COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk
•Multi-stage stochastic epidemics-ventilator-logistics compartmental model.•Optimize ventilator allocation under asymptomatic uncertainty and risk.•Epidemiological, population, migration, and cost data-driven model.•New region-based sub-problem and bounds improving optimality gap.•A general model th...
Gespeichert in:
| Veröffentlicht in: | European journal of operational research Jg. 304; H. 1; S. 255 - 275 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Netherlands
Elsevier B.V
01.01.2023
|
| Schlagworte: | |
| ISSN: | 0377-2217, 1872-6860, 0377-2217 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •Multi-stage stochastic epidemics-ventilator-logistics compartmental model.•Optimize ventilator allocation under asymptomatic uncertainty and risk.•Epidemiological, population, migration, and cost data-driven model.•New region-based sub-problem and bounds improving optimality gap.•A general model that could be extended to other infectious diseases.
This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics. |
|---|---|
| AbstractList | This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics. This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics. •Multi-stage stochastic epidemics-ventilator-logistics compartmental model.•Optimize ventilator allocation under asymptomatic uncertainty and risk.•Epidemiological, population, migration, and cost data-driven model.•New region-based sub-problem and bounds improving optimality gap.•A general model that could be extended to other infectious diseases. This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics. |
| Author | Patel, Bhumi P. Yin, Xuecheng Büyüktahtakın, İ. Esra |
| Author_xml | – sequence: 1 givenname: Xuecheng surname: Yin fullname: Yin, Xuecheng organization: Yale School of Public Health, New Haven, CT, United States – sequence: 2 givenname: İ. Esra surname: Büyüktahtakın fullname: Büyüktahtakın, İ. Esra email: esratoy@njit.edu organization: Systems Optimization and Data Analytics Lab (SODAL), Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ, United States – sequence: 3 givenname: Bhumi P. surname: Patel fullname: Patel, Bhumi P. organization: Systems Optimization and Data Analytics Lab (SODAL), Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34866765$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Uctu1DAUtVARnRZ-gAXKspsEP-LHVKgSmuFRqVI3gNhZjnMDnnriYDsjzd_j6bQIWHTjK_mex9U5Z-hkDCMg9JrghmAi3m4a2ITYUExJQ0iDOX2GFkRJWgsl8AlaYCZlTSmRp-gspQ3GmHDCX6BT1iohpOAL9H11--16XZPlZbU22dTr6HYwVmHKbmt8ZbwP1mQXytdQlU123uQQqzRPk99X89hDLK-FmI0b874yY19Fl-5eoueD8QlePcxz9PXjhy-rz_XN7afr1fub2rZK5VoqLKCVVCoOhnUct93AO-DWikEOS7q0QEzbMdkTY5gSXPVy2cqOMq5oay07R1dH3WnuttDbcmI0Xk-x3B_3Ohin_92M7qf-EXZaCUZbLIrAxYNADL9mSFlvXbLgvRkhzElTgSXDQt1D3_zt9cfkMc4CUEeAjSGlCIO2Lt_HV6yd1wTrQ3N6ow_N6UNzmhBdmitU-h_1Uf1J0rsjCUrCOwdRJ-ugtNG7CDbrPrin6L8Bc22y4w |
| CitedBy_id | crossref_primary_10_1016_j_ejor_2023_09_034 crossref_primary_10_1080_0305215X_2023_2234294 crossref_primary_10_1016_j_ijpe_2024_109200 crossref_primary_10_1002_oca_3032 crossref_primary_10_1016_j_cie_2023_109107 crossref_primary_10_1016_j_ejor_2023_10_045 crossref_primary_10_1007_s10479_022_04926_7 crossref_primary_10_1108_K_12_2023_2738 crossref_primary_10_3389_fpubh_2023_1029385 crossref_primary_10_1080_01605682_2024_2436622 crossref_primary_10_1007_s10729_025_09703_z crossref_primary_10_1080_00207543_2025_2455448 crossref_primary_10_1007_s12652_022_03863_4 crossref_primary_10_1016_j_cie_2025_111094 crossref_primary_10_1016_j_cor_2024_106578 crossref_primary_10_1287_mnsc_2023_02958 crossref_primary_10_1007_s12046_024_02541_9 crossref_primary_10_1007_s12046_023_02332_8 crossref_primary_10_1016_j_omega_2025_103405 crossref_primary_10_1016_j_tre_2024_103828 crossref_primary_10_1016_j_jclepro_2023_139669 crossref_primary_10_1016_j_jclepro_2023_135985 crossref_primary_10_1080_00207543_2022_2162619 crossref_primary_10_1016_j_ejor_2023_03_032 crossref_primary_10_1016_j_ejor_2023_09_005 crossref_primary_10_1016_j_ejor_2022_07_019 crossref_primary_10_1111_itor_13473 crossref_primary_10_1111_risa_14339 crossref_primary_10_1007_s10479_025_06721_6 crossref_primary_10_1002_net_22149 crossref_primary_10_1007_s10898_024_01364_6 crossref_primary_10_1016_j_cor_2024_106568 crossref_primary_10_1016_j_cor_2023_106149 crossref_primary_10_1007_s10479_024_06100_7 crossref_primary_10_3390_su152215911 crossref_primary_10_1007_s13132_023_01537_w crossref_primary_10_1007_s12351_025_00928_y crossref_primary_10_1016_j_omega_2025_103318 crossref_primary_10_1016_j_cie_2025_111157 crossref_primary_10_1080_24725854_2023_2223246 crossref_primary_10_1016_j_eswa_2025_127143 crossref_primary_10_1016_j_compchemeng_2023_108527 crossref_primary_10_1016_j_eswa_2025_129545 crossref_primary_10_1016_j_seps_2025_102269 crossref_primary_10_1016_j_cor_2025_107275 crossref_primary_10_1108_IJOPM_09_2022_0581 crossref_primary_10_1080_23302674_2025_2457459 |
| Cites_doi | 10.1016/j.ejor.2017.08.037 10.1016/j.ijheh.2020.113610 10.1287/ijoc.2018.0885 10.1016/j.compchemeng.2020.106945 10.1098/rsos.201131 10.1016/S0025-5564(01)00050-5 10.1016/j.ejor.2016.09.049 10.1007/s10479-022-04926-7 10.1007/s00134-016-4325-4 10.3201/eid2306.161417 10.1016/j.ejor.2017.10.038 10.1016/j.mjafi.2020.03.022 10.1016/j.mbs.2008.07.006 10.1016/j.ejor.2015.05.048 10.1056/NEJMp2006141 10.1371/journal.pone.0241468 10.1097/ALN.0000000000003296 10.1016/j.eswa.2020.114077 10.3390/v12070777 10.1007/s10107-018-1249-5 10.1016/j.cie.2018.01.018 10.1016/j.ijpe.2012.05.023 10.1016/S0025-5564(03)00090-7 10.1155/2020/3452402 10.1016/S2468-2667(20)30157-2 10.1001/jama.2020.5046 10.1371/journal.pone.0241027 10.7326/M20-1260 10.1016/S1473-3099(20)30553-3 10.1137/16M1075594 10.2196/19368 10.1002/nav.21905 10.1016/S1473-3099(20)30144-4 10.1101/2020.06.26.20141127 10.1016/S0378-4266(02)00271-6 10.1016/j.mbs.2018.10.010 10.1016/j.chaos.2020.110107 10.1016/j.tre.2020.101967 10.1016/j.ejor.2017.12.022 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. – notice: 2021 Elsevier B.V. All rights reserved. – notice: 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. |
