Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of “what-...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of parallel and distributed computing Vol. 191; p. 104899
Main Authors:	Bhattacharya, Parantapa, Machi, Dustin, Chen, Jiangzhuo, Hoops, Stefan, Lewis, Bryan, Mortveit, Henning, Venkatramanan, Srinivasan, Wilson, Mandy L., Marathe, Achla, Porebski, Przemyslaw, Klahn, Brian, Outten, Joseph, Vullikanti, Anil, Xie, Dawen, Adiga, Abhijin, Brown, Shawn, Barrett, Christopher, Marathe, Madhav
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.09.2024
Subjects:	Agent-based modeling and simulation Covid-19 vaccine acceptance High performance computing workflow Covid-19 vaccine acceptance Agent-based modeling and simulation High performance computing workflow
ISSN:	0743-7315, 1096-0848
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of “what-if” scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10 K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4 K to 28.2 K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2 K to 29.9 K (a 6% improvement) nationwide. •Presents MacKenzie, a novel multi-cluster HPC job scheduler.•A novel workflow that reduces execution time and improves productivity.•Detailed, fine-grained, data driven epidemic models.•Detailed analysis of the COVID-19 vaccine allocation problem.•Real world workflow; has been used repeatedly brief VA and Federal policymakers.
AbstractList	We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of “what-if” scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide. We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide.We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide. We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of “what-if” scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10 K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4 K to 28.2 K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2 K to 29.9 K (a 6% improvement) nationwide. •Presents MacKenzie, a novel multi-cluster HPC job scheduler.•A novel workflow that reduces execution time and improves productivity.•Detailed, fine-grained, data driven epidemic models.•Detailed analysis of the COVID-19 vaccine allocation problem.•Real world workflow; has been used repeatedly brief VA and Federal policymakers.
ArticleNumber	104899
Author	Venkatramanan, Srinivasan Marathe, Achla Klahn, Brian Chen, Jiangzhuo Lewis, Bryan Marathe, Madhav Wilson, Mandy L. Brown, Shawn Machi, Dustin Bhattacharya, Parantapa Porebski, Przemyslaw Mortveit, Henning Adiga, Abhijin Hoops, Stefan Barrett, Christopher Outten, Joseph Vullikanti, Anil Xie, Dawen
AuthorAffiliation	e Hewlett Packard, San Francisco, USA b Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA a Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA c Department of Computer Science, University of Virginia, Charlottesville, VA, USA d Department of Systems Engineering, University of Virginia, Charlottesville, VA, USA
AuthorAffiliation_xml	– name: c Department of Computer Science, University of Virginia, Charlottesville, VA, USA – name: a Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – name: b Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA – name: e Hewlett Packard, San Francisco, USA – name: d Department of Systems Engineering, University of Virginia, Charlottesville, VA, USA
Author_xml	– sequence: 1 givenname: Parantapa orcidid: 0000-0002-3626-9939 surname: Bhattacharya fullname: Bhattacharya, Parantapa email: parantapa@virginia.edu organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 2 givenname: Dustin surname: Machi fullname: Machi, Dustin organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 3 givenname: Jiangzhuo surname: Chen fullname: Chen, Jiangzhuo organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 4 givenname: Stefan surname: Hoops fullname: Hoops, Stefan organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 5 givenname: Bryan surname: Lewis fullname: Lewis, Bryan organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 6 givenname: Henning surname: Mortveit fullname: Mortveit, Henning organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 7 givenname: Srinivasan surname: Venkatramanan fullname: Venkatramanan, Srinivasan organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 8 givenname: Mandy L. surname: Wilson fullname: Wilson, Mandy L. organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 9 givenname: Achla surname: Marathe fullname: Marathe, Achla organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 10 givenname: Przemyslaw surname: Porebski fullname: Porebski, Przemyslaw organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 11 givenname: Brian surname: Klahn fullname: Klahn, Brian organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 12 givenname: Joseph surname: Outten fullname: Outten, Joseph organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 13 givenname: Anil surname: Vullikanti fullname: Vullikanti, Anil organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 14 givenname: Dawen surname: Xie fullname: Xie, Dawen organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 15 givenname: Abhijin surname: Adiga fullname: Adiga, Abhijin organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 16 givenname: Shawn surname: Brown fullname: Brown, Shawn organization: Hewlett Packard, San Francisco, USA – sequence: 17 givenname: Christopher surname: Barrett fullname: Barrett, Christopher organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA – sequence: 18 givenname: Madhav surname: Marathe fullname: Marathe, Madhav organization: Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38774820$$D View this record in MEDLINE/PubMed
