Ignite Global Health Research Lab, Global Research Institute, William & Mary, Williamsburg, Virginia, USA
Kinesiology and Health Sciences, William & Mary, Williamsburg, Virginia, USA.
BMJ Open. 2023 Jan 25;13(1):e067134. doi: 10.1136/bmjopen-2022-067134.
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.
Systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
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.
Peer-reviewed studies that demonstrated the application of spatial and temporal approaches to COVID-19 outcomes.
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.
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.
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.
CRD42021279767.
已有多项研究旨在更好地理解 COVID-19 的潜在时空动态。本综述旨在确定已应用于 COVID-19 的不同空间和时空建模方法，并研究据报道与非洲 COVID-19 风险相关的影响因素。
使用系统评价和荟萃分析的首选报告项目指南进行系统综述。
使用主题挖掘关键词从以下数据库中确定 2020 年 1 月至 2022 年 2 月期间进行的同行评审研究：PubMed、Scopus、Proquest 中的 MEDLINE、EBSCOhost 中的 CINHAL 和 ProQuest 中的冠状病毒研究数据库。还通过研究参考文献列表进行了手动搜索。
展示 COVID-19 结果的空间和时间方法应用的同行评审研究。
使用基于预测建模研究系统评价的关键评估和数据提取的标准化提取表来提取纳入研究的元数据。使用经过验证的评分标准根据方法学相关性和质量对研究进行评估。
在五个数据库的 2065 个命中中，标题和摘要筛选产生了 827 项研究，其中 22 项进行了综合和定性分析。最常见的社会经济变量是人口密度。HIV 流行率是最常见的流行病学指标，而温度是最常见的环境指标。13 项研究（59%）实施了包含 COVID-19 未测量因素和时间和空间细微影响的各种空间和时空模型配方。聚类分析在七项研究（32%）中得到应用，以探索 COVID-19 的变化，并确定观察到的模式是否随机。
非洲的 COVID-19 建模仍处于起步阶段，已在各种环境中应用了多种空间和时空方法。加强常规数据系统仍然是为脆弱人群的空间变化和大流行进展的时间变化生成估计和了解驱动因素的关键。
PROSPERO 注册号：CRD42021279767。
