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Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review

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 info...

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Autores principales: Kimani, Teresia Njoki, Nyamai, Mutono, Owino, Lillian, Makori, Anita, Ombajo, Loice Achieng, Maritim, MaryBeth, Anzala, Omu, Thumbi, S.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281458/
https://www.ncbi.nlm.nih.gov/pubmed/35868211
http://dx.doi.org/10.1016/j.epidem.2022.100610
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author Kimani, Teresia Njoki
Nyamai, Mutono
Owino, Lillian
Makori, Anita
Ombajo, Loice Achieng
Maritim, MaryBeth
Anzala, Omu
Thumbi, S.M.
author_facet Kimani, Teresia Njoki
Nyamai, Mutono
Owino, Lillian
Makori, Anita
Ombajo, Loice Achieng
Maritim, MaryBeth
Anzala, Omu
Thumbi, S.M.
author_sort Kimani, Teresia Njoki
collection PubMed
description 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.
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spelling pubmed-92814582022-07-15 Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review Kimani, Teresia Njoki Nyamai, Mutono Owino, Lillian Makori, Anita Ombajo, Loice Achieng Maritim, MaryBeth Anzala, Omu Thumbi, S.M. Epidemics Article 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. The Author(s). Published by Elsevier B.V. 2022-09 2022-07-14 /pmc/articles/PMC9281458/ /pubmed/35868211 http://dx.doi.org/10.1016/j.epidem.2022.100610 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Kimani, Teresia Njoki
Nyamai, Mutono
Owino, Lillian
Makori, Anita
Ombajo, Loice Achieng
Maritim, MaryBeth
Anzala, Omu
Thumbi, S.M.
Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review
title Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review
title_full Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review
title_fullStr Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review
title_full_unstemmed Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review
title_short Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review
title_sort infectious disease modelling for sars-cov-2 in africa to guide policy: a systematic review
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281458/
https://www.ncbi.nlm.nih.gov/pubmed/35868211
http://dx.doi.org/10.1016/j.epidem.2022.100610
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