 metropolitan statistical areas in the US.
Humanities and Social Sciences Communications (2022).
Zhong, L., Diagne, M., Wang, Q., Gao, J.
Country distancing increase reveals the effectiveness of travel restrictions in stopping COVID-19 transmission.
Communications Physics 4, 121 (2021) [cover story].
Zhong, L., Diagne, M., Wang, W., Gao, J.
[paper] [code]
Optimizing HIV Interventions for Mulitplex Social Networks Via Partition-Based Random Search.
IEEE Transactions on Cybernetics, vol. 48, no. 12, (2018).
Zhang, Q., Zhong, L., Gao, S., Li, X.
[paper] [code]
Zhong, L., Zhang, Q., Li, X.
[paper] [code]
Publication
Resilience hinges on persistence in scientific collaboration
(Under review) 2025.
Chen H., Bu Yi., Zhong, L. , Du C., Meyer E., Ding Y., Gao J.
Socio-technical resilience
Socio-technical systems—spanning people, organizations, technologies, and infrastructures—are dynamic, complex, and evolving. Strengthening their resilience demands methods that capture adaptive, interdependent, and emergent properties. Beyond static assessments, resilience frameworks must model nonlinear dynamics, feedback loops, and multi-scale interactions to anticipate vulnerabilities, guide adaptive decision-making, and maintain robustness amid disruptions.
Data Source