| DBID | AAYXX CITATION NPM 7X8 5PM |
| DOI | 10.1016/j.ejor.2021.11.052 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Business |
| EISSN | 1872-6860 0377-2217 |
| EndPage | 275 |
| ExternalDocumentID | PMC8632406 34866765 10_1016_j_ejor_2021_11_052 S0377221721010031 |
| Genre | Journal Article |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 6OB 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABAOU ABBOA ABFNM ABFRF ABJNI ABMAC ABUCO ABYKQ ACAZW ACDAQ ACGFO ACGFS ACIWK ACNCT ACRLP ACZNC ADBBV ADEZE ADGUI AEBSH AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR AXJTR BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W KOM LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ RXW SCC SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSV SSW SSZ T5K TAE TN5 U5U XPP ZMT ~02 ~G- 1OL 29G 41~ 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADIYS ADJOM ADMUD ADNMO ADXHL AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HVGLF HZ~ R2- SEW VH1 WUQ ~HD BNPGV NPM SSH 7X8 5PM |
| ID | FETCH-LOGICAL-c488t-7806e472785ea3b504bf5be5cc6f7f929ce1a4b37d1aa38658d7947b235824cc3 |
| ISICitedReferencesCount | 51 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000861383000005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0377-2217 |
| IngestDate | Tue Sep 30 16:33:16 EDT 2025 Sun Sep 28 02:34:59 EDT 2025 Thu Apr 03 07:08:38 EDT 2025 Sat Nov 29 07:21:13 EST 2025 Tue Nov 18 21:49:25 EST 2025 Fri Feb 23 02:38:24 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Pandemic resource and ventilator allocation Mean-CVaR multi-stage stochastic mixed-integer programming model COVID-19 Risk-averse optimization OR in health services |
| Language | English |
| License | 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c488t-7806e472785ea3b504bf5be5cc6f7f929ce1a4b37d1aa38658d7947b235824cc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC8632406 |
| PMID | 34866765 |
| PQID | 2607306806 |
| PQPubID | 23479 |
| PageCount | 21 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8632406 proquest_miscellaneous_2607306806 pubmed_primary_34866765 crossref_citationtrail_10_1016_j_ejor_2021_11_052 crossref_primary_10_1016_j_ejor_2021_11_052 elsevier_sciencedirect_doi_10_1016_j_ejor_2021_11_052 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-01-01 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | European journal of operational research |
| PublicationTitleAlternate | Eur J Oper Res |
| PublicationYear | 2023 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Ambikapathy, Krishnamurthy (bib0002) 2020; 6 Manca, Caldiroli, Storti (bib0046) 2020 Harvard Medical School (2021). Symptoms, spread and other essential information about Coronavirus and COVID-19. Kucharski, Russell, Diamond, Liu, Edmunds, Funk, Munday (bib0039) 2020 Meng, Qiu, Wan, Ai, Xue, Guo, Tong (bib0051) 2020; 132 Defourny, Ernst, Wehenkel (bib0022) 2012 Dasaklis, Pappis, Rachaniotis (bib0021) 2012; 139 Accessed November 30, 2020. Accessed May 30, 2021. Bakir, Boland, Dandurand, Erera (bib0004) 2020; 32 Queiroz, Ivanov, Dolgui, Wamba (bib0054) 2020 Loeffler-Wirth, Schmidt, Binder (bib0044) 2020; 12 Bushaj, Büyüktahtakın, Haight (bib0011) 2021 Büyüktahtakın (bib0013) 2021 Kretzschmar, Rozhnova, Bootsma, van Boven, van de Wijgert, Bonten (bib0037) 2020; 5 Alonso-Ayuso, Escudero, Guignard, Weintraub (bib0001) 2018; 267 Meller, M. (2020). The asymptomatic and pre-symptomatic spread of COVID-19. Yin, Büyüktahtakın (bib0066) 2021 Zou, Ahmed, Sun (bib0071) 2019; 175 Tanner, Sattenspiel, Ntaimo (bib0059) 2008; 215 Govindan, Mina, Alavi (bib0026) 2020; 138 Kaplan, Craft, Wein (bib0032) 2003; 185 Zaric, Brandeau (bib0068) 2001; 171 . Burki, T. (2020). China’s successful control of COVID-19. CNN (2021). New Zealand and Australia were Covid success stories. why are they behind on vaccine rollouts? Kıbış, Büyüktahtakın (bib0035) 2019; 307 Chatterjee, Chatterjee, Kumar, Shankar (bib0018) 2020 JHU (2020). COVID-19 United States cases by county. Ku, Ng, Lin (bib0038) 2020 Yin, Büyüktahtakın (bib0067) 2021; 0 Wang, Pasco, Du, Petty, Fox, Galvani, Meyers (bib0061) 2020 CDC (2021). Clinical questions about COVID-19: Questions and answers. Homem-de Mello, Pagnoncelli (bib0050) 2016; 249 Zeb, Alzahrani, Erturk, Zaman (bib0069) 2020; 2020 Coşgun, Büyüktahtakın (bib0020) 2018; 118 Lee, Zhao, Sun, Pan, Zhou, Xiong, Zhang (bib0041) 2020; 15 Kıbış, Büyüktahtakın, Haight, Akhundov, Knight, Flower (bib0036) 2021; 33 Huang, Araz, Morton, Johnson, Damien, Clements, Meyers (bib0030) 2017; 23 Sandıkçı, Özaltın (bib0058) 2017; 27 Fischer, Avrashi, Oz, Fadul, Gutman, Rubenstein, Glöckner (bib0024) 2020; 7 Li, Bouardi, Lami, Trikalinos, Trichakis, Bertsimas (bib0042) 2020 Bushaj, S., Yin, X., Beqiri, A., Andrews, D., & Büyüktahtakın, İ. E. (2021b). A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization. Under Review. Ranney, Griffeth, Jha (bib0055) 2020; 382 Lilleker, D. (2021). The good, the bad and the ugly of government responses to COVID. Accessed February 13, 2021. Environmental Systems Research Institute (ESRI) (2021). ArcGIS map. Accessed February 12, 2021. Katris (bib0033) 2020; 166 Hogan, A. (2020). Watch: Ventilators are in high demand for COVID-19 patients. How do they work? Mahmutoğulları, Çavuş, Aktürk (bib0045) 2018; 266 Bertsimas, D., Boussioux, L., Wright, R. C., Delarue, A., Digalakis Jr, V., Jacquillat, A., Kitane, D. L., Lukin, G., Li, M. L., Mingardi, L. et