Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study

Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. Methods We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller...

Full description

Saved in:
Bibliographic Details
Published in:BMC health services research Vol. 22; no. 1; pp. 1 - 1059
Main Authors: Hanly, Mark, Churches, Tim, Fitzgerald, Oisín, Caterson, Ian, MacIntyre, Chandini Raina, Jorm, Louisa
Format: Journal Article
Language:English
Published: London BioMed Central Ltd 19-08-2022
BioMed Central
BMC
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. Methods We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. Results Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. Conclusions With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery. Keywords: Stochastic network models, Queues, Health services research, Vaccination, COVID-19
AbstractList BACKGROUNDCOVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. METHODSWe use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. RESULTSBased on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. CONCLUSIONSWith the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.
COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.
Abstract Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. Methods We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. Results Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. Conclusions With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.
Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. Methods We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. Results Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. Conclusions With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery. Keywords: Stochastic network models, Queues, Health services research, Vaccination, COVID-19
Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. Methods We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. Results Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. Conclusions With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.
ArticleNumber 1059
Audience Academic
Author Churches, Tim
Jorm, Louisa
Hanly, Mark
Caterson, Ian
Fitzgerald, Oisín
MacIntyre, Chandini Raina
Author_xml – sequence: 1
  givenname: Mark
  surname: Hanly
  fullname: Hanly, Mark
– sequence: 2
  givenname: Tim
  surname: Churches
  fullname: Churches, Tim
– sequence: 3
  givenname: Oisín
  surname: Fitzgerald
  fullname: Fitzgerald, Oisín
– sequence: 4
  givenname: Ian
  surname: Caterson
  fullname: Caterson, Ian
– sequence: 5
  givenname: Chandini Raina
  surname: MacIntyre
  fullname: MacIntyre, Chandini Raina
– sequence: 6
  givenname: Louisa
  surname: Jorm
  fullname: Jorm, Louisa
BookMark eNptkkuLFDEQxxtZcR_6BTw1ePHSayqPTuJBWBYfCyte9Bxq8pjJ0J2Mne6F-fZmdhZ1RHJIqPrXr1LF_7I5Szn5pnkN5BpA9e8KUA2sI5R2RHEuO_WsuQAuadfrnp399T5vLkvZEgJSUfmiOWdCq55RetFsvmbnhyGmdfuA1saEc8yptbhDG-d9i3M7Yiknyc2yKi0m16598hMO7W5CO0frW1tB0Zb3LbYljstw1Jd5cfuXzfOAQ_Gvnu6r5senj99vv3T33z7f3d7cd1ZwMneogCNzNqDtVysBPTLoIVAUCETw3guiKErmAVGo4ILogw4SCKFAgDN21dwduS7j1uymOOK0NxmjeQzkaW1wqp8dvJHeCWJR95ZTLhgoETS44HQAD4KKyvpwZO2W1eid9Wmu455ATzMpbsw6PxjNlNJKVsDbJ8CUfy6-zGaMxdZ9Y_J5KYZKwlUvqaJV-uYf6TYvU6qrOqiY1lIw_Ue1xjpATCHXvvYANTcSqge4Uqqqrv-jqsf5MdpqohBr_KSAHgvslEuZfPg9IxBz8Jo5es1Ur5lHrxnFfgGIO8bA
CitedBy_id crossref_primary_10_1080_20476965_2024_2339817
crossref_primary_10_31631_2073_3046_2023_22_4_95_105
crossref_primary_10_1089_hs_2022_0100
crossref_primary_10_1186_s13104_024_06736_5
crossref_primary_10_1016_j_jvacx_2024_100524
Cites_doi 10.1186/s12911-016-0390-4
10.1016/j.cie.2014.07.022
10.1287/opre.29.1.1
10.1101/2021.03.24.21253517
10.1136/bmj.n421
10.1016/j.vaccine.2021.05.024
10.21105/joss.01686
10.1007/BF01158785
10.1007/s43069-021-00114-8
10.18637/jss.v095.i05
10.1016/S1473-3099(21)00079-7
10.1136/bmjopen-2016-015007
10.1287/opre.9.3.383
10.5694/mja2.50845
10.1109/WSC.2007.4419734
10.1016/S0927-0507(05)80094-5
10.1136/bmj.n1088
10.1177/003754977502400302
10.1016/j.cie.2015.02.018
10.1016/j.ejor.2011.05.026
10.1016/j.lanwpc.2021.100224
10.1101/2021.08.08.21261768
10.1007/s10729-010-9143-6
10.1016/S0140-6736(21)02046-8
10.15585/mmwr.mm7013e3
10.1007/s10729-016-9363-5
10.1038/s41562-021-01122-8
10.1109/TSMC.1977.4309624
10.5694/mja2.51263
10.1007/s40273-017-0523-3
10.1056/NEJMoa2114255
10.1007/s10916-017-0711-x
10.1136/bmj.m1090
10.1056/NEJMp2102535
10.3390/healthcare8040469
10.1136/bmj.n2082
10.1007/978-0-8176-8421-1
10.1136/bmjoq-2021-001525
10.1136/bmj.n292
10.5694/mja2.51291
10.1056/NEJMoa2035389
10.1287/inte.2020.1063
10.1057/s41306-017-0024-9
10.1186/s12199-021-01018-z
10.1016/j.vaccine.2021.04.042
ContentType Journal Article
Copyright COPYRIGHT 2022 BioMed Central Ltd.