BookMark	eNp9Uctu1DAUtVARnRZ-gAXykk0G23k5CAmh4dGRKsoC2FqOczP14NjBdlL1T_q5dTRDBSy68ZXvPQ_7njN0Yp0FhF5SsqaEVm_26_3YqTUjrEiNgjfNE7SipKkywgt-glakLvKszml5is5C2BNCaVnzZ-g053VdcEZW6O6rm8HgYTJRZ8pMIYLHN87_6o27weE23QccHQ7TODofsQdpsqgHwBffNhlY2RroMIy6g0ErHJQGq-At3toZQtQ7GbXd4XgNWA-jVBG7Hs9SKW0BpwJjlAmPncWbq5_bjxltcBiTSfccPe2lCfDiWM_Rj8-fvm8ussurL9vNh8tMFZTFrGIADVV12VecFV3JgPaKVmVB6rboGwV126WDSVkRRmif5rQkkHcVkcDaJj9H7w-649QOkLA2emnE6PUg_a1wUot_J1Zfi52bBaWUlHWzKLw-Knj3e0q_FoMOCoyRFtwURE5KXuW8bPIEffW32YPLnzwSgB0AyrsQPPQPEErEErrYiyV0sYQuDqEnEv-PpHRMi3fLg7V5nPruQIW04lmDF8cEO-1BRdE5_Rj9Hgwbyo8
CitedBy_id	crossref_primary_10_1016_j_epidem_2025_100818
Cites_doi	10.1126/science.1175570 10.1016/j.ejor.2022.06.034 10.3390/vaccines9080876 10.1016/S1473-3099(21)00143-2 10.1016/j.puhe.2021.02.025 10.1038/s41591-022-01728-z 10.1371/journal.pbio.3001211 10.1073/pnas.2311573120 10.1073/pnas.2026322118 10.1016/S1473-3099(21)00057-8 10.1126/science.abe6959 10.1016/j.mbs.2021.108614 10.1038/s41598-022-26468-5 10.1126/science.abg2334 10.1016/S2214-109X(21)00563-5 10.1016/j.jpdc.2018.01.001 10.1038/s41467-021-21018-5 10.1186/s13062-015-0071-8 10.1177/10943420221127034 10.1371/journal.pone.0013767 10.1016/j.jpdc.2016.04.004 10.1073/pnas.2025786118 10.1007/s41019-017-0047-z 10.15585/mmwr.mm7019e3 10.1007/s00285-022-01858-5 10.14257/ijt.2016.4.1.03 10.1016/0965-8564(96)00004-3 10.1007/s10654-020-00671-y 10.1038/s41467-022-28170-6 10.1186/s12879-022-07486-0 10.1214/aoms/1177731829 10.1038/nature02541 10.1371/journal.pcbi.1007111 10.1016/0021-9991(76)90041-3 10.1002/jmv.28156 10.1038/s41467-022-31441-x 10.3390/vaccines9020160 10.1007/s40484-020-0199-0 10.1073/pnas.2021726118
ContentType	Journal Article
Copyright	2024
Copyright_xml	– notice: 2024
DBID	AAYXX CITATION NPM 7X8 5PM
DOI	10.1016/j.jpdc.2024.104899
DatabaseName	CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef PubMed MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed
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	Computer Science
EISSN	1096-0848
ExternalDocumentID	PMC11105799 38774820 10_1016_j_jpdc_2024_104899 S0743731524000637
Genre	Journal Article
GrantInformation_xml	– fundername: NCEZID CDC HHS grantid: U01 CK000589 – fundername: ACL HHS grantid: U01CK000589 – fundername: NIGMS NIH HHS grantid: R01 GM109718
GroupedDBID	--K --M -~X .~1 0R~ 1B1 1~. 1~5 29L 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFSI ABJNI ABMAC ABTAH ABXDB ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADFGL ADHUB ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CAG COF CS3 DM4 DU5 E.L EBS EFBJH EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA K-O KOM LG5 LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TN5 TWZ WUQ XJT XOL XPP ZMT ZU3 ZY4 ~G- ~G0 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD AGCQF NPM 7X8 5PM
ID	FETCH-LOGICAL-c412t-62ee91c75f6824d52e1fc165407b4f9ce7bdce72aa60201f2e1150e3d60ae2b93
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001265739700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0743-7315
IngestDate	Tue Sep 30 17:00:19 EDT 2025 Sat Nov 01 14:55:57 EDT 2025 Thu Sep 04 04:46:16 EDT 2025 Sat Nov 29 06:55:14 EST 2025 Tue Nov 18 20:03:59 EST 2025 Tue Jun 18 08:51:39 EDT 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Covid-19 vaccine acceptance Agent-based modeling and simulation High performance computing workflow
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c412t-62ee91c75f6824d52e1fc165407b4f9ce7bdce72aa60201f2e1150e3d60ae2b93
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0002-3626-9939
PMID	38774820
PQID	3058638593
PQPubID	23479
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_11105799 proquest_miscellaneous_3058638593 pubmed_primary_38774820 crossref_primary_10_1016_j_jpdc_2024_104899 crossref_citationtrail_10_1016_j_jpdc_2024_104899 elsevier_sciencedirect_doi_10_1016_j_jpdc_2024_104899
PublicationCentury	2000
PublicationDate	2024-09-01
PublicationDateYYYYMMDD	2024-09-01
PublicationDate_xml	– month: 09 year: 2024 text: 2024-09-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	Journal of parallel and distributed computing
PublicationTitleAlternate	J Parallel Distrib Comput
PublicationYear	2024
Publisher	Elsevier Inc
Publisher_xml	– name: Elsevier Inc
References	Perrault, Fang, Sinha, Tambe (br0590) 2019 Venkatramanan, Chen, Fadikar, Gupta, Higdon, Lewis, Marathe, Mortveit, Vullikanti (br0770) 2019; 15 Beckman, Baggerly, McKay (br0080) 1996; 30 Klonower (br0440) Virginia Department of Health (br0790) Borchering, Viboud, Howerton, Smith, Truelove, Runge, Reich, Contamin, Levander, Salerno (br0120) 2021; 70 Bhattacharya, Chen, Hoops, Machi, Lewis, Venkatramanan (br0090) 2023; 37 Troiano, Nardi (br0710) 2021; 194 Hager, Drobnis, Fang, Ghani, Greenwald, Lyons, Parkes, Schultz, Saria, Smith (br0380) 2019 Mutombo, Fallah, Munodawafa, Kabel, Houeto, Goronga, Mweemba, Balance, Onya, Kamba (br0580) 2022; 10 Zhao, Chen (br0820) 2020; 8 Matrajt, Longini (br0510) 2010; 5 Dror, Eisenbach, Taiber, Morozov, Mizrachi, Zigron, Srouji, Sela (br0270) 2020; 35 Cénat, Noorishad, Moshirian Farahi, Darius, Mesbahi El Aouame, Onesi, Broussard, Furyk, Yaya, Caulley (br0170) 2023; 95 Mortveit, Adiga, Barrett, Chen, Chungbaek, Eubank, Kuhlman, Lewis, Swarup, Venkatramanan, Wilson, Xie, Marathe (br0570) 2020 Sowa, Kiszkiel, Laskowski, Alimowski, Szczerbiński, Paniczko, Moniuszko-Malinowska, Kamiński (br0640) 2021; 9 Merzky, Turilli, Titov, Al-Saadi, Jha (br0530) Nov. 2021 Rao (br0610) 2016; 93 Spjuth, Bongcam-Rudloff, Hernández, Forer, Giovacchini, Guimera (br0650) 2015; 10 Wrigley-Field, Kiang, Riley, Barbieri, Chen, Duchowny (br0810) 2021 Fineberg, Wilson (br0310) 2009 Goldstein, Cassidy, Wachter (br0360) 2021; 118 Fitzpatrick, Galvani (br0320) 2021; 371 Ferguson, Laydon, Nedjati Gilani, Imai, Ainslie, Baguelin, Bhatia, Boonyasiri, Cucunuba Perez, Cuomo-Dannenburg (br0300) 2020 The University of Oxford. The multinational time use study (MTUS), Last accessed: February 2020. br0630 