Dynamic Resilience Assessment for Urban Healthcare Infrastructure Operations Considering the Spatiotemporal Characteristics of Public Health Emergencies
Publication: Journal of Management in Engineering
Volume 40, Issue 6
Abstract
Healthcare infrastructure (HI) is essential for ensuring access to quality medical services, controlling diseases, and enhancing human health and the overall livability of cities. As public health emergencies such as COVID-19 pose considerable challenges, effectively assessing and enhancing the resilience of HI operations has become imperative. Notably, public health emergencies exhibit significant spatial and temporal variability, and HI is a complex system comprising multiple units with various interactions such as geographic relationships and social cooperation, which need to be carefully considered in resilience assessment. This study proposes an integrated framework for dynamic resilience assessment of HI operations, considering spatiotemporal characteristics of emergencies and multiple interrelations among healthcare facilities. First, an improved susceptible-exposed-infectious-removed-hospitalized-Fangcang-dead model was developed to simulate various scenarios across different epidemic phases, given the spatial heterogeneity in outbreak locations and temporal variability in transmission capacity. Furthermore, considering the multiple interrelations among hospitals, the complex HI network was constructed by integrating geographic locations and social cooperation. Finally, under various outbreak scenarios, the cascading failure process of HI was accurately simulated to dynamically assess resilience during different time periods of the epidemic. The proposed method was applied to assess the resilience of HI operations during the COVID-19 pandemic in Wuhan, China, demonstrating its effectiveness and applicability. It was found that 38% of the hospitals in the entire HI system experienced failure during the initiation phase of the epidemic, and this percentage rose to 78% during the acceleration phase. Moreover, HI’s operational resilience presented obvious variations among different outbreak scenarios. Dynamic resilience assessment under various epidemic scenarios can inform the development of targeted optimization strategies for improving HI’s operational resilience, encompassing pre-emergency prevention, during-emergency response, and postemergency restoration. Our study provides valuable insights into enhancing HI’s operational resilience, which has obvious strengths in supporting the optimization of management strategies during public health emergencies.
Practical Applications
Healthcare infrastructure (HI) plays a vital role in crisis response for the prevention and control of epidemics. Accurately assessing and improving the operational resilience to public health emergencies is crucial for maintaining the reliable services they provide. This study proposes an integrated framework for dynamic resilience assessment of HI operations, carefully considering both the spatiotemporal characteristics of emergencies and the multiple interrelations among healthcare facilities. Based on dynamic assessment of HI’s operational resilience under various epidemic scenarios, targeted optimization and management strategies can be developed for resilience improvement throughout the entire process, regarding pre-emergency prevention, during-emergency response, and postemergency restoration. The proposed framework offers a fine-grained assessment of the dynamic evolution of HI’s operational resilience and emphasizes the critical role of infrastructure network construction and emergency scenario setting in resilience planning and risk management. This study provides valuable guidance for creating optimal system configuration and emergency response strategies that can be implemented to foster the resilience of HI.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Specific data available include patient data and healthcare infrastructure data used in this study.
Acknowledgments
This work was supported by the National Science Fund for Excellent Young Scholars of China (Grant No. 72122007) and the National Natural Science Foundation of China (NSFC) (Grant Nos. 72071089 and 71821001).
References
Alderwick, H., A. Hutchings, A. Briggs, and N. Mays. 2021. “The impacts of collaboration between local health care and non-health care organizations and factors shaping how they work: A systematic review of reviews.” BMC Public Health 21 (1): 753. https://doi.org/10.1186/s12889-021-10630-1.
Arsenault, C., et al. 2022. “COVID-19 and resilience of healthcare systems in ten countries.” Nat. Med. 28 (6): 1314–1324. https://doi.org/10.1038/s41591-022-01750-1.
Balakrishnan, S., and Z. Zhang. 2020. “Criticality and susceptibility indexes for resilience-based ranking and prioritization of components in interdependent infrastructure networks.” J. Manage. Eng. 36 (4): 04020022. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000769.
Boccaletti, S., V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. 2006. “Complex networks: Structure and dynamics.” Phys. Rep. 424 (4–5): 175–308. https://doi.org/10.1016/j.physrep.2005.10.009.
Chen, Y., Q. Li, H. Karimian, X. Chen, and X. Li. 2021. “Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China.” Sci. Rep. 11 (1): 3717. https://doi.org/10.1038/s41598-021-83166-4.
Dui, H., K. Liu, and S. Wu. 2023. “Cascading failures and resilience optimization of hospital infrastructure systems against the COVID-19.” Comput. Ind. Eng. 179 (Sep): 109158. https://doi.org/10.1016/j.cie.2023.109158.