al. (2020). From predictions to prescriptions: A data-driven response to COVID-19. Accessed January 20, 2021. Badr, Du, Marshall, Dong, Squire, Gardner (bib0003) 2020; 20 Rockafellar, Uryasev (bib0057) 2002; 26 Roberts, Seymour, Dimitrov (bib0056) 2020 Accessed August 2, 2021. Murray, C. (2020). Forecasting COVID-19 impact on hospital bed-days, ICU-Days, ventilator-days and deaths by US state in the next 4 months, 10.1101/2020.03.27.20043752. Hu, Lin, Wang, Xu, Tatem, Meng, Wu (bib0029) 2020 Büyüktahtakın, des Bordes, Kıbış (bib0014) 2018; 265 Bein, Grasso, Moerer, Quintel, Guerin, Deja, Mehta (bib0005) 2016; 42 Weissman, Crane-Droesch, Chivers, Luong, Hanish, Levy, Anesi (bib0063) 2020; 173 Billingham, Widrick, Edwards, Klaus (bib0008) 2020 Glass, H. (2020). High-acuity ventilator cost guide. Tuan, Mohammadi, Rezapour (bib0060) 2020; 140 McCrimmon, K. K. (2021). The truth about COVID-19 and asymptomatic spread: It’s common, so wear a mask and avoid large gatherings. Wei, Liu, Zhu, Qian, Ye, Li, Wang (bib0062) 2020; 230 Yin, X., Bushaj, S., Yuan, Y., & Büyüktahtakın, İ. E. (2021). COVID-19: An agent-based simulation-optimization approach to vaccine center location vaccine allocation problem. Under Review. Blanco, V., Gázquez, R., & Leal, M. (2020). Reallocating and sharing health equipments in sanitary emergency situations: The COVID-19 case in Spain. Zhang, Jia, Lei, Wang, Zhao, Guo, Xie (bib0070) 2020 Kıbış, Büyüktahtakın (bib0034) 2017; 259 Castro, de Carvalho, Chin, Kahn, Franca, Macario, de Oliveira (bib0015) 2020 White, Lo (bib0064) 2020; 323 Bernstein, L. (2020). More COVID-19 patients are surviving ventilators in the ICU. CENSUS (2020). Datasets. Parker, F., Sawczuk, H., Ganjkhanloo, F., Ahmadi, F., & Ghobadi, K. (2020). Optimal resource and demand redistribution for healthcare systems under stress from COVID-19. Lacasa, Challen, Brooks-Pollock, Danon (bib0040) 2020; 15 Mehrotra, Rahimian, Barah, Luo, Schantz (bib0048) 2020 Büyüktahtakın (10.1016/j.ejor.2021.11.052_bib0014) 2018; 265 10.1016/j.ejor.2021.11.052_bib0009 10.1016/j.ejor.2021.11.052_bib0006 10.1016/j.ejor.2021.11.052_bib0007 Li (10.1016/j.ejor.2021.11.052_bib0042) 2020 Katris (10.1016/j.ejor.2021.11.052_bib0033) 2020; 166 10.1016/j.ejor.2021.11.052_bib0049 10.1016/j.ejor.2021.11.052_bib0047 Billingham (10.1016/j.ejor.2021.11.052_bib0008) 2020 Bein (10.1016/j.ejor.2021.11.052_bib0005) 2016; 42 10.1016/j.ejor.2021.11.052_bib0043 Yin (10.1016/j.ejor.2021.11.052_bib0067) 2021; 0 Huang (10.1016/j.ejor.2021.11.052_bib0030) 2017; 23 Kıbış (10.1016/j.ejor.2021.11.052_bib0036) 2021; 33 Zhang (10.1016/j.ejor.2021.11.052_bib0070) 2020 Bushaj (10.1016/j.ejor.2021.11.052_bib0011) 2021 Weissman (10.1016/j.ejor.2021.11.052_bib0063) 2020; 173 Homem-de Mello (10.1016/j.ejor.2021.11.052_bib0050) 2016; 249 10.1016/j.ejor.2021.11.052_bib0019 10.1016/j.ejor.2021.11.052_bib0017 Castro (10.1016/j.ejor.2021.11.052_bib0015) 2020 Mehrotra (10.1016/j.ejor.2021.11.052_bib0048) 2020 10.1016/j.ejor.2021.11.052_bib0016 Coşgun (10.1016/j.ejor.2021.11.052_bib0020) 2018; 118 Alonso-Ayuso (10.1016/j.ejor.2021.11.052_bib0001) 2018; 267 Kıbış (10.1016/j.ejor.2021.11.052_bib0035) 2019; 307 10.1016/j.ejor.2021.11.052_bib0012 10.1016/j.ejor.2021.11.052_bib0053 10.1016/j.ejor.2021.11.052_bib0010 10.1016/j.ejor.2021.11.052_bib0052 Zaric (10.1016/j.ejor.2021.11.052_bib0068) 2001; 171 Loeffler-Wirth (10.1016/j.ejor.2021.11.052_bib0044) 2020; 12 Rockafellar (10.1016/j.ejor.2021.11.052_bib0057) 2002; 26 Tuan (10.1016/j.ejor.2021.11.052_bib0060) 2020; 140 Roberts (10.1016/j.ejor.2021.11.052_bib0056) 2020 Zeb (10.1016/j.ejor.2021.11.052_bib0069) 2020; 2020 Wang (10.1016/j.ejor.2021.11.052_bib0061) 2020 Tanner (10.1016/j.ejor.2021.11.052_bib0059) 2008; 215 Ambikapathy (10.1016/j.ejor.2021.11.052_bib0002) 2020; 6 10.1016/j.ejor.2021.11.052_bib0028 Kıbış (10.1016/j.ejor.2021.11.052_bib0034) 2017; 259 10.1016/j.ejor.2021.11.052_bib0027 Bakir (10.1016/j.ejor.2021.11.052_bib0004) 2020; 32 10.1016/j.ejor.2021.11.052_bib0025 10.1016/j.ejor.2021.11.052_bib0023 Mahmutoğulları (10.1016/j.ejor.2021.11.052_bib0045) 2018; 266 10.1016/j.ejor.2021.11.052_bib0065 Wei (10.1016/j.ejor.2021.11.052_bib0062) 2020; 230 Kretzschmar (10.1016/j.ejor.2021.11.052_bib0037) 2020; 5 White (10.1016/j.ejor.2021.11.052_bib0064) 2020; 323 Zou (10.1016/j.ejor.2021.11.052_bib0071) 2019; 175 Manca (10.1016/j.ejor.2021.11.052_bib0046) 2020 Dasaklis (10.1016/j.ejor.2021.11.052_bib0021) 2012; 139 Büyüktahtakın (10.1016/j.ejor.2021.11.052_bib0013) 2021 Badr (10.1016/j.ejor.2021.11.052_bib0003) 2020; 20 Sandıkçı (10.1016/j.ejor.2021.11.052_bib0058) 2017; 27 Kaplan (10.1016/j.ejor.2021.11.052_bib0032) 2003; 185 Yin (10.1016/j.ejor.2021.11.052_bib0066) 2021 10.1016/j.ejor.2021.11.052_bib0031 Kucharski (10.1016/j.ejor.2021.11.052_bib0039) 2020 Defourny (10.1016/j.ejor.2021.11.052_bib0022) 2012 Govindan (10.1016/j.ejor.2021.11.052_bib0026) 2020; 138 Lee (10.1016/j.ejor.2021.11.052_bib0041) 2020; 15 Ku (10.1016/j.ejor.2021.11.052_bib0038) 2020 Queiroz (10.1016/j.ejor.2021.11.052_bib0054) 2020 Lacasa (10.1016/j.ejor.2021.11.052_bib0040) 2020; 15 Hu (10.1016/j.ejor.2021.11.052_bib0029) 2020 Fischer (10.1016/j.ejor.2021.11.052_bib0024) 2020; 7 Ranney (10.1016/j.ejor.2021.11.052_bib0055) 2020; 382 Chatterjee (10.1016/j.ejor.2021.11.052_bib0018) 2020 Meng (10.1016/j.ejor.2021.11.052_bib0051) 2020; 132 |