Barrett, Beckman, Khan, Kumar, Marathe, Stretz, Dutta, Lewis (br0070) 2009 Torche, Nobles (br0700) 2023; 120 Babuji, Woodard, Li, Katz, Clifford, Kumar, Lacinski, Chard, Wozniak, Foster, Wilde, Chard (br0060) 2019 Swarup, Marathe (br0660) 2017 Borchering, Mullany, Howerton, Chinazzi, Smith, Qin (br0110) 2023; 17 Hogan, Winskill, Watson, Walker, Whittaker, Baguelin, Haw, Lochen, Gaythorpe, Ainslie (br0400) 2020 Delmas, Dronnier, Zitt (br0250) 2023; 86 Eubank, Guclu, Anil Kumar, Marathe, Srinivasan, Toroczkai, Wang (br0280) 2004; 429 Moghadas, Vilches, Zhang, Nourbakhsh, Sah, Fitzpatrick, Galvani (br0550) 2021; 19 br0670 br0230 Acuña-Zegarra, Díaz-Infante, Baca-Carrasco, Liceaga (br0030) 2021 (br0390) 2020 Huberts, Thijssen (br0410) 2023; 305 Arino, Portet (br0050) 2020; 5 Kaplan, Milstein (br0430) 2021; 118 de Miguel-Arribas, Aleta, Moreno (br0240) 2022; 22 Eubank, Anil Kumar, Marathe, Srinivasan, Wang (br0290) 2006; vol. 70 Pertwee, Simas, Larson (br0600) 2022; 28 Breiman (br0130) 1984 Collier, Wozniak, Stevens, Babuji, Binois, Fadikar, Würth, Chard, Ozik (br0220) 2023 Lemaitre, Pasetto, Zanon, Bertuzzo, Mari, Miccoli, Casagrandi, Gatto, Rinaldo (br0480) 2021 United States Censuc Bureau. American Community Survey 2013-2017 5-year estimates, Last accessed: February 2020. br0760 Lum, Chungbaek, Eubank, Marathe (br0490) 2016; 4 Lazebnik, Shami, Bunimovich-Mendrazitsky (br0470) 2022; 35 Buckner, Chowell, Springborn (br0150) 2021; 118 (br0680) 2020 Chen, Hoops, Lewis, Henning, Mortveit, Wilson (br0200) 2019 Machi, Bhattacharya, Hoops, Chen, Mortveit, Venkatramanan (br0500) 2021 Lasser, Sorger, Richter, Thurner, Schmid, Klimek (br0450) 2022; 13 Lazarus, Wyka, White, Picchio, Rabin, Ratzan, Parsons Leigh, Hu, El-Mohandes (br0460) 2022; 13 Walensky, Fauci (br0800) 2021 Abell, McCaw, Baker (br0020) 2022 Zheng, Xia, Guo, Dehmer (br0830) 2018; 115 United States Department of Labor, Bureau of Labor Statistics. The American Time Use Survey (ATUS), Last accessed: February 2020. Sallam (br0620) 2021; 9 Althobaity, Tildesley (br0040) 2023; 13 br0160 Deming, Stephan (br0260) 1940; 11 Bhattacharya, Machi, Chen, Hoops, Lewis, Mortveit, Venkatramanan, Wilson, Marathe, Porebski (br0100) 2021 Medlock, Galvani (br0520) 2009; 325 Venkatramanan, Sadilek, Fadikar, Barrett, Biggerstaff, Chen (br0780) 2021; 12 Moore, Hill, Tildesley, Dyson, Keeling (br0560) 2021 Gillespie (br0350) 1976; 22 Bubar, Reinholt, Kissler, Lipsitch, Cobey, Grad, Larremore (br0140) 2021; 371 United States Censuc Bureau. 2011-2015 5-year ACS commuting flows, Last accessed: April 2020. Fjukstad, Bongo (br0330) 2017; 2 Jentsch, Anand, Bauch (br0420) 2021 Centers for Disease Control and Prevention (br0180) Microsoft (br0540) 2020 br0010 Centers for Disease Control and Prevention (br0190) 2020 br0370 Foster, Parkes, Zheng (br0340) 2020 U.S. Department of Transportation, Federal Highway Administration (br0750) Chen, Hoops, Marathe, Mortveit, Lewis, Venkatramanan, Haddadan, Bhattacharya, Adiga, Vullikanti, Srinivasan, Wilson, Ehrlich, Fenster, Eubank, Barrett, Marathe (br0210) 2022 de Miguel-Arribas (10.1016/j.jpdc.2024.104899_br0240) 2022; 22 Bhattacharya (10.1016/j.jpdc.2024.104899_br0100) 2021 Lemaitre (10.1016/j.jpdc.2024.104899_br0480) 2021 Hager (10.1016/j.jpdc.2024.104899_br0380) Sallam (10.1016/j.jpdc.2024.104899_br0620) 2021; 9 Perrault (10.1016/j.jpdc.2024.104899_br0590) 10.1016/j.jpdc.2024.104899_br0720 Barrett (10.1016/j.jpdc.2024.104899_br0070) 2009 Bubar (10.1016/j.jpdc.2024.104899_br0140) 2021; 371 Centers for Disease Control and Prevention (10.1016/j.jpdc.2024.104899_br0190) Lasser (10.1016/j.jpdc.2024.104899_br0450) 2022; 13 Virginia Department of Health (10.1016/j.jpdc.2024.104899_br0790) Ferguson (10.1016/j.jpdc.2024.104899_br0300) 2020 Moore (10.1016/j.jpdc.2024.104899_br0560) 2021 Arino (10.1016/j.jpdc.2024.104899_br0050) 2020; 5 Microsoft (10.1016/j.jpdc.2024.104899_br0540) 10.1016/j.jpdc.2024.104899_br0730 Chen (10.1016/j.jpdc.2024.104899_br0210) 2022 Zheng (10.1016/j.jpdc.2024.104899_br0830) 2018; 115 Fineberg (10.1016/j.jpdc.2024.104899_br0310) 2009 Lazebnik (10.1016/j.jpdc.2024.104899_br0470) 2022; 35 Mortveit (10.1016/j.jpdc.2024.104899_br0570) 2020 Bhattacharya (10.1016/j.jpdc.2024.104899_br0090) 2023; 37 Mutombo (10.1016/j.jpdc.2024.104899_br0580) 2022; 10 Borchering (10.1016/j.jpdc.2024.104899_br0120) 2021; 70 Venkatramanan (10.1016/j.jpdc.2024.104899_br0780) 2021; 12 Lum (10.1016/j.jpdc.2024.104899_br0490) 2016; 4 Spjuth (10.1016/j.jpdc.2024.104899_br0650) 2015; 10 Beckman (10.1016/j.jpdc.2024.104899_br0080) 1996; 30 Eubank (10.1016/j.jpdc.2024.104899_br0280) 2004; 429 10.1016/j.jpdc.2024.104899_br0690 Hogan (10.1016/j.jpdc.2024.104899_br0400) 2020 Lazarus (10.1016/j.jpdc.2024.104899_br0460) 2022; 13 Klonower (10.1016/j.jpdc.2024.104899_br0440) Walensky (10.1016/j.jpdc.2024.104899_br0800) Dror (10.1016/j.jpdc.2024.104899_br0270) 2020; 35 Cénat (10.1016/j.jpdc.2024.104899_br0170) 2023; 95 Collier (10.1016/j.jpdc.2024.104899_br0220) 2023 10.1016/j.jpdc.2024.104899_br0740 Wrigley-Field (10.1016/j.jpdc.2024.104899_br0810) 2021 Fitzpatrick (10.1016/j.jpdc.2024.104899_br0320) 2021; 371 Abell (10.1016/j.jpdc.2024.104899_br0020) 2022 Eubank (10.1016/j.jpdc.2024.104899_br0290) 2006; vol. 70 Centers for Disease Control and Prevention (10.1016/j.jpdc.2024.104899_br0180) Deming (10.1016/j.jpdc.2024.104899_br0260) 1940; 11 Chen (10.1016/j.jpdc.2024.104899_br0200) 2019 Swarup (10.1016/j.jpdc.2024.104899_br0660) 2017 Rao (10.1016/j.jpdc.2024.104899_br0610) 2016; 93 Fjukstad (10.1016/j.jpdc.2024.104899_br0330) 2017; 2 Acuña-Zegarra (10.1016/j.jpdc.2024.104899_br0030) 2021 Gillespie (10.1016/j.jpdc.2024.104899_br0350) 1976; 22 Sowa (10.1016/j.jpdc.2024.104899_br0640) 2021; 9 Buckner (10.1016/j.jpdc.2024.104899_br0150) 2021; 118 Borchering (10.1016/j.jpdc.2024.104899_br0110) 2023; 17 