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Zhang J, Long DZ, Li Y. A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands. TRANSPORTATION RESEARCH. PART E, LOGISTICS AND TRANSPORTATION REVIEW 2023; 172:103087. [PMID: 36909783 PMCID: PMC9986146 DOI: 10.1016/j.tre.2023.103087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, including the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A Demand prediction-Network optimization-Decision adjustment framework is proposed for the emergency logistic network design. We first present an improved short-term epidemic model to predict the evolutionary trajectory of the epidemic. Then, considering the uncertainty of the estimated demands, we construct a capacitated multi-period, multi-echelon facility deployment and resource allocation robust optimization model to improve the reliability of the decisions. To address the conservativeness of robust solutions during the evolution of the epidemic, an uncertainty budget adjustment strategy is proposed and integrated into the rolling horizon optimization approach. The results of the case study show that (i) the short-term prediction method has higher accuracy and the accuracy increases with the amount of observed data; (ii) considering the demand uncertainty, the proposed robust optimization model combined with uncertainty budget adjustment strategy can improve the performance of the emergency logistic network; (iii) the proposed solution method is more efficient than its benchmark, especially for large-scale cases. Moreover, some managerial insights related to the emergency logistics network design problem are presented.
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Affiliation(s)
- Jianghua Zhang
- School of Management, Shandong University, Jinan, Shandong, 250100, China
- Institute of Data & Decision Science, Shandong University, Jinan, Shandong, 250100, China
| | - Daniel Zhuoyu Long
- Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Yuchen Li
- School of Management, Shandong University, Jinan, Shandong, 250100, China
- Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
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Huberts NFD, Thijssen JJJ. Optimal timing of non-pharmaceutical interventions during an epidemic. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023; 305:1366-1389. [PMID: 35765314 PMCID: PMC9221090 DOI: 10.1016/j.ejor.2022.06.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/15/2022] [Indexed: 05/10/2023]
Abstract
In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.
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Affiliation(s)
- Nick F D Huberts
- Management School, University of York, Heslington, York YO10 5ZF, United Kingdom
| | - Jacco J J Thijssen
- Management School, University of York, Heslington, York YO10 5ZF, United Kingdom
- Department of Mathematics, University of York, Heslington, York YO10 5ZF, United Kingdom
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Retrospective Modeling of the Omicron Epidemic in Shanghai, China: Exploring the Timing and Performance of Control Measures. Trop Med Infect Dis 2023; 8:tropicalmed8010039. [PMID: 36668946 PMCID: PMC9862922 DOI: 10.3390/tropicalmed8010039] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND In late February 2022, the Omicron epidemic swept through Shanghai, and the Shanghai government responded to it by adhering to a dynamic zero-COVID strategy. In this study, we conducted a retrospective analysis of the Omicron epidemic in Shanghai to explore the timing and performance of control measures based on the eventual size and duration of the outbreak. METHODS We constructed an age-structured and vaccination-stratified SEPASHRD model by considering populations that had been detected or controlled before symptom onset. In addition, we retrospectively modeled the epidemic in Shanghai from 26 February 2022 to 31 May 2022 across four periods defined by events and interventions, on the basis of officially reported confirmed (58,084) and asymptomatic (591,346) cases. RESULTS According to our model fitting, there were about 785,123 positive infections, of which about 57,585 positive infections were symptomatic infections. Our counterfactual assessment found that precise control by grid management was not so effective and that citywide static management was still needed. Universal and enforced control by citywide static management contained 87.65% and 96.29% of transmission opportunities, respectively. The number of daily new and cumulative infections could be significantly reduced if we implemented static management in advance. Moreover, if static management was implemented in the first 14 days of the epidemic, the number of daily new infections would be less than 10. CONCLUSIONS The above research suggests that dynamic zeroing can only be achieved when strict prevention and control measures are implemented as early as possible. In addition, a lot of preparation is still needed if China wants to change its strategy in the future.
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Leung KK, Zhang R, Hashim MJ, Fang M, Xu J, Sun D, Li X, Liu Y, Deng H, Zeng D, Lin Z, He P, Zhang Y, Zhu X, Liang D, Xing A, Lee SS, Memish ZA, Jiang G, Khan G. Effectiveness of containment strategies in preventing SARS-CoV-2 transmission. J Infect Public Health 2022; 15:609-614. [PMID: 35537237 PMCID: PMC9052634 DOI: 10.1016/j.jiph.2022.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Despite substantial resources deployed to curb SARS-CoV-2 transmission, controlling the COVID-19 pandemic has been a major challenge. New variants of the virus are frequently emerging leading to new waves of infection and re-introduction of control measures. In this study, we assessed the effectiveness of containment strategies implemented in the early phase of the pandemic. METHODS Real-world data for COVID-19 cases was retrieved for the period Jan 1 to May 1, 2020 from a number of different sources, including PubMed, MEDLINE, Facebook, Epidemic Forecasting and Google Mobility Reports. We analyzed data for 18 countries/regions that deployed containment strategies such as travel restrictions, lockdowns, stay-at-home requests, school/public events closure, social distancing, and exposure history information management (digital contact tracing, DCT). Primary outcome measure was the change in the number of new cases over 30 days before and after deployment of a control measure. We also compared the effectiveness of centralized versus decentralized DCT. Time series data for COVID-19 were analyzed using Mann-Kendall (M-K) trend tests to investigate the impact of these measures on changes in the number of new cases. The rate of change in the number of new cases was compared using M-K z-values and Sen's slope. RESULTS In spite of the widespread implementation of conventional strategies such as lockdowns, travel restrictions, social distancing, school closures, and stay-at-home requests, analysis revealed that these measures could not prevent the spread of the virus. However, countries which adopted DCT with centralized data storage were more likely to contain the spread. CONCLUSIONS Centralized DCT was more effective in containing the spread of COVID-19. Early implementation of centralized DCT should be considered in future outbreaks. However, challenges such as public acceptance, data security and privacy concerns will need to be addressed.
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Affiliation(s)
- Ka Kit Leung
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China; Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Rusheng Zhang
- Changsha Center for Disease Control and Prevention, Changsha, 410005, Hunan, China
| | - Muhammad Jawad Hashim
- Department of Family Medicine, College of Medicine & Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mingying Fang
- Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Jing Xu
- Department of Pathology, School of Basic Medical Sciences, Shanghai University of Medicine and Health, 279 Zhou Zhu Gong Road, Shanghai 201318, China
| | - Derek Sun
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Xiang Li
- Sophia Technologies, Hunan 410001, China
| | - Yanhui Liu
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Haohui Deng
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Dingyuan Zeng
- Liuzhou Maternal and Child Health Care Hospital, Liuzhou, Guangxi 545001, China
| | - Zhong Lin
- Reproductive Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, China
| | - Peiqing He
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Yu Zhang
- Liuzhou Maternal and Child Health Care Hospital, Liuzhou, Guangxi 545001, China
| | - Xuehong Zhu
- Reproductive Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, China
| | - Dachao Liang
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Abao Xing
- Dongguan Institute of Reproductive and Genetic Research, Guangdong 523120, China; Affiliated Dongguan Maternal & Child Healthcare Hospital, Southern Medical University, Guangdong 523120, China
| | - Shui-Shan Lee
- Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ziad A Memish
- Research & Innovation Centre, King Saud Medical City, Ministry of Health & College of Medicine, AlFaisal University, Riyadh, Saudi Arabia
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Gulfaraz Khan
- Department of Microbiology and Immunology, College of Medicine & Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
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