| References_xml | – volume: 20 start-page: 1247 year: 2020 end-page: 1254 ident: bib0003 article-title: Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study publication-title: The Lancet Infectious Diseases – reference: . Accessed February 13, 2021. – reference: Murray, C. (2020). Forecasting COVID-19 impact on hospital bed-days, ICU-Days, ventilator-days and deaths by US state in the next 4 months, 10.1101/2020.03.27.20043752. – reference: McCrimmon, K. K. (2021). The truth about COVID-19 and asymptomatic spread: It’s common, so wear a mask and avoid large gatherings. – start-page: 1 year: 2020 end-page: 38 ident: bib0054 article-title: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review publication-title: Annals of Operations Research – year: 2020 ident: bib0056 article-title: Selectively caring for the most severe COVID-19 patients delays ICU bed shortages more than increasing hospital capacity publication-title: MedRxiv – start-page: 1 year: 2021 end-page: 36 ident: bib0013 article-title: Stage- publication-title: Accepted for publication in Annals of Operations Research – volume: 27 start-page: 1772 year: 2017 end-page: 1800 ident: bib0058 article-title: A scalable bounding method for multistage stochastic programs publication-title: SIAM Journal on Optimization – reference: Accessed May 30, 2021. – volume: 42 start-page: 699 year: 2016 end-page: 711 ident: bib0005 article-title: The standard of care of patients with ARDS: Ventilatory settings and rescue therapies for refractory hypoxemia publication-title: Intensive Care Medicine – reference: CENSUS (2020). Datasets. – volume: 7 start-page: 201131 year: 2020 ident: bib0024 article-title: The behavioural challenge of the COVID-19 pandemic: Indirect measurements and personalized attitude changing treatments (impact) publication-title: Royal Society Open Science – volume: 23 start-page: 914 year: 2017 ident: bib0030 article-title: Stockpiling ventilators for influenza pandemics publication-title: Emerging Infectious Diseases – year: 2020 ident: bib0038 article-title: Epidemiological benchmarks of the COVID-19 outbreak control in China after Wuhan’s lockdown: A modelling study with an empirical approach publication-title: Available at SSRN 3544127 – reference: Bernstein, L. (2020). More COVID-19 patients are surviving ventilators in the ICU. – volume: 266 start-page: 595 year: 2018 end-page: 608 ident: bib0045 article-title: Bounds on risk-averse mixed-integer multi-stage stochastic programming problems with mean-CVaR publication-title: European Journal of Operational Research – volume: 15 start-page: e0241468 year: 2020 ident: bib0041 article-title: Human mobility trends during the early stage of the COVID-19 pandemic in the United States publication-title: PLoS One – reference: Harvard Medical School (2021). Symptoms, spread and other essential information about Coronavirus and COVID-19. – year: 2020 ident: bib0042 article-title: Forecasting COVID-19 and analyzing the effect of government interventions publication-title: MedRxiv – volume: 12 start-page: 777 year: 2020 ident: bib0044 article-title: COVID-19 transmission trajectories-monitoring the pandemic in the worldwide context publication-title: Viruses – year: 2020 ident: bib0015 article-title: Demand for hospitalization services for COVID-19 patients in Brazil publication-title: MedRxiv – reference: Meller, M. (2020). The asymptomatic and pre-symptomatic spread of COVID-19. – year: 2020 ident: bib0008 article-title: COVID-19 (SARS-CoV-2) ventilator resource management using a network optimization model and predictive system demand publication-title: MedRxiv – volume: 140 start-page: 110107 year: 2020 ident: bib0060 article-title: A mathematical model for COVID-19 transmission by using the caputo fractional derivative publication-title: Chaos, Solitons & Fractals – reference: Bushaj, S., Yin, X., Beqiri, A., Andrews, D., & Büyüktahtakın, İ. E. (2021b). A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization. Under Review. – volume: 173 start-page: 21 year: 2020 end-page: 28 ident: bib0063 article-title: Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic publication-title: Annals of Internal Medicine – volume: 185 start-page: 33 year: 2003 end-page: 72 ident: bib0032 article-title: Analyzing bioterror response logistics: The case of smallpox publication-title: Mathematical Biosciences – volume: 307 start-page: 53 year: 2019 end-page: 69 ident: bib0035 article-title: Optimizing multi-modal cancer treatment under 3D spatio-temporal tumor growth publication-title: Mathematical Biosciences – reference: Hogan, A. (2020). Watch: Ventilators are in high demand for COVID-19 patients. How do they work? – volume: 249 start-page: 188 year: 2016 end-page: 199 ident: bib0050 article-title: Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective publication-title: European Journal of Operational Research – volume: 171 start-page: 33 year: 2001 end-page: 58 ident: bib0068 article-title: Resource allocation for epidemic control over short time horizons publication-title: Mathematical Biosciences – year: 2020 ident: bib0061 article-title: Impact of social distancing measures on COVID-19 healthcare demand in Central Texas publication-title: MedRxiv – reference: . Accessed January 20, 2021. – year: 2020 ident: bib0029 article-title: The risk of COVID-19 transmission in train passengers: An epidemiological and modelling study publication-title: Clinical Infectious Diseases – volume: 382 start-page: e41 year: 2020 ident: bib0055 article-title: Critical supply shortages-the need for ventilators and personal protective equipment during the COVID-19 pandemic publication-title: New England Journal of Medicine – reference: Accessed February 12, 2021. – start-page: 1 year: 2021 end-page: 26 ident: bib0066 article-title: A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations publication-title: Published online ahead of print in Health Care Management Science – volume: 166 start-page: 114077 year: 2020 ident: bib0033 article-title: A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece publication-title: Expert Systems with Applications – reference: Burki, T. (2020). China’s successful control of