2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
The Author(s) 2022
Copyright_xml – notice: COPYRIGHT 2022 BioMed Central Ltd.
– notice: 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: The Author(s) 2022
DBID AAYXX
CITATION
3V.
7RV
7WY
7WZ
7X7
7XB
87Z
88C
88E
8FI
8FJ
8FK
8FL
ABUWG
AFKRA
AZQEC
BENPR
BEZIV
CCPQU
DWQXO
FRNLG
FYUFA
F~G
GHDGH
K60
K6~
K9.
KB0
L.-
M0C
M0S
M0T
M1P
NAPCQ
PIMPY
PQBIZ
PQBZA
PQEST
PQQKQ
PQUKI
Q9U
7X8
5PM
DOA
DOI 10.1186/s12913-022-08447-8
DatabaseName CrossRef
ProQuest Central (Corporate)
Proquest Nursing & Allied Health Source
ABI/INFORM Collection
ABI/INFORM Global (PDF only)
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
ABI/INFORM Collection
Healthcare Administration Database (Alumni)
Medical Database (Alumni Edition)
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ABI/INFORM Collection (Alumni Edition)
ProQuest Central (Alumni)
ProQuest Central
ProQuest Central Essentials
ProQuest Central
Business Premium Collection
ProQuest One Community College
ProQuest Central Korea
Business Premium Collection (Alumni)
Health Research Premium Collection
ABI/INFORM Global (Corporate)
Health Research Premium Collection (Alumni)
ProQuest Business Collection (Alumni Edition)
ProQuest Business Collection
ProQuest Health & Medical Complete (Alumni)
Nursing & Allied Health Database (Alumni Edition)
ABI/INFORM Professional Advanced
ABI/INFORM Global
Health & Medical Collection (Alumni Edition)
Healthcare Administration Database
PML(ProQuest Medical Library)
Nursing & Allied Health Premium
Publicly Available Content Database
One Business
ProQuest One Business (Alumni)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central Basic
MEDLINE - Academic
PubMed Central (Full Participant titles)
Directory of Open Access Journals
DatabaseTitle CrossRef
Publicly Available Content Database
ABI/INFORM Global (Corporate)
ProQuest Business Collection (Alumni Edition)
ProQuest One Business
ProQuest Central Essentials
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ABI/INFORM Complete
ProQuest Central
ABI/INFORM Professional Advanced
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
ProQuest Medical Library (Alumni)
ABI/INFORM Complete (Alumni Edition)
Business Premium Collection
ABI/INFORM Global
ABI/INFORM Global (Alumni Edition)
ProQuest Central Basic
ProQuest One Academic Eastern Edition
ProQuest Health Management
ProQuest Nursing & Allied Health Source
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Business Collection
ProQuest Hospital Collection (Alumni)
Nursing & Allied Health Premium
ProQuest Health & Medical Complete
ProQuest Medical Library
ProQuest One Academic UKI Edition
ProQuest Health Management (Alumni Edition)
ProQuest One Business (Alumni)
ProQuest Nursing & Allied Health Source (Alumni)
ProQuest One Academic
ProQuest Central (Alumni)
Business Premium Collection (Alumni)
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic



Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: Directory of Open Access Journals
  url: http://www.doaj.org/
  sourceTypes: Open Website
DeliveryMethod fulltext_linktorsrc
Discipline Public Health
EISSN 1472-6963
EndPage 1059
ExternalDocumentID oai_doaj_org_article_7ed50ca96c42453185f91dfd9f1e1525
A714474888
10_1186_s12913_022_08447_8
GeographicLocations Australia
GeographicLocations_xml – name: Australia
GrantInformation_xml – fundername: ;
GroupedDBID ---
-A0
0R~
23N
2WC
3V.
