Emergency medical service planning considering dynamic and stochastic demands of infected and non-infected patients during epidemics

References

• Ahmadi, M., Seifi, A., & Tootooni, B. (2015). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, 75, 145–163. https://doi.org/10.1016/j.tre.2015.01.008
   Web of Science ®Google Scholar
• Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, 255(1), 187–206. https://doi.org/10.1016/j.ejor.2016.04.041
   Web of Science ®Google Scholar
• Alistar, S. S., Long, E. F., Brandeau, M. L., & Beck, E. J. (2014). Hiv epidemic control—a model for optimal allocation of prevention and treatment resources. Health Care Management Science, 17(2), 162–181. https://doi.org/10.1007/s10729-013-9240-4
   PubMed Web of Science ®Google Scholar
• Anparasan, A., & Lejeune, M. (2019). Resource deployment and donation allocation for epidemic outbreaks. Annals of Operations Research, 283(1–2), 9–32. https://doi.org/10.1007/s10479-016-2392-0
   Web of Science ®Google Scholar
• Bayram, V., Tansel, B. Ç., & Yaman, H. (2015). Compromising system and user interests in shelter location and evacuation planning. Transportation Research Part B: Methodological, 72, 146–163. https://doi.org/10.1016/j.trb.2014.11.010
   Web of Science ®Google Scholar
• Bish, D. R., Tarhini, H., Amara, R., Zoraster, R., Bosson, N., & Gausche-Hill, M. (2017). Modeling to optimize hospital evacuation planning in ems systems. Prehospital Emergency Care, 21(4), 503–510. https://doi.org/10.1080/10903127.2017.1302531
   PubMed Web of Science ®Google Scholar
• Büyüktahtakin, İ. E., Des Bordes, E., & Kibiş, E. Y. (2018). A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa. European Journal of Operational Research, 265(3), 1046–1063.
   Web of Science ®Google Scholar
• Caress, B. (2020). Covid-19 and hospital inequality: Why it’s getting worse, and how to fix it. http://www.centernyc.org/urban-matters-2/2020/4/28/covid-19-and-hospital-inequality-why-its-getting-worse-and-how-to-fix-it
   Google Scholar
• China Daily. (2020). Report: China’s fight against covid-19 (full text). https://covid-19.chinadaily.com.cn/a/202004/21/WS5e9e2c62a3105d50a3d17880.html
   Google Scholar
• Chotirmall, S. H., Wang, L.-F., & Abisheganaden, J. A. (2020). Letter from Singapore: The clinical and research response to covid-19. Respirology (Carlton, Vic.), 25(10), 1101–1102. https://doi.org/10.1111/resp.13929
   PubMed Web of Science ®Google Scholar
• Dasaklis, T. K., Rachaniotis, N., & Pappis, C. (2017). Emergency supply chain management for controlling a smallpox outbreak: The case for regional mass vaccination. International Journal of Systems Science: Operations & Logistics, 4(1), 27–40. https://doi.org/10.1080/23302674.2015.1126379
   Google Scholar
• Esposito Amideo, A., Scaparra, M., & Kotiadis, K. (2019). Optimising shelter location and evacuation routing operations: The critical issues. European Journal of Operational Research, 279(2), 279–295. https://doi.org/10.1016/j.ejor.2018.12.009
   Web of Science ®Google Scholar
• Ghasemi, P., Khalili-Damghani, K., Hafezalkotob, A., & Raissi, S. (2020). Stochastic optimization model for distribution and evacuation planning (a case study of Tehran earthquake). Socio-Economic Planning Sciences, 71, 100745. https://doi.org/10.1016/j.seps.2019.100745
   Web of Science ®Google Scholar
• Hsu, Y.-T., & Peeta, S. (2014). Risk-based spatial zone determination problem for stage-based evacuation operations. Transportation Research Part C: Emerging Technologies, 41, 73–89. https://doi.org/10.1016/j.trc.2014.01.013
   Web of Science ®Google Scholar
• Huang, Q. (2020). Down the road! the number of patients hospitalized in Wuhan dropped to double digits (in Chinese). http://hb.sina.com.cn/news/b/2020-04-23/detail-iirczymi7840211.shtml
   Google Scholar
• Liu, M., Xu, X., Cao, J., & Zhang, D. (2020). Integrated planning for public health emergencies: A modified model for controlling H1N1 pandemic. Journal of the Operational Research Society, 71(5), 748–761. https://doi.org/10.1080/01605682.2019.1582589
   Web of Science ®Google Scholar
• Liu, M., & Zhang, D. (2016). A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating. Journal of the Operational Research Society, 67(6), 841–852. https://doi.org/10.1057/jors.2015.105
   Web of Science ®Google Scholar
• Luo, L., Wan, X., & Wang, Q. (2022). A multi-period location-allocation model for integrated management of emergency medical supplies and infected patients during epidemics. Computers & Industrial Engineering, 173, 108640. https://doi.org/10.1016/j.cie.2022.108640
   Web of Science ®Google Scholar