Machi (10.1016/j.jpdc.2024.104899_br0500) 2021 Jentsch (10.1016/j.jpdc.2024.104899_br0420) 2021 Merzky (10.1016/j.jpdc.2024.104899_br0530) 2021 Moghadas (10.1016/j.jpdc.2024.104899_br0550) 2021; 19 Goldstein (10.1016/j.jpdc.2024.104899_br0360) 2021; 118 Foster (10.1016/j.jpdc.2024.104899_br0340) Huberts (10.1016/j.jpdc.2024.104899_br0410) 2023; 305 Pertwee (10.1016/j.jpdc.2024.104899_br0600) 2022; 28 Venkatramanan (10.1016/j.jpdc.2024.104899_br0770) 2019; 15 Matrajt (10.1016/j.jpdc.2024.104899_br0510) 2010; 5 Troiano (10.1016/j.jpdc.2024.104899_br0710) 2021; 194 Medlock (10.1016/j.jpdc.2024.104899_br0520) 2009; 325 Babuji (10.1016/j.jpdc.2024.104899_br0060) 2019 Delmas (10.1016/j.jpdc.2024.104899_br0250) 2023; 86 U.S. Department of Transportation, Federal Highway Administration (10.1016/j.jpdc.2024.104899_br0750) Breiman (10.1016/j.jpdc.2024.104899_br0130) 1984 Torche (10.1016/j.jpdc.2024.104899_br0700) 2023; 120 Althobaity (10.1016/j.jpdc.2024.104899_br0040) 2023; 13 Zhao (10.1016/j.jpdc.2024.104899_br0820) 2020; 8 Kaplan (10.1016/j.jpdc.2024.104899_br0430) 2021; 118
References_xml	– ident: br0160 article-title: CDC/ATSDR social vulnerability index – volume: 325 start-page: 1705 year: 2009 end-page: 1708 ident: br0520 article-title: Optimizing influenza vaccine distribution publication-title: Science – ident: br0750 article-title: The national household travel survey (NHTS) – volume: 371 start-page: 916 year: 2021 end-page: 921 ident: br0140 article-title: Model-informed COVID-19 vaccine prioritization strategies by age and serostatus publication-title: Science – year: 2021 ident: br0560 article-title: Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study publication-title: Lancet Infect. Dis. – volume: 115 start-page: 20 year: 2018 end-page: 28 ident: br0830 article-title: Interplay between sir-based disease spreading and awareness diffusion on multiplex networks publication-title: J. Parallel Distrib. Comput. – year: 2019 ident: br0060 article-title: Parsl: Pervasive parallel programming in python publication-title: Proceedings of the 28th ACM International Symposium on High-Performance Parallel and Distributed Computing – volume: 4 start-page: 41 year: 2016 end-page: 56 ident: br0490 article-title: A two-stage, fitted values approach to activity matching publication-title: Int. J. Transp. – year: Nov. 2021 ident: br0530 article-title: Design and performance characterization of radical-pilot on leadership-class platforms – year: 2021 ident: br0480 article-title: Optimizing the spatio-temporal allocation of COVID-19 vaccines: Italy as a case study publication-title: medRxiv – year: 2021 ident: br0810 article-title: Geographically-targeted COVID-19 vaccination is more equitable than age-based thresholds alone publication-title: medRxiv – year: 1984 ident: br0130 article-title: Classification and Regression Trees – volume: 305 start-page: 1366 year: 2023 end-page: 1389 ident: br0410 article-title: Optimal timing of non-pharmaceutical interventions during an epidemic publication-title: Eur. J. Oper. Res. – year: 2020 ident: br0680 article-title: The New York times. Coronavirus (covid-19) data in the United States – volume: 118 year: 2021 ident: br0430 article-title: Influence of a COVID-19 vaccine's effectiveness and safety profile on vaccination acceptance publication-title: Proc. Natl. Acad. Sci. – start-page: 868 year: 2023 end-page: 877 ident: br0220 article-title: Developing distributed high-performance computing capabilities of an open science platform for robust epidemic analysis publication-title: 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) – year: 2020 ident: br0400 article-title: Report 33: Modelling the allocation and impact of a COVID-19 vaccine – volume: 19 year: 2021 ident: br0550 article-title: Evaluation of COVID-19 vaccination strategies with a delayed second dose publication-title: PLoS Biol. – ident: br0760 article-title: COVID-19 vaccine incentives – reference: United States Censuc Bureau. American Community Survey 2013-2017 5-year estimates, Last accessed: February 2020. – start-page: 4675 year: 2022 end-page: 4683 ident: br0210 article-title: Effective social network-based allocation of COVID-19 vaccines publication-title: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining – year: 2021 ident: br0420 article-title: Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study publication-title: Lancet Infect. Dis. – volume: 30 start-page: 415 year: 1996 end-page: 429 ident: br0080 article-title: Creating synthetic baseline populations publication-title: Transp. Res., Part A, Policy Pract. – start-page: 1003 year: 2009 end-page: 1014 ident: br0070 article-title: Generation and analysis of large synthetic social contact networks publication-title: Proceedings of the 2009 Winter Simulation Conference (WSC) – year: 2020 ident: br0540 article-title: U.S. building footprints – start-page: 1566 year: 2021 end-page: 1574 ident: br0100 article-title: AI-driven agent-based models to study the role of vaccine acceptance in controlling COVID-19 spread in the US publication-title: 2021 IEEE International Conference on Big Data (Big Data) – volume: 429 start-page: 180 year: 2004 end-page: 184 ident: br0280 article-title: Modelling disease outbreaks in realistic urban social networks publication-title: Nature – volume: 5 start-page: 309 year: 2020 end-page: 315 ident: br0050 article-title: A simple model for COVID-19 publication-title: Infect. Dis. Model. – year: 2020 ident: br0300 article-title: Report 9: Impact of non-pharmaceutical interventions, (NPIs) to reduce COVID19 mortality and healthcare demand – volume: 5 year: 2010 ident: br0510 article-title: Optimizing vaccine allocation at different points in time during an epidemic publication-title: PLoS ONE – year: 2020 ident: br0570 article-title: Synthetic populations