44B
53G
5VS
6J9
6PF
7RV
7WY
7X7
88E
8FI
8FJ
8FL
AAFWJ
AAJSJ
AAWTL
AAYXX
ABDBF
ABUWG
ACGFO
ACGFS
ACIHN
ACRMQ
ADBBV
ADINQ
ADUKV
AEAQA
AENEX
AFKRA
AFPKN
AHBYD
AHMBA
AHYZX
ALIPV
ALMA_UNASSIGNED_HOLDINGS
AMKLP
AMTXH
AOIJS
BAPOH
BAWUL
BCNDV
BENPR
BEZIV
BFQNJ
BMC
BPHCQ
BVXVI
C24
C6C
CCPQU
CITATION
CS3
DIK
DU5
DWQXO
E3Z
EAD
EAP
EAS
EBD
EBLON
EBS
EMB
EMK
EMOBN
ESX
F5P
FRNLG
FYUFA
GROUPED_DOAJ
GX1
HMCUK
IAO
IHR
INH
INR
ITC
K60
K6~
KQ8
M0C
M0T
M1P
M48
M~E
NAPCQ
O5R
O5S
OK1
P2P
PGMZT
PIMPY
PQBIZ
PQBZA
PQQKQ
PROAC
PSQYO
RBZ
RNS
ROL
RPM
RSV
SMD
SOJ
SV3
TR2
TUS
UKHRP
W2D
WOQ
WOW
XSB
7XB
8FK
AZQEC
K9.
L.-
PQEST
PQUKI
Q9U
7X8
5PM
ID FETCH-LOGICAL-c540t-a814a3dcfac6bb516a3161f2a5a10546e5082a73e1aa58fdf56f9f71002101433
IEDL.DBID RPM
ISSN 1472-6963
IngestDate Tue Oct 22 15:16:01 EDT 2024
Tue Sep 17 21:18:19 EDT 2024
Fri Oct 25 03:19:26 EDT 2024
Thu Oct 10 14:48:58 EDT 2024
Tue Nov 19 21:16:56 EST 2024
Tue Nov 12 22:50:53 EST 2024
Thu Nov 21 23:19:40 EST 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c540t-a814a3dcfac6bb516a3161f2a5a10546e5082a73e1aa58fdf56f9f71002101433
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388987/
PMID 35986322
PQID 2703997539
PQPubID 44821
ParticipantIDs doaj_primary_oai_doaj_org_article_7ed50ca96c42453185f91dfd9f1e1525
pubmedcentral_primary_oai_pubmedcentral_nih_gov_9388987
proquest_miscellaneous_2704867282
proquest_journals_2703997539
gale_infotracmisc_A714474888
gale_infotracacademiconefile_A714474888
crossref_primary_10_1186_s12913_022_08447_8
PublicationCentury 2000
PublicationDate 2022-08-19
PublicationDateYYYYMMDD 2022-08-19
PublicationDate_xml – month: 08
  year: 2022
  text: 2022-08-19
  day: 19
PublicationDecade 2020
PublicationPlace London
PublicationPlace_xml – name: London
PublicationTitle BMC health services research
PublicationYear 2022
Publisher BioMed Central Ltd
BioMed Central
BMC
Publisher_xml – name: BioMed Central Ltd
– name: BioMed Central
– name: BMC
References FG Sandmann (8447_CR4) 2021; 21
Australian Government (8447_CR52) 2022
L Eaton (8447_CR12) 2021; 372
A Elalouf (8447_CR38) 2022; 3
TA Casadonte (8447_CR21) 2021; 6
F Rico (8447_CR41) 2007
JL Bernal (8447_CR3) 2021; 373
J Beliën (8447_CR40) 2012; 217
N Scott (8447_CR8) 2021; 214
E Mahase (8447_CR15) 2021; 374
C Baraniuk (8447_CR17) 2021; 372
8447_CR6
8447_CR5
RM Wood (8447_CR23) 2021; 39
M Kang (8447_CR55) 2020; 21
A Ebert (8447_CR48) 2020; 95
S Mohiuddin (8447_CR36) 2017; 7
8447_CR24
8447_CR10
8447_CR11
R Core Team. R (8447_CR50) 2020
JC Hershey (8447_CR27) 1981; 29
SL Albin (8447_CR28) 1990; 7
UN Bhat (8447_CR47) 2015
A Asgary (8447_CR22) 2020; 8
C Zachreson (8447_CR9) 2021; 14
F Guerriero (8447_CR33) 2011; 14
H Wickham (8447_CR51) 2019; 4
M Gul (8447_CR37) 2015; 83
NR Gowda (8447_CR43) 2021; 7
LR Baden (8447_CR1) 2021; 384
ES McBryde (8447_CR7) 2021; 25
8447_CR44
YM Bar-On (8447_CR14) 2021; 385
S Salleh (8447_CR30) 2017; 35
E Mathieu (8447_CR13) 2021; 5
KW Soh (8447_CR35) 2017; 41
RB Fetter (8447_CR25) 1975; 24
T van de Kracht (8447_CR42) 2021; 51