COVID-19. – reference: Blanco, V., Gázquez, R., & Leal, M. (2020). Reallocating and sharing health equipments in sanitary emergency situations: The COVID-19 case in Spain. – year: 2021 ident: bib0011 article-title: Risk-averse multi-stage stochastic optimization for surveillance and operations planning of a forest insect infestation publication-title: European Journal of Operational Research – reference: Bertsimas, D., Boussioux, L., Wright, R. C., Delarue, A., Digalakis Jr, V., Jacquillat, A., Kitane, D. L., Lukin, G., Li, M. L., Mingardi, L. et al. (2020). From predictions to prescriptions: A data-driven response to COVID-19. – volume: 33 start-page: 808 year: 2021 end-page: 834 ident: bib0036 article-title: A multistage stochastic programming approach to the optimal surveillance and control of the emerald ash borer in cities publication-title: INFORMS Journal on Computing – volume: 0 start-page: 1 year: 2021 end-page: 23 ident: bib0067 article-title: Risk-averse multi-stage stochastic programming to optimizing vaccine allocation and treatment logistics for effective epidemic response publication-title: IISE Transactions on Healthcare Systems Engineering – volume: 118 start-page: 423 year: 2018 end-page: 439 ident: bib0020 article-title: Stochastic dynamic resource allocation for HIV prevention and treatment: An approximate dynamic programming approach publication-title: Computers & Industrial Engineering – volume: 139 start-page: 393 year: 2012 end-page: 410 ident: bib0021 article-title: Epidemics control and logistics operations: A review publication-title: International Journal of Production Economics – volume: 267 start-page: 1051 year: 2018 end-page: 1074 ident: bib0001 article-title: Risk management for forestry planning under uncertainty in demand and prices publication-title: European Journal of Operational Research – start-page: 97 year: 2012 end-page: 143 ident: bib0022 article-title: Multistage stochastic programming: A scenario tree based approach to planning under uncertainty publication-title: Decision theory models for applications in artificial intelligence: Concepts and solutions – volume: 2020 year: 2020 ident: bib0069 article-title: Mathematical model for coronavirus disease 2019 (COVID-19) containing isolation class publication-title: BioMed Research International – reference: JHU (2020). COVID-19 United States cases by county. – start-page: 106945 year: 2020 ident: bib0046 article-title: A simplified math approach to predict ICU beds and mortality rate for hospital emergency planning under COVID-19 pandemic publication-title: Computers & Chemical Engineering – year: 2020 ident: bib0048 article-title: A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19 publication-title: Naval Research Logistics (NRL) – reference: CNN (2021). New Zealand and Australia were Covid success stories. why are they behind on vaccine rollouts? – volume: 15 start-page: e0241027 year: 2020 ident: bib0040 article-title: A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic publication-title: Plos One – reference: Lilleker, D. (2021). The good, the bad and the ugly of government responses to COVID. – volume: 32 start-page: 145 year: 2020 end-page: 163 ident: bib0004 article-title: Sampling scenario set partition dual bounds for multistage stochastic programs publication-title: INFORMS Journal on Computing – volume: 6 start-page: e19368 year: 2020 ident: bib0002 article-title: Mathematical modelling to assess the impact of lockdown on COVID-19 transmission in India: Model development and validation publication-title: JMIR Public Health and Surveillance – volume: 265 start-page: 1046 year: 2018 end-page: 1063 ident: bib0014 article-title: A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa publication-title: European Journal of Operational Research – volume: 230 start-page: 113610 year: 2020 ident: bib0062 article-title: Impacts of transportation and meteorological factors on the transmission of COVID-19 publication-title: International Journal of Hygiene and Environmental Health – year: 2020 ident: bib0070 article-title: Effects of human behaviour changes during the COVID-19 pandemic on influenza spread in Hong Kong publication-title: Clinical Infectious Diseases – volume: 259 start-page: 308 year: 2017 end-page: 321 ident: bib0034 article-title: Optimizing invasive species management: A mixed-integer linear programming approach publication-title: European Journal of Operational Research – reference: . Accessed August 2, 2021. – reference: Environmental Systems Research Institute (ESRI) (2021). ArcGIS map. – volume: 323 start-page: 1773 year: 2020 end-page: 1774 ident: bib0064 article-title: A framework for rationing ventilators and critical care beds during the COVID-19 pandemic publication-title: JAMA – reference: Glass, H. (2020). High-acuity ventilator cost guide. – volume: 5 start-page: e452 year: 2020 end-page: e459 ident: bib0037 article-title: Impact of delays on effectiveness of contact tracing strategies for COVID-19: A modelling study publication-title: The Lancet Public Health – reference: Parker, F., Sawczuk, H., Ganjkhanloo, F., Ahmadi, F., & Ghobadi, K. (2020). Optimal resource and demand redistribution for healthcare systems under stress from COVID-19. – volume: 132 start-page: 1317 year: 2020 end-page: 1332 ident: bib0051 article-title: Intubation and ventilation amid the COVID-19 outbreak: Wuhan’s experience publication-title: Anesthesiology – reference: . – reference: . Accessed May 30, 2021. – reference: . Accessed November 30, 2020. – volume: 138 start-page: 101967 year: 2020 ident: bib0026 article-title: A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19) publication-title: Transportation Research Part E: Logistics and Transportation Review – volume: 26 start-page: 