and interaction networks for the U.S. – year: 2021 ident: br0030 article-title: COVID-19 optimal vaccination policies: a modeling study on efficacy, natural and vaccine-induced immunity responses publication-title: Math. Biosci. – ident: br0630 article-title: COVID-19 scenario modeling hub – volume: 22 start-page: 1 year: 2022 end-page: 12 ident: br0240 article-title: Impact of vaccine hesitancy on secondary COVID-19 outbreaks in the US: an age-structured SIR model publication-title: BMC Infect. Dis. – volume: 95 year: 2023 ident: br0170 article-title: Prevalence and factors related to COVID-19 vaccine hesitancy and unwillingness in Canada: a systematic review and meta-analysis publication-title: J. Med. Virol. – ident: br0180 article-title: Trends in number of COVID-19 vaccinations in the US – ident: br0790 article-title: UVA COVID-19 modeling weekly update – year: 2020 ident: br0390 – ident: br0230 article-title: Covidcast: vaccine acceptance summary – ident: br0440 article-title: AMD EPYC advanced user training on expanse – volume: 9 start-page: 876 year: 2021 ident: br0640 article-title: COVID-19 vaccine hesitancy in Poland – multifactorial impact trajectories publication-title: Vaccines – volume: 2 start-page: 245 year: 2017 end-page: 251 ident: br0330 article-title: A review of scalable bioinformatics pipelines publication-title: Data Sci. Eng. – start-page: 639 year: 2021 end-page: 650 ident: br0500 article-title: Scalable epidemiological workflows to support COVID-19 planning and response publication-title: 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) – year: 2009 ident: br0310 article-title: Epidemic science in real time – volume: 194 start-page: 245 year: 2021 end-page: 251 ident: br0710 article-title: Vaccine hesitancy in the era of COVID-19 publication-title: Publ. Health – volume: 13 start-page: 843 year: 2023 ident: br0040 article-title: Modelling the impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia publication-title: Sci. Rep. – ident: br0370 article-title: COVID-19 pandemic response – year: 2019 ident: br0380 article-title: Artificial intelligence for social good – year: 2019 ident: br0590 article-title: AI for social impact: learning and planning in the data-to-deployment pipeline – volume: 93 start-page: 102 year: 2016 end-page: 119 ident: br0610 article-title: Efficient parallel simulation of spatially-explicit agent-based epidemiological models publication-title: J. Parallel Distrib. Comput. – volume: vol. 70 start-page: 179 year: 2006 end-page: 200 ident: br0290 article-title: Structure of Social Contact Networks and Their Impact on Epidemics publication-title: Discrete Methods in Epidemiology – volume: 11 start-page: 427 year: 1940 end-page: 444 ident: br0260 article-title: On a least squares adjustment of a sampled frequency table when the expected marginal tables are known publication-title: Ann. Math. Stat. – volume: 120 year: 2023 ident: br0700 article-title: Vaccination, immunity, and the changing impact of COVID-19 on infant health publication-title: Proc. Natl. Acad. Sci. – reference: United States Department of Labor, Bureau of Labor Statistics. The American Time Use Survey (ATUS), Last accessed: February 2020. – volume: 118 year: 2021 ident: br0360 article-title: Vaccinating the oldest against covid-19 saves both the most lives and most years of life publication-title: Proc. Natl. Acad. Sci. – ident: br0670 article-title: The National Center for Education Statistics (NCES) – year: 2022 ident: br0020 article-title: Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation publication-title: medRxiv – volume: 17 year: 2023 ident: br0110 article-title: Impact of SARS-CoV-2 vaccination of children ages 5–11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021–March 2022: a multi-model study publication-title: Lancet Reg. Health Am. – volume: 35 start-page: 1833 year: 2022 end-page: 1861 ident: br0470 article-title: Spatio-temporal influence of non-pharmaceutical interventions policies on pandemic dynamics and the economy: the case of COVID-19 publication-title: Econ. Res.-Ekon. Istraž. – volume: 70 start-page: 719 year: 2021 end-page: 724 ident: br0120 article-title: Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios–United States, April–September 2021 publication-title: MMWR Morb. Mort. Wkly. Rep. – ident: br0010 article-title: RSV scenario modeling hub – volume: 15 year: 2019 ident: br0770 article-title: Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints publication-title: PLoS Comput. Biol. – volume: 371 start-page: 890 year: 2021 end-page: 891 ident: br0320 article-title: Optimizing age-specific vaccination publication-title: Science – year: 2017 ident: br0660 article-title: Generating synthetic populations for social modeling publication-title: Tutorial at the Sixteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS) – volume: 86 start-page: 26 year: 2023 ident: br0250 article-title: Optimal vaccination: various (counter) intuitive examples publication-title: J. Math. Biol. – volume: 35 start-page: 775 year: 2020 end-page: 779 ident: br0270 article-title: Vaccine hesitancy: the next challenge in the fight against covid-19 publication-title: Eur. J. Epidemiol. – volume: 37 start-page: 4 year: 2023 end-page: 27 ident: br0090 article-title: Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support publication-title: Int. J. High Perform. Comput. Appl. – reference: United States Censuc Bureau. 