WP Pierskalla (8447_CR29) 1994; 1
R Palmer (8447_CR39) 2018; 7
MG Thompson (8447_CR2) 2021; 70
F Papini (8447_CR20) 2021; 26
IM Smith (8447_CR19) 2021; 10
8447_CR45
H Takagi (8447_CR31) 2017; 20
8447_CR46
CM Woodside (8447_CR26) 1977; 7
R M’Hallah (8447_CR34) 2014; 78
E Goralnick (8447_CR18) 2021; 384
JD Little (8447_CR49) 1961; 9
PR Krause (8447_CR16) 2021; 398
C Carr (8447_CR53) 2011; 26
JH Tanne (8447_CR54) 2020; 368
B Vieira (8447_CR32) 2016; 16
References_xml – volume: 16
  start-page: 1
  issue: 1
  year: 2016
  ident: 8447_CR32
  publication-title: BMC Med Inform Decis Mak
  doi: 10.1186/s12911-016-0390-4
  contributor:
    fullname: B Vieira
– ident: 8447_CR44
– volume: 78
  start-page: 235
  year: 2014
  ident: 8447_CR34
  publication-title: Comput Ind Eng
  doi: 10.1016/j.cie.2014.07.022
  contributor:
    fullname: R M’Hallah
– volume: 29
  start-page: 1
  issue: 1
  year: 1981
  ident: 8447_CR27
  publication-title: Oper Res
  doi: 10.1287/opre.29.1.1
  contributor:
    fullname: JC Hershey
– ident: 8447_CR24
  doi: 10.1101/2021.03.24.21253517
– volume: 372
  start-page: n421
  year: 2021
  ident: 8447_CR17
  publication-title: BMJ
  doi: 10.1136/bmj.n421
  contributor:
    fullname: C Baraniuk
– volume: 39
  start-page: 3537
  issue: 27
  year: 2021
  ident: 8447_CR23
  publication-title: Vaccine
  doi: 10.1016/j.vaccine.2021.05.024
  contributor:
    fullname: RM Wood
– volume: 4
  start-page: 1686
  issue: 43
  year: 2019
  ident: 8447_CR51
  publication-title: J Open Source Softw
  doi: 10.21105/joss.01686
  contributor:
    fullname: H Wickham
– volume: 7
  start-page: 51
  issue: 1
  year: 1990
  ident: 8447_CR28
  publication-title: Queueing Syst
  doi: 10.1007/BF01158785
  contributor:
    fullname: SL Albin
– volume: 3
  start-page: 1
  issue: 1
  year: 2022
  ident: 8447_CR38
  publication-title: Oper Res Forum
  doi: 10.1007/s43069-021-00114-8
  contributor:
    fullname: A Elalouf
– volume-title: A language and environment for statistical computing. R Foundation for statistical Computing
  year: 2020
  ident: 8447_CR50
  contributor:
    fullname: R Core Team. R
– volume: 95
  start-page: 1
  issue: 5
  year: 2020
  ident: 8447_CR48
  publication-title: J Stat Softw
  doi: 10.18637/jss.v095.i05
  contributor:
    fullname: A Ebert
– volume: 21
  start-page: 962
  issue: 7
  year: 2021
  ident: 8447_CR4
  publication-title: Lancet Infect Dis
  doi: 10.1016/S1473-3099(21)00079-7
  contributor:
    fullname: FG Sandmann
– volume: 7
  start-page: e015007
  issue: 5
  year: 2017
  ident: 8447_CR36
  publication-title: BMJ Open
  doi: 10.1136/bmjopen-2016-015007
  contributor:
    fullname: S Mohiuddin
– volume: 7
  start-page: 1
  year: 2021
  ident: 8447_CR43
  publication-title: Digit Health
  contributor:
    fullname: NR Gowda
– volume: 9
  start-page: 383
  issue: 3
  year: 1961
  ident: 8447_CR49
  publication-title: Oper Res
  doi: 10.1287/opre.9.3.383
  contributor:
    fullname: JD Little
– volume: 214