Eisenberg, D. A., J. Park, and T. P. Seager. 2020. “Linking cascading failure models and organizational networks to manage large-scale blackouts in South Korea.” J. Manage. Eng. 36 (5): 04020067. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000820.
Ganesan, S., and D. Subramani. 2021. “Spatio-temporal predictive modeling framework for infectious disease spread.” Sci. Rep. 11 (1): 6741. https://doi.org/10.1038/s41598-021-86084-7.
Gao, S., and H. Wang. 2022. “Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes.” Future Gener. Comput. Syst. 127 (Jun): 334–346. https://doi.org/10.1016/j.future.2021.09.028.
Giancotti, M., M. Lopreite, M. Mauro, and M. Puliga. 2021. “The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic.” Sci. Rep. 11 (1): 23739. https://doi.org/10.1038/s41598-021-03120-2.
Haldane, V., et al. 2021. “Health systems resilience in managing the COVID-19 pandemic: Lessons from 28 countries.” Nat. Med. 27 (6): 964–980. https://doi.org/10.1038/s41591-021-01381-y.
Hassan, E. M., and H. N. Mahmoud. 2021. “Orchestrating performance of healthcare networks subjected to the compound events of natural disasters and pandemic.” Nat. Commun. 12 (1): 1338. https://doi.org/10.1038/s41467-021-21581-x.
Hosseini, S., K. Barker, and J. E. Ramirez-Marquez. 2016. “A review of definitions and measures of system resilience.” Reliab. Eng. Syst. Saf. 145 (Mar): 47–61. https://doi.org/10.1016/j.ress.2015.08.006.
Jia, Q., Y. Guo, G. Wang, and S. J. Barnes. 2020. “Big data analytics in the fight against major public health incidents (including COVID-19): A conceptual framework.” Int. J. Environ. Res. Public Health 17 (17): 6161. https://doi.org/10.3390/ijerph17176161.
Jiang, H., Z. Sun, H. Guo, Q. Xing, W. Du, and G. Cai. 2022. “Assessing OSM building completeness for almost 13,000 cities globally.” Int. J. Digital Earth 15 (1): 2400–2421. https://doi.org/10.1080/20964471.2021.1950351.
Jin, K., W. Wang, X. Li, S. Chen, S. Qin, and X. Hua. 2023. “Cascading failure in urban rail transit network considering demand variation and time delay.” Physica A 630 (Sep): 129290. https://doi.org/10.1016/j.physa.2023.129290.
Kang, D., H. Choi, J.-H. Kim, and J. Choi. 2020. “Spatial epidemic dynamics of the COVID-19 outbreak in China.” Int. J. Infect. Dis. 94 (Mar): 96–102. https://doi.org/10.1016/j.ijid.2020.03.076.
Kapucu, N., Y. Martín, and Z. Williamson. 2021. “Urban resilience for building a sustainable and safe environment.” Urban Governance 1 (1): 10–16. https://doi.org/10.1016/j.ugj.2021.09.001.
Laínez-Aguirre, J. M., G. E. Blau, and G. V. Reklaitis. 2014. “Postulating compartmental models using a flexible approach.” Comput. Aided Chem. Eng. 33 (Jan): 1171–1176. https://doi.org/10.1016/B978-0-444-63455-9.50030-1.
Lam, C. Y., and T. Shimizu. 2021. “A network analytical framework to analyze infrastructure damage based on earthquake cascades: A study of earthquake cases in Japan.” Int. J. Disaster Risk Reduct. 54 (Sep): 102025. https://doi.org/10.1016/j.ijdrr.2020.102025.
Li, L., Y. Ding, J. Yuan, W. Ji, J. Zhao, and L. Shen. 2022. “Quantifying the resilience of emergency response networks to infrastructure interruptions through an enhanced metanetwork-based framework.” J. Manage. Eng. 38 (5): 04022047. https://doi.org/10.1061/(asce)me.1943-5479.0001080.
Li, W.-J., Z. Chen, J. Wang, L.-L. Jiang, and M. Perc. 2023. “Social mobility and network reciprocity shape cooperation in collaborative networks.” Chaos Solitons Fractals 170 (Jun): 113378. https://doi.org/10.1016/j.chaos.2023.113378.
Li, Z., N. Li, G. P. Cimellaro, and D. Fang. 2020. “System dynamics modeling-based approach for assessing seismic resilience of hospitals: Methodology and a case in China.” J. Manage. Eng. 36 (5): 04020050. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000814.
Liu, W., et al. 2021. “Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China.” Sci. Rep. 11 (1): 13648. https://doi.org/10.1038/s41598-021-93020-2.
