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Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa

OBJECTIVE: Various studies have been published to better understand the underlying spatial and temporal dynamics of COVID-19. This review sought to identify different spatial and spatio-temporal modelling methods that have been applied to COVID-19 and examine influential covariates that have been re...

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Autores principales: Odhiambo, Julius Nyerere, Dolan, Carrie B., Troup, Lydia, Rojas, Nathaly Perez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884571/
https://www.ncbi.nlm.nih.gov/pubmed/36697047
http://dx.doi.org/10.1136/bmjopen-2022-067134
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author Odhiambo, Julius Nyerere
Dolan, Carrie B.
Troup, Lydia
Rojas, Nathaly Perez
author_facet Odhiambo, Julius Nyerere
Dolan, Carrie B.
Troup, Lydia
Rojas, Nathaly Perez
author_sort Odhiambo, Julius Nyerere
collection PubMed
description OBJECTIVE: Various studies have been published to better understand the underlying spatial and temporal dynamics of COVID-19. This review sought to identify different spatial and spatio-temporal modelling methods that have been applied to COVID-19 and examine influential covariates that have been reportedly associated with its risk in Africa. DESIGN: Systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DATA SOURCES: Thematically mined keywords were used to identify refereed studies conducted between January 2020 and February 2022 from the following databases: PubMed, Scopus, MEDLINE via Proquest, CINHAL via EBSCOhost and Coronavirus Research Database via ProQuest. A manual search through the reference list of studies was also conducted. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Peer-reviewed studies that demonstrated the application of spatial and temporal approaches to COVID-19 outcomes. DATA EXTRACTION AND SYNTHESIS: A standardised extraction form based on critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist was used to extract the meta-data of the included studies. A validated scoring criterion was used to assess studies based on their methodological relevance and quality. RESULTS: Among 2065 hits in five databases, title and abstract screening yielded 827 studies of which 22 were synthesised and qualitatively analysed. The most common socioeconomic variable was population density. HIV prevalence was the most common epidemiological indicator, while temperature was the most common environmental indicator. Thirteen studies (59%) implemented diverse formulations of spatial and spatio-temporal models incorporating unmeasured factors of COVID-19 and the subtle influence of time and space. Cluster analyses were used across seven studies (32%) to explore COVID-19 variation and determine whether observed patterns were random. CONCLUSION: COVID-19 modelling in Africa is still in its infancy, and a range of spatial and spatio-temporal methods have been employed across diverse settings. Strengthening routine data systems remains critical for generating estimates and understanding factors that drive spatial variation in vulnerable populations and temporal variation in pandemic progression. PROSPERO REGISTRATION NUMBER: CRD42021279767.
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spelling pubmed-98845712023-01-30 Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa Odhiambo, Julius Nyerere Dolan, Carrie B. Troup, Lydia Rojas, Nathaly Perez BMJ Open Global Health OBJECTIVE: Various studies have been published to better understand the underlying spatial and temporal dynamics of COVID-19. This review sought to identify different spatial and spatio-temporal modelling methods that have been applied to COVID-19 and examine influential covariates that have been reportedly associated with its risk in Africa. DESIGN: Systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DATA SOURCES: Thematically mined keywords were used to identify refereed studies conducted between January 2020 and February 2022 from the following databases: PubMed, Scopus, MEDLINE via Proquest, CINHAL via EBSCOhost and Coronavirus Research Database via ProQuest. A manual search through the reference list of studies was also conducted. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Peer-reviewed studies that demonstrated the application of spatial and temporal approaches to COVID-19 outcomes. DATA EXTRACTION AND SYNTHESIS: A standardised extraction form based on critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist was used to extract the meta-data of the included studies. A validated scoring criterion was used to assess studies based on their methodological relevance and quality. RESULTS: Among 2065 hits in five databases, title and abstract screening yielded 827 studies of which 22 were synthesised and qualitatively analysed. The most common socioeconomic variable was population density. HIV prevalence was the most common epidemiological indicator, while temperature was the most common environmental indicator. Thirteen studies (59%) implemented diverse formulations of spatial and spatio-temporal models incorporating unmeasured factors of COVID-19 and the subtle influence of time and space. Cluster analyses were used across seven studies (32%) to explore COVID-19 variation and determine whether observed patterns were random. CONCLUSION: COVID-19 modelling in Africa is still in its infancy, and a range of spatial and spatio-temporal methods have been employed across diverse settings. Strengthening routine data systems remains critical for generating estimates and understanding factors that drive spatial variation in vulnerable populations and temporal variation in pandemic progression. PROSPERO REGISTRATION NUMBER: CRD42021279767. BMJ Publishing Group 2023-01-25 /pmc/articles/PMC9884571/ /pubmed/36697047 http://dx.doi.org/10.1136/bmjopen-2022-067134 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Global Health
Odhiambo, Julius Nyerere
Dolan, Carrie B.
Troup, Lydia
Rojas, Nathaly Perez
Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa
title Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa
title_full Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa
title_fullStr Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa
title_full_unstemmed Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa
title_short Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa
title_sort spatial and spatio-temporal epidemiological approaches to inform covid-19 surveillance and control: a systematic review of statistical and modelling methods in africa
topic Global Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884571/
https://www.ncbi.nlm.nih.gov/pubmed/36697047
http://dx.doi.org/10.1136/bmjopen-2022-067134
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