  start-page: 79
  year: 2021
  ident: 8447_CR8
  publication-title: Med J Aust
  doi: 10.5694/mja2.50845
  contributor:
    fullname: N Scott
– start-page: 1292
  volume-title: Proceedings of the 2007 Winter Simulation Conference
  year: 2007
  ident: 8447_CR41
  doi: 10.1109/WSC.2007.4419734
  contributor:
    fullname: F Rico
– volume: 1
  start-page: 469
  issue: 6
  year: 1994
  ident: 8447_CR29
  publication-title: Handbooks Oper Res Manag Sci
  doi: 10.1016/S0927-0507(05)80094-5
  contributor:
    fullname: WP Pierskalla
– volume: 373
  start-page: n1088
  year: 2021
  ident: 8447_CR3
  publication-title: BMJ
  doi: 10.1136/bmj.n1088
  contributor:
    fullname: JL Bernal
– volume: 24
  start-page: 73
  issue: 3
  year: 1975
  ident: 8447_CR25
  publication-title: Simulation
  doi: 10.1177/003754977502400302
  contributor:
    fullname: RB Fetter
– volume: 83
  start-page: 327
  year: 2015
  ident: 8447_CR37
  publication-title: Comput Ind Eng
  doi: 10.1016/j.cie.2015.02.018
  contributor:
    fullname: M Gul
– ident: 8447_CR45
– volume: 217
  start-page: 1
  issue: 1
  year: 2012
  ident: 8447_CR40
  publication-title: Eur J Op Res
  doi: 10.1016/j.ejor.2011.05.026
  contributor:
    fullname: J Beliën
– volume: 14
  start-page: 100224
  year: 2021
  ident: 8447_CR9
  publication-title: Lancet Reg Health West Pac
  doi: 10.1016/j.lanwpc.2021.100224
  contributor:
    fullname: C Zachreson
– ident: 8447_CR11
  doi: 10.1101/2021.08.08.21261768
– volume: 14
  start-page: 89
  issue: 1
  year: 2011
  ident: 8447_CR33
  publication-title: Health Care Manag Sci
  doi: 10.1007/s10729-010-9143-6
  contributor:
    fullname: F Guerriero
– volume: 398
  start-page: 1377
  issue: 10308
  year: 2021
  ident: 8447_CR16
  publication-title: Lancet
  doi: 10.1016/S0140-6736(21)02046-8
  contributor:
    fullname: PR Krause
– volume: 70
  start-page: 495
  issue: 13
  year: 2021
  ident: 8447_CR2
  publication-title: MMWR Morb Mortal Wkly Rep
  doi: 10.15585/mmwr.mm7013e3
  contributor:
    fullname: MG Thompson
– volume: 20
  start-page: 433
  issue: 3
  year: 2017
  ident: 8447_CR31
  publication-title: Health Care Manag Sci
  doi: 10.1007/s10729-016-9363-5
  contributor:
    fullname: H Takagi
– volume: 5
  start-page: 947
  year: 2021
  ident: 8447_CR13
  publication-title: Nat Hum Behav
  doi: 10.1038/s41562-021-01122-8
  contributor:
    fullname: E Mathieu
– volume: 7
  start-page: 793
  issue: 11
  year: 1977
  ident: 8447_CR26
  publication-title: IEEE Tran Syst Man Cybernet
  doi: 10.1109/TSMC.1977.4309624
  contributor:
    fullname: CM Woodside
– volume: 25
  start-page: 427
  issue: 9
  year: 2021
  ident: 8447_CR7
  publication-title: Med J Aust
  doi: 10.5694/mja2.51263
  contributor:
    fullname: ES McBryde
– volume: 35
  start-page: 937
  issue: 9
  year: 2017
  ident: 8447_CR30
  publication-title: PharmacoEconomics
  doi: 10.1007/s40273-017-0523-3
  contributor:
    fullname: S Salleh
– ident: 8447_CR46
– volume: 385
  start-page: 1393