Ministry of Construction of the People’s Republic of China. 2005. “Urban planning-making method.” Accessed January 10, 2024. https://www.gov.cn/zhengce/2022-01/25/content_5711996.htm.
Ministry of Natural Resources. 2021. “TD/T 1064-2021, Code of practice for standard urban built-up area delineation.” Accessed January 15, 2024. http://gi.mnr.gov.cn/202106/t20210621_2658597.html.
Nath, A., K. L. Sudarshan, G. K. Rajput, S. Mathew, K. R. R. Chandrika, and P. Mathur. 2022. “A rapid assessment of the impact of coronavirus disease (COVID-19) pandemic on health care & service delivery for noncommunicable diseases in India.” Diabetes Metab. Syndr. Clin. Res. Rev. 16 (10): 102607. https://doi.org/10.1016/j.dsx.2022.102607.
Odagaki, T. 2023. “New compartment model for COVID-19.” Sci. Rep. 13 (1): 5409. https://doi.org/10.1038/s41598-023-32159-6.
Peng, Z., S. Ao, L. Liu, S. Bao, T. Hu, H. Wu, and R. Wang. 2021. “Estimating unreported COVID-19 cases with a time-varying SIR regression model.” Int. J. Environ. Res. Public Health 18 (3): 1090. https://doi.org/10.3390/ijerph18031090.
Pescaroli, G., and D. Alexander. 2016. “Critical infrastructure, panarchies and the vulnerability paths of cascading disasters.” Nat. Hazards 82 (1): 175–192. https://doi.org/10.1007/s11069-016-2186-3.
Pishnamazzadeh, M., M. M. Sepehri, and B. Ostadi. 2020. “An assessment model for hospital resilience according to the simultaneous consideration of key performance indicators: A system dynamics approach.” Perioperative Care Operat. Room Manage. 20 (Sep): 100118. https://doi.org/10.1016/j.pcorm.2020.100118.
Qiu, Z., Y. Sun, X. He, J. Wei, R. Zhou, J. Bai, and S. Du. 2022. “Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China.” Sci. Rep. 12 (1): 8910. https://doi.org/10.1038/s41598-022-12958-z.
Rajulapati, P. S., N. Nukavarapu, and S. Durbha. 2020. “Multi-agent deep reinforcement learning based interdependent critical infrastructure simulation model for situational awareness during a flood event.” In Proc., IGARSS 2020-2020 IEEE Int. Geoscience and Remote Sensing Symp., 6890–6893. New York: IEEE. https://doi.org/10.1109/IGARSS39084.2020.9323380.
Ratnapalan, S., and D. Lang. 2020. “Health care organizations as complex adaptive systems.” Health Care Manager. 39 (1): 18–23. https://doi.org/10.1097/HCM.0000000000000284.
Rus, K., V. Kilar, and D. Koren. 2018. “Resilience assessment of complex urban systems to natural disasters: A new literature review.” Int. J. Disaster Risk Reduct. 31 (Sep): 311–330. https://doi.org/10.1016/j.ijdrr.2018.05.015.
Salem, S., A. Siam, W. El-Dakhakhni, and M. Tait. 2020. “Probabilistic resilience-guided infrastructure risk management.” J. Manage. Eng. 36 (6): 04020073. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000818.
Scholz, S., B. Ngoli, and S. Flessa. 2015. “Rapid assessment of infrastructure of primary health care facilities—A relevant instrument for health care systems management.” BMC Health Serv. Res. 15 (1): 183. https://doi.org/10.1186/s12913-015-0838-8.
Shi, J., X. Gao, S. Xue, F. Li, Q. Nie, Y. Lv, J. Wang, T. Xu, G. Du, and G. Li. 2021. “Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China.” Sci. Rep. 11 (1): 7811. https://doi.org/10.1038/s41598-021-86188-0.
Shi, X., and Y. Cao. 2020. “Dynamics of a stochastic periodic SIRS model with time delay.” Int. J. Biomath. 13 (8): 2050072. https://doi.org/10.1142/S1793524520500722.
Sohrabizadeh, S., S. Yousefian, A. Bahramzadeh, and M. H. Vaziri. 2021. “A systematic review of health sector responses to the coincidence of disasters and COVID-19.” BMC Public Health 21 (1): 709. https://doi.org/10.1186/s12889-021-10806-9.
Sweya, L. N., S. Wilkinson, J. Mayunga, A. Joseph, G. Lugomela, and J. Victor. 2020. “Development of a tool to measure resilience against floods for water supply systems in Tanzania.” J. Manage. Eng. 36 (4): 05020007. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000783.