1443 year: 2002 end-page: 1471 ident: bib0057 article-title: Conditional value-at-risk for general loss distributions publication-title: Journal of Banking & Finance – volume: 215 start-page: 144 year: 2008 end-page: 151 ident: bib0059 article-title: Finding optimal vaccination strategies under parameter uncertainty using stochastic programming publication-title: Mathematical Biosciences – reference: CDC (2021). Clinical questions about COVID-19: Questions and answers. – year: 2020 ident: bib0018 article-title: Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model publication-title: Medical Journal Armed Forces India – year: 2020 ident: bib0039 article-title: Early dynamics of transmission and control of COVID-19: A mathematical modelling study publication-title: The Lancet Infectious Diseases – reference: Accessed November 30, 2020. – reference: Yin, X., Bushaj, S., Yuan, Y., & Büyüktahtakın, İ. E. (2021). COVID-19: An agent-based simulation-optimization approach to vaccine center location vaccine allocation problem. Under Review. – volume: 175 start-page: 461 year: 2019 end-page: 502 ident: bib0071 article-title: Stochastic dual dynamic integer programming publication-title: Mathematical Programming – ident: 10.1016/j.ejor.2021.11.052_bib0028 – ident: 10.1016/j.ejor.2021.11.052_bib0053 – volume: 265 start-page: 1046 issue: 3 year: 2018 ident: 10.1016/j.ejor.2021.11.052_bib0014 article-title: A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2017.08.037 – volume: 230 start-page: 113610 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0062 article-title: Impacts of transportation and meteorological factors on the transmission of COVID-19 publication-title: International Journal of Hygiene and Environmental Health doi: 10.1016/j.ijheh.2020.113610 – volume: 32 start-page: 145 issue: 1 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0004 article-title: Sampling scenario set partition dual bounds for multistage stochastic programs publication-title: INFORMS Journal on Computing doi: 10.1287/ijoc.2018.0885 – start-page: 106945 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0046 article-title: A simplified math approach to predict ICU beds and mortality rate for hospital emergency planning under COVID-19 pandemic publication-title: Computers & Chemical Engineering doi: 10.1016/j.compchemeng.2020.106945 – volume: 7 start-page: 201131 issue: 8 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0024 article-title: The behavioural challenge of the COVID-19 pandemic: Indirect measurements and personalized attitude changing treatments (impact) publication-title: Royal Society Open Science doi: 10.1098/rsos.201131 – volume: 171 start-page: 33 issue: 1 year: 2001 ident: 10.1016/j.ejor.2021.11.052_bib0068 article-title: Resource allocation for epidemic control over short time horizons publication-title: Mathematical Biosciences doi: 10.1016/S0025-5564(01)00050-5 – volume: 259 start-page: 308 issue: 1 year: 2017 ident: 10.1016/j.ejor.2021.11.052_bib0034 article-title: Optimizing invasive species management: A mixed-integer linear programming approach publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2016.09.049 – ident: 10.1016/j.ejor.2021.11.052_bib0012 doi: 10.1007/s10479-022-04926-7 – volume: 42 start-page: 699 issue: 5 year: 2016 ident: 10.1016/j.ejor.2021.11.052_bib0005 article-title: The standard of care of patients with ARDS: Ventilatory settings and rescue therapies for refractory hypoxemia publication-title: Intensive Care Medicine doi: 10.1007/s00134-016-4325-4 – volume: 23 start-page: 914 issue: 6 year: 2017 ident: 10.1016/j.ejor.2021.11.052_bib0030 article-title: Stockpiling ventilators for influenza pandemics publication-title: Emerging Infectious Diseases doi: 10.3201/eid2306.161417 – volume: 266 start-page: 595 issue: 2 year: 2018 ident: 10.1016/j.ejor.2021.11.052_bib0045 article-title: Bounds on risk-averse mixed-integer multi-stage stochastic programming problems with mean-CVaR publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2017.10.038 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0018 article-title: Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model publication-title: Medical Journal Armed Forces India doi: 10.1016/j.mjafi.2020.03.022 – ident: 10.1016/j.ejor.2021.11.052_bib0052 – volume: 215 start-page: 144 issue: 2 year: 2008 ident: 10.1016/j.ejor.2021.11.052_bib0059 article-title: Finding optimal vaccination strategies under parameter uncertainty using stochastic programming publication-title: Mathematical Biosciences doi: 10.1016/j.mbs.2008.07.006 – volume: 0 start-page: 1 issue: 0 year: 2021 ident: 10.1016/j.ejor.2021.11.052_bib0067 article-title: Risk-averse multi-stage stochastic programming to optimizing vaccine allocation and treatment logistics for effective epidemic response publication-title: IISE Transactions on Healthcare Systems Engineering – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0070 article-title: Effects of human behaviour changes during the COVID-19 pandemic on influenza spread in Hong Kong publication-title: Clinical Infectious Diseases – ident: 10.1016/j.ejor.2021.11.052_bib0025 – volume: 249 start-page: 188 issue: 1 year: 2016 ident: 10.1016/j.ejor.2021.11.052_bib0050 article-title: Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2015.05.048 – volume: 382 start-page: e41 issue: 18 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0055 article-title: Critical supply shortages-the need for ventilators and personal protective equipment during the COVID-19 pandemic publication-title: New England Journal of Medicine doi: 10.1056/NEJMp2006141 – ident: 10.1016/j.ejor.2021.11.052_bib0031 – volume: 15 start-page: e0241468 issue: 11 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0041 article-title: Human mobility trends during the early stage of the COVID-19 pandemic in the United States publication-title: PLoS One doi: 10.1371/journal.pone.0241468 – start-page: 1 year: 2021 ident: 10.1016/j.ejor.2021.11.052_bib0013 article-title: Stage-t scenario dominance for risk-averse multi-stage stochastic mixed-integer programs publication-title: Accepted for publication in Annals of Operations Research – volume: 132 start-page: 1317 issue: 6 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0051 article-title: Intubation and ventilation amid the COVID-19 outbreak: Wuhan’s experience publication-title: Anesthesiology doi: 10.1097/ALN.0000000000003296 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0042 article-title: Forecasting COVID-19 and analyzing the effect of government interventions publication-title: MedRxiv – volume: 166 start-page: 114077 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0033 article-title: A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.114077 – volume: 12 start-page: 777 issue: 7 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0044 article-title: COVID-19 transmission trajectories-monitoring the pandemic in the worldwide context publication-title: Viruses doi: 10.3390/v12070777 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0008 article-title: COVID-19 (SARS-CoV-2) ventilator resource management using a network optimization model and predictive system demand publication-title: MedRxiv – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0029 article-title: The risk of COVID-19 transmission in train passengers: An epidemiological and modelling study publication-title: Clinical Infectious Diseases – volume: 175 start-page: 461 issue: 1 year: 2019 ident: 10.1016/j.ejor.2021.11.052_bib0071 article-title: Stochastic dual dynamic integer programming publication-title: Mathematical Programming doi: 10.1007/s10107-018-1249-5 – ident: 10.1016/j.ejor.2021.11.052_bib0010 – year: 2021 ident: 10.1016/j.ejor.2021.11.052_bib0011 article-title: Risk-averse multi-stage stochastic optimization for surveillance and operations planning of a forest insect infestation publication-title: European Journal of Operational Research – ident: 10.1016/j.ejor.2021.11.052_bib0049 – volume: 118 start-page: 423 year: 2018 ident: 10.1016/j.ejor.2021.11.052_bib0020 article-title: Stochastic dynamic resource allocation for HIV prevention and treatment: An approximate dynamic programming approach publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2018.01.018 – volume: 139 start-page: 393 issue: 2 year: 2012 ident: 10.1016/j.ejor.2021.11.052_bib0021 article-title: Epidemics control and logistics operations: A review publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2012.05.023 – start-page: 97 year: 2012 ident: 10.1016/j.ejor.2021.11.052_bib0022 article-title: Multistage stochastic programming: A scenario tree based approach to planning under uncertainty – volume: 185 start-page: 33 issue: 1 year: 2003 ident: 10.1016/j.ejor.2021.11.052_bib0032 article-title: Analyzing bioterror response logistics: The case of smallpox publication-title: Mathematical Biosciences doi: 10.1016/S0025-5564(03)00090-7 – volume: 2020 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0069 article-title: Mathematical model for coronavirus disease 2019 (COVID-19) containing isolation class publication-title: BioMed Research International doi: 10.1155/2020/3452402 – volume: 33 start-page: 808 issue: 2 year: 2021 ident: 10.1016/j.ejor.2021.11.052_bib0036 article-title: A multistage stochastic programming approach to the optimal surveillance and control of the emerald ash borer in cities publication-title: INFORMS Journal on Computing – volume: 5 start-page: e452 issue: 8 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0037 article-title: Impact of delays on effectiveness of contact tracing strategies for COVID-19: A modelling study publication-title: The Lancet Public Health doi: 10.1016/S2468-2667(20)30157-2 – volume: 323 start-page: 1773 issue: 18 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0064 article-title: A framework for rationing ventilators and critical care beds during the COVID-19 pandemic publication-title: JAMA doi: 10.1001/jama.2020.5046 – start-page: 1 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0054 article-title: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review publication-title: Annals of Operations Research – volume: 15 start-page: e0241027 issue: 10 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0040 article-title: A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic publication-title: Plos One doi: 10.1371/journal.pone.0241027 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0061 article-title: Impact of social distancing measures on COVID-19 healthcare demand in Central Texas publication-title: MedRxiv – ident: 10.1016/j.ejor.2021.11.052_bib0065 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0056 article-title: Selectively caring for the most severe COVID-19 patients delays ICU bed shortages more than increasing hospital capacity publication-title: MedRxiv – volume: 173 start-page: 21 issue: 1 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0063 article-title: Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic publication-title: Annals of Internal Medicine doi: 10.7326/M20-1260 – volume: 20 start-page: 1247 issue: 11 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0003 article-title: Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study publication-title: The Lancet Infectious Diseases doi: 10.1016/S1473-3099(20)30553-3 – ident: 10.1016/j.ejor.2021.11.052_bib0017 – ident: 10.1016/j.ejor.2021.11.052_bib0027 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0015 article-title: Demand for