  issue: 15
  year: 2021
  ident: 8447_CR14
  publication-title: NEJM
  doi: 10.1056/NEJMoa2114255
  contributor:
    fullname: YM Bar-On
– volume: 41
  start-page: 1
  issue: 4
  year: 2017
  ident: 8447_CR35
  publication-title: J Med Sys
  doi: 10.1007/s10916-017-0711-x
  contributor:
    fullname: KW Soh
– volume-title: COVID-19 Vaccine Roll-out
  year: 2022
  ident: 8447_CR52
  contributor:
    fullname: Australian Government
– volume: 368
  start-page: m1090
  year: 2020
  ident: 8447_CR54
  publication-title: BMJ
  doi: 10.1136/bmj.m1090
  contributor:
    fullname: JH Tanne
– volume: 21
  start-page: 1
  issue: 220
  year: 2020
  ident: 8447_CR55
  publication-title: BMC Fam Pract
  contributor:
    fullname: M Kang
– volume: 384
  start-page: e67
  issue: 18
  year: 2021
  ident: 8447_CR18
  publication-title: N Engl J Med
  doi: 10.1056/NEJMp2102535
  contributor:
    fullname: E Goralnick
– volume: 8
  start-page: 469
  issue: 4
  year: 2020
  ident: 8447_CR22
  publication-title: Healthcare
  doi: 10.3390/healthcare8040469
  contributor:
    fullname: A Asgary
– volume: 374
  start-page: n2082
  year: 2021
  ident: 8447_CR15
  publication-title: BMJ
  doi: 10.1136/bmj.n2082
  contributor:
    fullname: E Mahase
– volume-title: An introduction to queueing theory: modeling and analysis in applications
  year: 2015
  ident: 8447_CR47
  doi: 10.1007/978-0-8176-8421-1
  contributor:
    fullname: UN Bhat
– ident: 8447_CR10
– volume: 10
  start-page: e001525
  issue: 3
  year: 2021
  ident: 8447_CR19
  publication-title: BMJ Open Qual
  doi: 10.1136/bmjoq-2021-001525
  contributor:
    fullname: IM Smith
– volume: 26
  start-page: 47
  issue: 1
  year: 2011
  ident: 8447_CR53
  publication-title: Aust J Emerg Manag
  contributor:
    fullname: C Carr
– volume: 372
  start-page: n292
  year: 2021
  ident: 8447_CR12
  publication-title: BMJ
  doi: 10.1136/bmj.n292
  contributor:
    fullname: L Eaton
– ident: 8447_CR6
  doi: 10.5694/mja2.51291
– volume: 384
  start-page: 403
  year: 2021
  ident: 8447_CR1
  publication-title: N Engl J Med
  doi: 10.1056/NEJMoa2035389
  contributor:
    fullname: LR Baden
– volume: 51
  start-page: 91
  issue: 2
  year: 2021
  ident: 8447_CR42
  publication-title: INFORMS J Appl Anal
  doi: 10.1287/inte.2020.1063
  contributor:
    fullname: T van de Kracht
– volume: 7
  start-page: 29
  issue: 1
  year: 2018
  ident: 8447_CR39
  publication-title: Health Syst
  doi: 10.1057/s41306-017-0024-9
  contributor:
    fullname: R Palmer
– volume: 26
  start-page: 1
  issue: 1
  year: 2021
  ident: 8447_CR20
  publication-title: Environ Health Prev Med
  doi: 10.1186/s12199-021-01018-z
  contributor:
    fullname: F Papini
– volume: 6
  start-page: 6
  issue: 1
  year: 2021
  ident: 8447_CR21
  publication-title: Manag Healthc
  contributor:
    fullname: TA Casadonte
– ident: 8447_CR5
  doi: 10.1016/j.vaccine.2021.04.042
SSID ssj0017827
Score 2.4385965
Snippet Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to...
COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform...
BACKGROUNDCOVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to...
Abstract Background COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination...
SourceID doaj
pubmedcentral
proquest
gale
crossref
SourceType Open Website
Open Access Repository
Aggregation Database
StartPage 1
SubjectTerms Bottlenecks
Clinics
Coronaviruses
COVID-19
COVID-19 vaccines
Customer services
Emergency medical care
Health care delivery
Health services research
Immunization
Management
Medical appointments and schedules
Methods
Pandemics
Public health
Queues
Queuing theory
Severe acute respiratory syndrome coronavirus 2
Simulation
Software
Stochastic network models
User interface
Vaccination
Workforce planning
SummonAdditionalLinks – databaseName: Directory of Open Access Journals
  dbid: DOA
  link: http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZKT5UQKqWItAW5EhKHKuo6Dz-4FWjVUy9tJW7WxA92D02rZpffz4ydrAgcuHCNHcWZz56H7fmGsY9KSd-0rStFrVzZdBiz6iB8GVUFUqkAQlPu8PWtuvmuv10STc621BfdCcv0wFlw5yr4duHASEdndJTrG43w0ZsoAtXuSdp3Iadgajw_QLunphQZLc8HtGpUxQADr4VuGtTLMzOU2Pr_1sl_3pP8zfBc7bNXo8fIL_JIX7Od0B-wl3m7jecsojdsSTXNEr02_wnOrfIeH3doCh362RzW_AHd5FnjEnUGh97zH5l6mk8ZUzynSw6fOfBh9TAW-OKJifaQ3V9d3n29LsciCqVDZ2xdghYN1N5FcLLrWiGhRicvVtACulaNDOihVaDqIABaHX1sZTSROH8qKuNb12_Zbv_Yh3eMe79wjTAddlVN66PuRK0VeCERbw2xYGeTTO1T5sqwKcbQ0mYELCJgEwJWF-wLiX3bk3iu0wNE347o23-hX7BPBJql1YgQORiTCnDAxGtlLxQGjAqVFH7uZNYTV5GbN0-w23EVD7ZCdWgo89gU7HTbTG_SzbQ-PG5SHyItxMi1YGo2XWZ_Nm_pV8vE5G1qrY1WR_9DFMdsr8oTvBTmhO2unzfhPXsx-M2HtDZ-AbgFE70
  priority: 102
  providerName: Directory of Open Access Journals
Title Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study
URI https://www.proquest.com/docview/2703997539
https://search.proquest.com/docview/2704867282
https://pubmed.ncbi.nlm.nih.gov/PMC9388987
https://doaj.org/article/7ed50ca96c42453185f91dfd9f1e1525
Volume 22
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3di9QwEB_ce5ADET-xei4RBB-kt5umTVLfzvOOE1EEFXwLaT5uF267x3XXv9-ZtD2svvnaJLTNJPORzO83AK-Vkr6sKpdzoVxeNhiz6sB9HlVhpVLBck3Y4Ytv6stP_eGMaHKqEQuTkvZdsz5urzbH7XqVciuvN24x5oktvn4-rYXWGCsvZjBD33AM0YerAzR5akTHaLno0KBRAQOMuZa6LFElH8Jdoq2Toigmxihx9v-rmf_OlvzD_Jw_gPuD38hO-u97CHdC-wju9YdurMcSPYYVVTZLJNvsl3Vu3Z_0MYcG0aG3zeyObdBZnjSuUHMw23p22RNQsxE3xXrQZPeOWdatN0OZL5b4aJ_Aj_Oz76cX-VBKIXfoku1yq3lphXfROtk0FZdWoKsXC1tZdLBKGdBPK6wSgVtb6ehjJWMdifmnoGK-QjyFg3bbhmfAvF-6ktcNdlVl5aNuuNDKei5R6trGDN6Oc2que8YMkyINLU0vDIPCMEkYRmfwnqb9tiexXacH25tLM8jcqOCrpbO1dHRPS3jvWHMffR15oPpNGbwhoRnakygiZwdoAX4wsVuZE4Vho0JVha87mvTEveSmzaPYzbCXO1OgUqwJf1xn8Oq2mUZSflobtvvUh6gLMX7NQE2Wy-TPpi24vBOf97Ccn__3yBdwWPQLPOf1ERzsbvbhJcw6v59jlPDx0zydNMzTPvkNPyAWiw