• Mazzaferro, V., Danelli, P., Torzilli, G., Droz Dit Busset, M., Virdis, M., & Sposito, C. (2020). A combined approach to priorities of surgical oncology during the covid-19 epidemic. Annals of Surgery, 272(2), e84–e86. https://doi.org/10.1097/SLA.0000000000004005
   PubMed Web of Science ®Google Scholar
• Mitsuya, H. (2020). Fight against covid-19 but avoid disruption of services for other communicable diseases (cds) and noncommunicable diseases (ncds). Global Health & Medicine, 2(6), 343–345. https://doi.org/10.35772/ghm.2020.01111
   PubMedGoogle Scholar
• Noyan, N., & Kahvecioğlu, G. (2018). Stochastic last mile relief network design with resource reallocation. Or Spectrum, 40(1), 187–231. https://doi.org/10.1007/s00291-017-0498-7
   Web of Science ®Google Scholar
• Pillac, V., Van Hentenryck, P., & Even, C. (2016). A conflict-based path-generation heuristic for evacuation planning. Transportation Research Part B: Methodological, 83, 136–150. https://doi.org/10.1016/j.trb.2015.09.008
   Web of Science ®Google Scholar
• Rabbani, M., Zhalechian, M., & Farshbaf-Geranmayeh, A. (2018). A robust possibilistic programming approach to multiperiod hospital evacuation planning problem under uncertainty. International Transactions in Operational Research, 25(1), 157–189. https://doi.org/10.1111/itor.12331
   Web of Science ®Google Scholar
• Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological, 44(4), 521–534. https://doi.org/10.1016/j.trb.2009.08.003
   Web of Science ®Google Scholar
• Sanci, E., & Daskin, M. S. (2019). Integrating location and network restoration decisions in relief networks under uncertainty. European Journal of Operational Research, 279(2), 335–350. https://doi.org/10.1016/j.ejor.2019.06.012
   Web of Science ®Google Scholar
• Santini, A. (2021). Optimising the assignment of swabs and reagent for PCR testing during a viral epidemic. Omega, 102, 102341. https://doi.org/10.1016/j.omega.2020.102341
   PubMed Web of Science ®Google Scholar
• Sun, L., DePuy, G. W., & Evans, G. W. (2014). Multi-objective optimization models for patient allocation during a pandemic influenza outbreak. Computers & Operations Research, 51, 350–359. https://doi.org/10.1016/j.cor.2013.12.001
   Web of Science ®Google Scholar
• United Nations. (2020). Covid-19 to slash global economic output by 8.5 trillion dollars over next two years. https://www.un.org/development/desa/en/news/policy/wesp-mid-2020-report.html
   Google Scholar
• Wang, L. (2020). A two-stage stochastic programming framework for evacuation planning in disaster responses. Computers & Industrial Engineering, 145, 106458. https://doi.org/10.1016/j.cie.2020.106458
   Web of Science ®Google Scholar
• Wang, Q., Liu, Z., Jiang, P., & Luo, L. (2022). A stochastic programming model for emergency supplies pre-positioning, transshipment and procurement in a regional healthcare coalition. Socio-Economic Planning Sciences, 82, 101279. https://doi.org/10.1016/j.seps.2022.101279
   PubMed Web of Science ®Google Scholar
• Wang, Q., & Nie, X. (2019). A stochastic programming model for emergency supply planning considering traffic congestion. IISE Transactions, 51(8), 910–920. https://doi.org/10.1080/24725854.2019.1589657
   Web of Science ®Google Scholar
• Wang, Q., & Nie, X. (2022). A stochastic programming model for emergency supply planning considering transportation network mitigation and traffic congestion. Socio-Economic Planning Sciences, 79, 101119. https://doi.org/10.1016/j.seps.2021.101119
   Web of Science ®Google Scholar
• Wang, Q., & Wallace, S. W. (2022). Non-compliance in transit-based evacuation pick-up point assignments. Socio-Economic Planning Sciences, 82, 101259. https://doi.org/10.1016/j.seps.2022.101259
   Web of Science ®Google Scholar
• Wang, Y., & Wang, J. (2019). Integrated reconfiguration of both supply and demand for evacuation planning. Transportation Research Part E: Logistics and Transportation Review, 130, 82–94. https://doi.org/10.1016/j.tre.2019.08.016
   Web of Science ®Google Scholar
• Wikipedia. (2020a). Fangcang hospital - wikipedia. https://en.wikipedia.org/wiki/Fangcang-hospital
   Google Scholar
• Wikipedia. (2020b). Huoshenshan hospital - wikipedia. https://en.wikipedia.org/wiki/Huoshenshan-Hospital
   Google Scholar
• World Health Organization. (2022). Who coronavirus disease (Covid-19) dashboard. https://covid19.who.int/
   Google Scholar
• Yin, X., & Büyüktahtakin, İ. E. (2021). A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations. Health Care Management Science, 24(3), 597–622. https://doi.org/10.1007/s10729-021-09559-z
   PubMed Web of Science ®Google Scholar