Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review.
Africa
Applied Epidemiological modelling
COVID 19 dynamics
Dynamic mathematical models
Policy
SARS-CoV-2
Journal
Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
23
06
2021
revised:
13
06
2022
accepted:
12
07
2022
pubmed:
23
7
2022
medline:
20
9
2022
entrez:
22
7
2022
Statut:
ppublish
Résumé
Applied epidemiological models have played a critical role in understanding the transmission and control of disease outbreaks. Their utility and accuracy in decision-making on appropriate responses during public health emergencies is however a factor of their calibration to local data, evidence informing model assumptions, speed of obtaining and communicating their results, ease of understanding and willingness by policymakers to use their insights. We conducted a systematic review of infectious disease models focused on SARS-CoV-2 in Africa to determine: a) spatial and temporal patterns of SARS-CoV-2 modelling in Africa, b) use of local data to calibrate the models and local expertise in modelling activities, and c) key modelling questions and policy insights. We searched PubMed, Embase, Web of Science and MedRxiv databases following the PRISMA guidelines to obtain all SARS-CoV-2 dynamic modelling papers for one or multiple African countries. We extracted data on countries studied, authors and their affiliations, modelling questions addressed, type of models used, use of local data to calibrate the models, and model insights for guiding policy decisions. A total of 74 papers met the inclusion criteria, with nearly two-thirds of these coming from 6% (3) of the African countries. Initial papers were published 2 months after the first cases were reported in Africa, with most papers published after the first wave. More than half of all papers (53, 78%) and (48, 65%) had a first and last author affiliated to an African institution respectively, and only 12% (9) used local data for model calibration. A total of 60% (46) of the papers modelled assessment of control interventions. The transmission rate parameter was found to drive the most uncertainty in the sensitivity analysis for majority of the models. The use of dynamic models to draw policy insights was crucial and therefore there is need to increase modelling capacity in the continent.
Identifiants
pubmed: 35868211
pii: S1755-4365(22)00053-6
doi: 10.1016/j.epidem.2022.100610
pmc: PMC9281458
pii:
doi:
Types de publication
Journal Article
Systematic Review
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
100610Subventions
Organisme : FIC NIH HHS
ID : D43 TW011519
Pays : United States
Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors have declared no competing interest.
Références
Libyan J Med. 2021 Dec;16(1):1871798
pubmed: 33407008
PLoS One. 2020 Jul 24;15(7):e0236003
pubmed: 32706790
PLoS One. 2020 Aug 5;15(8):e0237126
pubmed: 32756608
BMJ. 2015 Jan 02;350:g7647
pubmed: 25555855
Adv Differ Equ. 2021;2021(1):57
pubmed: 33495699
PLoS One. 2021 May 6;16(5):e0250086
pubmed: 33956823
Chaos Solitons Fractals. 2020 Dec;141:110437
pubmed: 33519111
BMJ Glob Health. 2019 Mar 4;4(2):e001047
pubmed: 30899571
Infect Dis Model. 2021;6:15-23
pubmed: 33200107
J Environ Public Health. 2020 Jun 2;2020:9769267
pubmed: 32565842
Pak J Med Sci. 2020 May;36(COVID19-S4):S134-S137
pubmed: 32582333
J Appl Math Comput. 2021;66(1-2):1-20
pubmed: 32837466
J Med Virol. 2021 Mar;93(3):1449-1458
pubmed: 32790106
Wellcome Open Res. 2020 Apr 2;5:59
pubmed: 32529040
PLoS One. 2021 Feb 22;16(2):e0247456
pubmed: 33617579
PeerJ. 2023 Feb 15;11:e14736
pubmed: 36819996
BMC Med. 2020 Oct 5;18(1):316
pubmed: 33012285
J Biol Dyn. 2020 Dec;14(1):748-766
pubmed: 32990177
Interdiscip Perspect Infect Dis. 2020 Dec 18;2020:6231461
pubmed: 33381170
Sci Afr. 2022 Jul;16:e01268
pubmed: 35791321
BMJ Open. 2021 Mar 8;11(3):e044149
pubmed: 34006031
PLoS Comput Biol. 2015 Jul 30;11(7):e1004383
pubmed: 26226185
Res Synth Methods. 2019 Mar;10(1):125-133
pubmed: 30508309
Infect Dis Model. 2021;6:381-397
pubmed: 33521403
BMJ. 2021 Mar 29;372:n71
pubmed: 33782057
BMC Res Notes. 2020 Jul 23;13(1):352
pubmed: 32703315
Chaos Solitons Fractals. 2020 Nov;140:110163
pubmed: 32834647
J Biol Dyn. 2021 Dec;15(1):137-150
pubmed: 33538240
Indian J Public Health. 2020 Jun;64(Supplement):S142-S146
pubmed: 32496246
Epidemics. 2021 Dec;37:100499
pubmed: 34534749
PLOS Glob Public Health. 2022 May 16;2(5):e0000086
pubmed: 36962136
PLoS One. 2021 Apr 6;16(4):e0249479
pubmed: 33822785
Ann Intern Med. 2014 May 20;160(10):684-94
pubmed: 24842415
Infect Dis Model. 2021;6:258-272
pubmed: 33458453
Sci Afr. 2022 Nov;18:e01408
pubmed: 36340510
Biology (Basel). 2020 Nov 03;9(11):
pubmed: 33153015
Epidemics. 2021 Dec;37:100529
pubmed: 34871942
BMJ Glob Health. 2020 May;5(5):
pubmed: 32451366
BMC Med. 2020 May 25;18(1):152
pubmed: 32448247
BMJ Glob Health. 2020 Sep;5(9):
pubmed: 32948617
Eur Phys J Plus. 2020;135(11):938
pubmed: 33262923
Infect Dis Model. 2021;6:370-380
pubmed: 33527092
Science. 2021 Nov 19;374(6570):989-994
pubmed: 34618602
Math Biosci Eng. 2020 Oct 22;17(6):7192-7220
pubmed: 33378893
PLoS Curr. 2014 Oct 31;6:
pubmed: 25642378
Epidemiol Infect. 2020 Aug 05;148:e166
pubmed: 32753078
Science. 2020 Jul 24;369(6502):413-422
pubmed: 32532802
BMC Med. 2020 Oct 14;18(1):324
pubmed: 33050951
Pan Afr Med J. 2020 Aug 27;36:355
pubmed: 33224421
PLoS Biol. 2021 Apr 2;19(4):e3000959
pubmed: 33798194
Med Decis Making. 2021 May;41(4):379-385
pubmed: 33535889
BMJ Glob Health. 2020 May;5(5):
pubmed: 32451367
Comput Math Methods Med. 2020 Oct 29;2020:5379278
pubmed: 33178332
Results Phys. 2021 Mar;22:103853
pubmed: 33532177
AAS Open Res. 2020 Apr 17;3:4
pubmed: 32399515
Lancet. 2021 Apr 3;397(10281):1238-1240
pubmed: 33773113