2011-2015 5-year ACS commuting flows, Last accessed: April 2020. – volume: 13 start-page: 554 year: 2022 ident: br0450 article-title: Assessing the impact of SARS-CoV-2 prevention measures in Austrian schools using agent-based simulations and cluster tracing data publication-title: Nat. Commun. – year: 2019 ident: br0200 article-title: EpiHiper: Modeling and implementation – volume: 28 start-page: 456 year: 2022 end-page: 459 ident: br0600 article-title: An epidemic of uncertainty: rumors, conspiracy theories and vaccine hesitancy publication-title: Nat. Med. – year: 2021 ident: br0800 article-title: Press briefing by White House COVID-19 response team and public health officials – volume: 8 start-page: 11 year: 2020 end-page: 19 ident: br0820 article-title: Modeling the epidemic dynamics and control of COVID-19 outbreak in China publication-title: Quant. Biol. – volume: 22 start-page: 403 year: 1976 end-page: 434 ident: br0350 article-title: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions publication-title: J. Comput. Phys. – volume: 9 start-page: 160 year: 2021 ident: br0620 article-title: COVID-19 vaccine hesitancy worldwide: a concise systematic review of vaccine acceptance rates publication-title: Vaccines – volume: 10 start-page: 1 year: 2015 end-page: 12 ident: br0650 article-title: Experiences with workflows for automating data-intensive bioinformatics publication-title: Biol. Direct – volume: 12 start-page: 726 year: 2021 ident: br0780 article-title: Forecasting influenza activity using machine-learned mobility map publication-title: Nat. Commun. – reference: The University of Oxford. The multinational time use study (MTUS), Last accessed: February 2020. – volume: 118 year: 2021 ident: br0150 article-title: Dynamic prioritization of COVID-19 vaccines when social distancing is limited for essential workers publication-title: Proc. Natl. Acad. Sci. – year: 2020 ident: br0340 article-title: The rise of AI-driven simulators: building a new crystal ball – year: 2020 ident: br0190 article-title: COVID-19 pandemic planning scenarios – volume: 13 start-page: 3801 year: 2022 ident: br0460 article-title: Revisiting COVID-19 vaccine hesitancy around the world using data from 23 countries in 2021 publication-title: Nat. Commun. – volume: 10 start-page: e320 year: 2022 end-page: e321 ident: br0580 article-title: COVID-19 vaccine hesitancy in Africa: a call to action publication-title: Lancet Glob. Health – ident: 10.1016/j.jpdc.2024.104899_br0380 – volume: 325 start-page: 1705 issue: 5948 year: 2009 ident: 10.1016/j.jpdc.2024.104899_br0520 article-title: Optimizing influenza vaccine distribution publication-title: Science doi: 10.1126/science.1175570 – volume: 305 start-page: 1366 issue: 3 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0410 article-title: Optimal timing of non-pharmaceutical interventions during an epidemic publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2022.06.034 – volume: 9 start-page: 876 issue: 8 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0640 article-title: COVID-19 vaccine hesitancy in Poland – multifactorial impact trajectories publication-title: Vaccines doi: 10.3390/vaccines9080876 – year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0530 – ident: 10.1016/j.jpdc.2024.104899_br0750 – start-page: 868 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0220 article-title: Developing distributed high-performance computing capabilities of an open science platform for robust epidemic analysis – year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0560 article-title: Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study publication-title: Lancet Infect. Dis. doi: 10.1016/S1473-3099(21)00143-2 – volume: 194 start-page: 245 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0710 article-title: Vaccine hesitancy in the era of COVID-19 publication-title: Publ. Health doi: 10.1016/j.puhe.2021.02.025 – volume: 28 start-page: 456 issue: 3 year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0600 article-title: An epidemic of uncertainty: rumors, conspiracy theories and vaccine hesitancy publication-title: Nat. Med. doi: 10.1038/s41591-022-01728-z – volume: 19 issue: 4 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0550 article-title: Evaluation of COVID-19 vaccination strategies with a delayed second dose publication-title: PLoS Biol. doi: 10.1371/journal.pbio.3001211 – volume: 120 issue: 49 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0700 article-title: Vaccination, immunity, and the changing impact of COVID-19 on infant health publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.2311573120 – ident: 10.1016/j.jpdc.2024.104899_br0340 – ident: 10.1016/j.jpdc.2024.104899_br0590 – volume: 118 issue: 11 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0360 article-title: Vaccinating the oldest against covid-19 saves both the most lives and most years of life publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.2026322118 – year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0420 article-title: Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study publication-title: Lancet Infect. Dis. doi: 10.1016/S1473-3099(21)00057-8 – year: 2009 ident: 10.1016/j.jpdc.2024.104899_br0310 – ident: 10.1016/j.jpdc.2024.104899_br0690 – volume: 371 start-page: 916 issue: 6532 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0140 article-title: Model-informed COVID-19 vaccine prioritization strategies by age and serostatus publication-title: Science doi: 10.1126/science.abe6959 – ident: 10.1016/j.jpdc.2024.104899_br0190 – year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0030 article-title: COVID-19 optimal vaccination policies: a modeling study on efficacy, natural and vaccine-induced immunity responses publication-title: Math. Biosci. doi: 10.1016/j.mbs.2021.108614 – volume: 13 start-page: 843 issue: 1 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0040 article-title: Modelling the impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia publication-title: Sci. Rep. doi: 10.1038/s41598-022-26468-5 – year: 2019 ident: 10.1016/j.jpdc.2024.104899_br0200 – ident: 10.1016/j.jpdc.2024.104899_br0180 – volume: 371 start-page: 890 issue: 