Tariverdi, M., H. Fotouhi, S. Moryadee, and E. Miller-Hooks. 2019. “Health care system disaster-resilience optimization given its reliance on interdependent critical lifelines.” J. Infrastruct. Syst. 25 (1): 04018044. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000465.
Tariverdi, M., M. Nunez-del-Prado, N. Leonova, and J. Rentschler. 2023. “Measuring accessibility to public services and infrastructure criticality for disasters risk management.” Sci. Rep. 13 (1): 1569. https://doi.org/10.1038/s41598-023-28460-z.
Tebes, J. K., et al. 2022. “The stress and resilience town hall: A systems response to support the health workforce during COVID-19 and beyond.” Gen. Hospital Psychiatry 77 (Sep): 80–87. https://doi.org/10.1016/j.genhosppsych.2022.04.009.
Tortorella, G. L., F. S. Fogliatto, T. A. Saurin, L. M. Tonetto, and D. McFarlane. 2022. “Contributions of Healthcare 4.0 digital applications to the resilience of healthcare organizations during the COVID-19 outbreak.” Technovation 111 (Sep): 102379. https://doi.org/10.1016/j.technovation.2021.102379.
Wang, C., Z. Wang, G. Wang, J. Y.-N. Lau, K. Zhang, and W. Li. 2021. “COVID-19 in early 2021: Current status and looking forward.” Signal Transduction Targeted Ther. 6 (1): 114. https://doi.org/10.1038/s41392-021-00527-1.
Wang, Y., J. E. Taylor, and M. J. Garvin. 2020a. “Measuring resilience of human–spatial systems to disasters: Framework combining spatial-network analysis and Fisher information.” J. Manage. Eng. 36 (4): 04020019. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000782.
Wang, Z., Z. Liu, and Z. Liu. 2020b. “COVID-19 analysis and forecast based on machine learning.” J. Biomed. Eng. Res. 39 (1): 1–5. https://doi.org/10.19529/j.cnki.1672-6278.2020.01.01.
Wu, J. T., K. Leung, M. Bushman, N. Kishore, R. Niehus, P. M. de Salazar, B. J. Cowling, M. Lipsitch, and G. M. Leung. 2020. “Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China.” Nat. Med. 26 (4): 506–510. https://doi.org/10.1038/s41591-020-0822-7.
Xiang, W., L. Chen, X. Yan, B. Wang, and X. Liu. 2023. “The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model.” Cities 135 (Sep): 104238. https://doi.org/10.1016/j.cities.2023.104238.
Yang, R., G. Du, Z. Duan, M. Du, X. Miao, and Y. Tang. 2020a. “Knowledge system analysis on emergency management of public health emergencies.” Sustainability 12 (11): 4410. https://doi.org/10.3390/su12114410.
Yang, Z., et al. 2020b. “Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.” J. Thoracic Dis. 12 (3): 165–174. https://doi.org/10.21037/jtd.2020.02.64.
Yin, K., J. Wu, W. Wang, D. H. Lee, and Y. Wei. 2023. “An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China.” Transp. Res. Part A: Policy Pract. 173 (Sep): 103687. https://doi.org/10.1016/j.tra.2023.103687.
Zelenkov, Y., and I. Reshettsov. 2023. “Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm.” Expert Syst. Appl. 224 (Jun): 120034. https://doi.org/10.1016/j.eswa.2023.120034.
Zhang, Z., W. Jigang, and X. Duan. 2010. “Practical algorithm for shortest path on transportation network.” In Proc., IEEE 2010 Int. Conf. on Computer and Information Application, 48–51. New York: IEEE. https://doi.org/10.1109/ICCIA.2010.6141534.
Zhu, D., X. Ye, and S. Manson. 2021. “Revealing the spatial shifting pattern of COVID-19 pandemic in the United States.” Sci. Rep. 11 (1): 8396. https://doi.org/10.1038/s41598-021-87902-8.
Information & Authors
Information
Published In
Journal of Management in Engineering
Volume 40 • Issue 6 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 24, 2024
Accepted: May 23, 2024
Published online: Aug 12, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 12, 2025
ASCE Technical Topics:
- Analysis (by type)
- Architectural engineering
- Buildings
- Business management
- Disaster preparedness
- Disaster risk management
- Emergency management
- Engineering fundamentals
- Epidemic and pandemic
- Facilities (by type)
- Failure analysis
- Health care facilities
- Infrastructure
- Infrastructure resilience
- Practice and Profession
- Public administration
- Public health and safety
- Social factors
- Structural engineering
- Structures (by type)
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.