hospitalization services for COVID-19 patients in Brazil publication-title: MedRxiv – volume: 27 start-page: 1772 issue: 3 year: 2017 ident: 10.1016/j.ejor.2021.11.052_bib0058 article-title: A scalable bounding method for multistage stochastic programs publication-title: SIAM Journal on Optimization doi: 10.1137/16M1075594 – volume: 6 start-page: e19368 issue: 2 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0002 article-title: Mathematical modelling to assess the impact of lockdown on COVID-19 transmission in India: Model development and validation publication-title: JMIR Public Health and Surveillance doi: 10.2196/19368 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0048 article-title: A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19 publication-title: Naval Research Logistics (NRL) doi: 10.1002/nav.21905 – ident: 10.1016/j.ejor.2021.11.052_bib0006 – ident: 10.1016/j.ejor.2021.11.052_bib0023 – ident: 10.1016/j.ejor.2021.11.052_bib0019 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0039 article-title: Early dynamics of transmission and control of COVID-19: A mathematical modelling study publication-title: The Lancet Infectious Diseases doi: 10.1016/S1473-3099(20)30144-4 – ident: 10.1016/j.ejor.2021.11.052_bib0007 doi: 10.1101/2020.06.26.20141127 – volume: 26 start-page: 1443 issue: 7 year: 2002 ident: 10.1016/j.ejor.2021.11.052_bib0057 article-title: Conditional value-at-risk for general loss distributions publication-title: Journal of Banking & Finance doi: 10.1016/S0378-4266(02)00271-6 – volume: 307 start-page: 53 year: 2019 ident: 10.1016/j.ejor.2021.11.052_bib0035 article-title: Optimizing multi-modal cancer treatment under 3D spatio-temporal tumor growth publication-title: Mathematical Biosciences doi: 10.1016/j.mbs.2018.10.010 – volume: 140 start-page: 110107 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0060 article-title: A mathematical model for COVID-19 transmission by using the caputo fractional derivative publication-title: Chaos, Solitons & Fractals doi: 10.1016/j.chaos.2020.110107 – volume: 138 start-page: 101967 year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0026 article-title: A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19) publication-title: Transportation Research Part E: Logistics and Transportation Review doi: 10.1016/j.tre.2020.101967 – year: 2020 ident: 10.1016/j.ejor.2021.11.052_bib0038 article-title: Epidemiological benchmarks of the COVID-19 outbreak control in China after Wuhan’s lockdown: A modelling study with an empirical approach – ident: 10.1016/j.ejor.2021.11.052_bib0043 – ident: 10.1016/j.ejor.2021.11.052_bib0016 – ident: 10.1016/j.ejor.2021.11.052_bib0009 – volume: 267 start-page: 1051 issue: 3 year: 2018 ident: 10.1016/j.ejor.2021.11.052_bib0001 article-title: Risk management for forestry planning under uncertainty in demand and prices publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2017.12.022 – ident: 10.1016/j.ejor.2021.11.052_bib0047 – start-page: 1 year: 2021 ident: 10.1016/j.ejor.2021.11.052_bib0066 article-title: A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations publication-title: Published online ahead of print in Health Care Management Science |
| SSID | ssj0001515 |
| Score | 2.596163 |
| Snippet | •Multi-stage stochastic epidemics-ventilator-logistics compartmental model.•Optimize ventilator allocation under asymptomatic uncertainty and... This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges... |
| SourceID | pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 255 |
| SubjectTerms | COVID-19 Mean-CVaR multi-stage stochastic mixed-integer programming model OR in health services Pandemic resource and ventilator allocation Risk-averse optimization |
| Title | COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk |
| URI | https://dx.doi.org/10.1016/j.ejor.2021.11.052 https://www.ncbi.nlm.nih.gov/pubmed/34866765 https://www.proquest.com/docview/2607306806 https://pubmed.ncbi.nlm.nih.gov/PMC8632406 |
| Volume | 304 |
| WOSCitedRecordID | wos000861383000005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6860 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001515 issn: 0377-2217 databaseCode: AIEXJ dateStart: 19950105 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9pAEF6RpKraQx_0RR_RVqp6QY7wc01vCRA1FSI5kMo9Wev1WkCIocZE4dD_3hnv2kCiROmhlwUtfsnzMTM7O_MNIV_auJ2ZJKYhI4cbTmQLA7wMbgjTjEQsXIvHBWV-nw0GfhC0z2q1P2UtzNWUpal_fd2e_1dRwxwIG0tn_0Hc1UVhAr6D0GEEscP4IMF3Tn-edA2zjWv9Ls-50c1QozVnoBwukRlgivardBSLdMcprrybC2zwuSoa42YwCpUskK-qFPQ7o_jao4WJrIwtahKhKtj8S3EVBEsJKNHWEqMAuFF_1Fmpj4ucj3J-gQ7ukalK0pq9RVZZjjNwjNUuyWh5OdalaTpkYdkbIQtdqsWYYVmqaLNUw7ZqQ7yFN61UFZHvLWWv4g6TAzmZIbOrZR4gH6sixN1m1h6chsfn_X447AXDr_PfBjYdw8153YFlh-xZzG2DUtw7POkFPypTjt5esQ2ln1dXXakEwZu3vcuzub1yuZmAu-HRDF-QZ3opQg8VhF6Smkzr5HFZCVEnz8uOH1QbgDp5ukFf-YoEJdS-0Q2gUQ00ugYanSV0DTSqgEYLoNENoFEAGkWgvSbnx71h57uhO3UYAgxAbjC_5UkHXGHfldyO3JYTJW4kXSG8hCXggQtpclAELDY5xy6zfgx2gEVFnbYjhP2G7KazVL4jFPnymBVHIvFhtdFuRa7PwEm17dhzEunFDWKWbzkUmsYeu6lMwzJfcRKiZEKUDKxvQ5BMgzSrc-aKxOXeo91SeKF2Q5V7GQLw7j3vcynpEHQ0brzxVM6Wi9Dy0JB68JIa5K2SfPUctuNjmrnbIGwLE9UByP--_Us6HhU88D62Wmh57x9w3w_kyfqP-JHs5tlSfiKPxFU-XmT7ZIcF_r4G_1-dxNDN |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=COVID-19%3A+Data-Driven+optimal+allocation+of+ventilator+supply+under+uncertainty+and+risk&rft.jtitle=European+journal+of+operational+research&rft.au=Yin%2C+Xuecheng&rft.au=B%C3%BCy%C3%BCktahtak%C4%B1n%2C+I+Esra&rft.au=Patel%2C+Bhumi+P&rft.date=2023-01-01&rft.issn=0377-2217&rft.volume=304&rft.issue=1&rft.spage=255&rft_id=info:doi/10.1016%2Fj.ejor.2021.11.052&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0377-2217&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0377-2217&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0377-2217&client=summon |