6532 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0320 article-title: Optimizing age-specific vaccination publication-title: Science doi: 10.1126/science.abg2334 – volume: 10 start-page: e320 issue: 3 year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0580 article-title: COVID-19 vaccine hesitancy in Africa: a call to action publication-title: Lancet Glob. Health doi: 10.1016/S2214-109X(21)00563-5 – year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0810 article-title: Geographically-targeted COVID-19 vaccination is more equitable than age-based thresholds alone publication-title: medRxiv – year: 1984 ident: 10.1016/j.jpdc.2024.104899_br0130 – year: 2017 ident: 10.1016/j.jpdc.2024.104899_br0660 article-title: Generating synthetic populations for social modeling – volume: 115 start-page: 20 year: 2018 ident: 10.1016/j.jpdc.2024.104899_br0830 article-title: Interplay between sir-based disease spreading and awareness diffusion on multiplex networks publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2018.01.001 – volume: 12 start-page: 726 issue: 1 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0780 article-title: Forecasting influenza activity using machine-learned mobility map publication-title: Nat. Commun. doi: 10.1038/s41467-021-21018-5 – volume: 10 start-page: 1 issue: 1 year: 2015 ident: 10.1016/j.jpdc.2024.104899_br0650 article-title: Experiences with workflows for automating data-intensive bioinformatics publication-title: Biol. Direct doi: 10.1186/s13062-015-0071-8 – volume: 37 start-page: 4 issue: 1 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0090 article-title: Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support publication-title: Int. J. High Perform. Comput. Appl. doi: 10.1177/10943420221127034 – volume: 5 issue: 11 year: 2010 ident: 10.1016/j.jpdc.2024.104899_br0510 article-title: Optimizing vaccine allocation at different points in time during an epidemic publication-title: PLoS ONE doi: 10.1371/journal.pone.0013767 – start-page: 1566 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0100 article-title: AI-driven agent-based models to study the role of vaccine acceptance in controlling COVID-19 spread in the US – volume: 35 start-page: 1833 issue: 1 year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0470 article-title: Spatio-temporal influence of non-pharmaceutical interventions policies on pandemic dynamics and the economy: the case of COVID-19 publication-title: Econ. Res.-Ekon. Istraž. – volume: 93 start-page: 102 year: 2016 ident: 10.1016/j.jpdc.2024.104899_br0610 article-title: Efficient parallel simulation of spatially-explicit agent-based epidemiological models publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2016.04.004 – volume: 118 issue: 16 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0150 article-title: Dynamic prioritization of COVID-19 vaccines when social distancing is limited for essential workers publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.2025786118 – ident: 10.1016/j.jpdc.2024.104899_br0440 – year: 2019 ident: 10.1016/j.jpdc.2024.104899_br0060 article-title: Parsl: Pervasive parallel programming in python – volume: 2 start-page: 245 issue: 3 year: 2017 ident: 10.1016/j.jpdc.2024.104899_br0330 article-title: A review of scalable bioinformatics pipelines publication-title: Data Sci. Eng. doi: 10.1007/s41019-017-0047-z – year: 2020 ident: 10.1016/j.jpdc.2024.104899_br0400 – volume: 70 start-page: 719 issue: 19 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0120 article-title: Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios–United States, April–September 2021 publication-title: MMWR Morb. Mort. Wkly. Rep. doi: 10.15585/mmwr.mm7019e3 – volume: 86 start-page: 26 issue: 2 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0250 article-title: Optimal vaccination: various (counter) intuitive examples publication-title: J. Math. Biol. doi: 10.1007/s00285-022-01858-5 – ident: 10.1016/j.jpdc.2024.104899_br0730 – year: 2020 ident: 10.1016/j.jpdc.2024.104899_br0570 – ident: 10.1016/j.jpdc.2024.104899_br0720 – volume: 4 start-page: 41 year: 2016 ident: 10.1016/j.jpdc.2024.104899_br0490 article-title: A two-stage, fitted values approach to activity matching publication-title: Int. J. Transp. doi: 10.14257/ijt.2016.4.1.03 – volume: 30 start-page: 415 issue: 6 year: 1996 ident: 10.1016/j.jpdc.2024.104899_br0080 article-title: Creating synthetic baseline populations publication-title: Transp. Res., Part A, Policy Pract. doi: 10.1016/0965-8564(96)00004-3 – volume: 35 start-page: 775 issue: 8 year: 2020 ident: 10.1016/j.jpdc.2024.104899_br0270 article-title: Vaccine hesitancy: the next challenge in the fight against covid-19 publication-title: Eur. J. Epidemiol. doi: 10.1007/s10654-020-00671-y – ident: 10.1016/j.jpdc.2024.104899_br0800 – volume: 13 start-page: 554 issue: 1 year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0450 article-title: Assessing the impact of SARS-CoV-2 prevention measures in Austrian schools using agent-based simulations and cluster tracing data publication-title: Nat. Commun. doi: 10.1038/s41467-022-28170-6 – volume: 22 start-page: 1 issue: 1 year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0240 article-title: Impact of vaccine hesitancy on secondary COVID-19 outbreaks in the US: an age-structured SIR model publication-title: BMC Infect. Dis. doi: 10.1186/s12879-022-07486-0 – year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0480 article-title: Optimizing the spatio-temporal allocation of COVID-19 vaccines: Italy as a case study publication-title: medRxiv – start-page: 639 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0500 article-title: Scalable epidemiological workflows to support COVID-19 planning and response – volume: 11 start-page: 427 issue: 4 year: 1940 ident: 