link.rule.ids 230,315,729,782,786,866,887,2106,27933,27934,53800,53802
linkProvider National Library of Medicine
linkToHtml http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9NAEB7RIkGlijciUGCRkDggN14_dtfcSmkVRFshUSRuq_U-mkjEqeqkv5-ZtV1huPWaWStO5m3P9w3AeymFK8rSJjyXNilq7FmV5y4JMjNCSm-4Iuzw7Ic8-6W-HBFNTjlgYeLQvq0X-83v5X6zmMfZysulnQ5zYtPvp4dVrhT2ytMtuIv-mqZDk96_PMCkJwd8jBLTFlMarTDAritVRYFBeQfuEXGdyLNslI4ia___sfnfecm_EtDxw1ve-iN40Fec7KATP4Y7vnkCu93jOtahkJ7CnHaiRXpudm2sXXTPCJnFVGqxTmdmzZZYZo-Ec4w5zDSOXXTU1WxAXLEObtl-Yoa1i2W_IIxFJttn8PP46PxwlvRLGBKLxdw6MYoXJnc2GCvquuTC5FgkhsyUBkuzQnis8DIjc8-NKVVwoRShCsQZlNEa4Dx_DtvNqvEvgDmX2oJXNR6VRemCqnmupHFcoL0oEybwcdCFvuy4NnTsUZTQnRI1KlFHJWo1gc-krpuTxJMdP1hdXej-L9fSuzK1phKW3vASUjxU3AVXBe5p89MEPpCyNXkzqtaaHpSAN0y8WPpAYsMpMcjh1-2NTqIX2rF4MBfdR4FWZxhOK0IuVxN4dyOmK2myrfGrTTxDpIfY-U5Ajsxs9MvGErSuyATeW9PLW1_5Fu7Pzk9P9MnXs2-vYCfrnCTh1R5sr682_jVstW7zJvrXH0KhKjk
linkToPdf http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1ba9RAFD7YCqUg9VqMVh1B8EHS7OQyF99q26WiloIKvg2TuXQX3OzS7Pb3e2aSLEbf9DVzhkxy7sk53wF4wzmzZVWZlBbcpGWNOatw1Kae55px7jQVoXf44iu__CHOzgNMznbUVyzaN_X8uPm5OG7ms1hbuVqYbKgTy66-nMpCCMyVs5X12Q7cRZ2d5EOi3v9AQMfHhx4ZwbIW3VoYY4CZ10SUJRrmfdgL4HWsyPORS4rI_X_b5z9rJn9zQtP7_3H8B3DQR57kpCN5CHdc8wjudZ_tSNeN9BhmYTZahOkmt9qYefetkBh0qQbjdaLXZIHh9mhxhraH6MaS6w7CmgydV6Rru2zfE03a-aIfFEYiou0T-D49_3Z6kfbDGFKDQd061YKWurDGa8PquqJMFxgs-lxXGkO0kjmM9HLNC0e1roS3vmJe-oAdlIdxwEVxCLvNsnFPgVg7MSWVNZLysrJe1LQQXFvKUG6E9gm8G_ihVh3mhoq5imCqY6RCRqrISCUS-BBYtqUMeNnxwvLmWvWvXXFnq4nRkpnwpzd0jHtJrbfSUxcmQCXwNjBcBa1G9hrdNyfggQM-ljrhmHhyNHZ4u6MRJWqjGS8PIqN6a9CqHM2qDB3MMoHX2-WwM1S4NW65iTQB_BAz4AT4SNRGTzZeQQmLiOC9RD37552vYO_qbKo-f7z89Bz2805PUiqPYHd9s3EvYKe1m5dRxX4Bk5wsuQ
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=Modelling+vaccination+capacity+at+mass+vaccination+hubs+and+general+practice+clinics%3A+a+simulation+study&rft.jtitle=BMC+health+services+research&rft.au=Hanly%2C+Mark&rft.au=Churches%2C+Tim&rft.au=Fitzgerald%2C+Ois%C3%ADn&rft.au=Caterson%2C+Ian&rft.date=2022-08-19&rft.eissn=1472-6963&rft.volume=22&rft.issue=1&rft.spage=1059&rft.epage=1059&rft_id=info:doi/10.1186%2Fs12913-022-08447-8&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1472-6963&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1472-6963&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1472-6963&client=summon