10.1016/j.jpdc.2024.104899_br0260 article-title: On a least squares adjustment of a sampled frequency table when the expected marginal tables are known publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177731829 – ident: 10.1016/j.jpdc.2024.104899_br0740 – volume: 429 start-page: 180 year: 2004 ident: 10.1016/j.jpdc.2024.104899_br0280 article-title: Modelling disease outbreaks in realistic urban social networks publication-title: Nature doi: 10.1038/nature02541 – volume: 17 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0110 article-title: Impact of SARS-CoV-2 vaccination of children ages 5–11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021–March 2022: a multi-model study publication-title: Lancet Reg. Health Am. – volume: 5 start-page: 309 year: 2020 ident: 10.1016/j.jpdc.2024.104899_br0050 article-title: A simple model for COVID-19 publication-title: Infect. Dis. Model. – volume: vol. 70 start-page: 179 year: 2006 ident: 10.1016/j.jpdc.2024.104899_br0290 article-title: Structure of Social Contact Networks and Their Impact on Epidemics – start-page: 4675 year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0210 article-title: Effective social network-based allocation of COVID-19 vaccines – volume: 15 issue: 9 year: 2019 ident: 10.1016/j.jpdc.2024.104899_br0770 article-title: Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1007111 – volume: 22 start-page: 403 year: 1976 ident: 10.1016/j.jpdc.2024.104899_br0350 article-title: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions publication-title: J. Comput. Phys. doi: 10.1016/0021-9991(76)90041-3 – volume: 95 issue: 1 year: 2023 ident: 10.1016/j.jpdc.2024.104899_br0170 article-title: Prevalence and factors related to COVID-19 vaccine hesitancy and unwillingness in Canada: a systematic review and meta-analysis publication-title: J. Med. Virol. doi: 10.1002/jmv.28156 – volume: 13 start-page: 3801 issue: 1 year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0460 article-title: Revisiting COVID-19 vaccine hesitancy around the world using data from 23 countries in 2021 publication-title: Nat. Commun. doi: 10.1038/s41467-022-31441-x – ident: 10.1016/j.jpdc.2024.104899_br0540 – volume: 9 start-page: 160 issue: 2 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0620 article-title: COVID-19 vaccine hesitancy worldwide: a concise systematic review of vaccine acceptance rates publication-title: Vaccines doi: 10.3390/vaccines9020160 – year: 2022 ident: 10.1016/j.jpdc.2024.104899_br0020 article-title: Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation publication-title: medRxiv – start-page: 1003 year: 2009 ident: 10.1016/j.jpdc.2024.104899_br0070 article-title: Generation and analysis of large synthetic social contact networks – volume: 8 start-page: 11 year: 2020 ident: 10.1016/j.jpdc.2024.104899_br0820 article-title: Modeling the epidemic dynamics and control of COVID-19 outbreak in China publication-title: Quant. Biol. doi: 10.1007/s40484-020-0199-0 – volume: 118 issue: 10 year: 2021 ident: 10.1016/j.jpdc.2024.104899_br0430 article-title: Influence of a COVID-19 vaccine's effectiveness and safety profile on vaccination acceptance publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.2021726118 – ident: 10.1016/j.jpdc.2024.104899_br0790 – year: 2020 ident: 10.1016/j.jpdc.2024.104899_br0300
SSID	ssj0011578
Score	2.41696
Snippet	We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic...
SourceID	pubmedcentral proquest pubmed crossref elsevier
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	104899
SubjectTerms	Agent-based modeling and simulation Covid-19 vaccine acceptance High performance computing workflow
Title	Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread
URI	https://dx.doi.org/10.1016/j.jpdc.2024.104899 https://www.ncbi.nlm.nih.gov/pubmed/38774820 https://www.proquest.com/docview/3058638593 https://pubmed.ncbi.nlm.nih.gov/PMC11105799
Volume	191
WOSCitedRecordID	wos001265739700001&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: 1096-0848 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011578 issn: 0743-7315 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtZ1bb9MwFICtrkNoL9wv5TIZibcoqLnbvE1laENsVGJMfYvcxKGrQhKtSRn8En4Nv41zYjttNzEBEi9plauV8znn2D4XQl5yXzgikYHti5DZWHgb-xy3uZtiYgAnydrV89P30fExm0z4uNf7aWJhlnlUFOziglf_VdSwD4SNobN_Ie7uprAD_oPQYQtih-0fCf64XMpcOQraSd5gIoS2IEyWl1914mY0OBdNhaa3BUZjbmOFeetgPLJlG0qVWlIVjk0srSFx4mAtJYcOsVqFWC5Fgiv0FvzIqm7DEACr0YfTwze2w61FBY_ZKAm6Zghj9vE8lypnQYqJfLEGl2zD7aqmNroV5wxmoq4FBop9E8r8BU1bi6pTLUfoGtp-RrFIWcf9yISgQF_4_H3WlB3MZakWQj7WMtP9RE-BuH7n42W-lJhnNfJUXGj3WVdVwK6oCDVbMX81r1JMYen6uMzNVJWmNTyqLy0fHgP7mLnDlbrsnBjHRyNQFBjMy7fIthsFnPXJ9t7h_uRdt47lBMoWMA3UYVvKw_ByC3bITfO431lJV0dBl51516yjkzvklpYm3VM43iU9Wdwjt03JEKo1yH3yo6WTbtBJDZ1U0Unrkmo6aUcnXaOTGjqppvM13WCTAptUsUnLjGo26YpNWhbUsEkVmw_Ip7f7J6MDWxcHsRPfcWs7dKXkThIFWchcPw1c6WQJhuYNo6mf8URGU3glkStECCMiJ4PjMPaRXhoOhXSn3HtI-kVZyMeEDlMvSdNQcMF9f5q5YEFPWRbwNILRzJTxAXGMMOJEZ87HAi55bFwk5zHKMkZZxkqWA2J111Qqb8y1ZwdGxrF-c8qijYHaa697YYCIQS3gWp8oZNksYlDjDFRrwL0BeaQA6dphIBsQtoFOdwKmnN88UpzN2tTzBvgn_37pU7Kz6sPPSL8-b-RzciNZ1meL812yFU3Yru5GvwCUYATP
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=Novel+multi-cluster+workflow+system+to+support+real-time+HPC-enabled+epidemic+science%3A+Investigating+the+impact+of+vaccine+acceptance+on+COVID-19+spread&rft.jtitle=Journal+of+parallel+and+distributed+computing&rft.au=Bhattacharya%2C+Parantapa&rft.au=Machi%2C+Dustin&rft.au=Chen%2C+Jiangzhuo&rft.au=Hoops%2C+Stefan&rft.date=2024-09-01&rft.issn=0743-7315&rft.volume=191&rft_id=info:doi/10.1016%2Fj.jpdc.2024.104899&rft_id=info%3Apmid%2F38774820&rft.externalDocID=PMC11105799
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0743-7315&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0743-7315&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0743-7315&client=summon