目的:已经发表了各种研究,以更好地了解COVID-19的潜在时空动态。这篇评论试图确定已应用于COVID-19的不同空间和时空建模方法,并研究据报道与其在非洲的风险相关的有影响力的协变量。
方法:使用系统评价和荟萃分析指南的首选报告项目进行系统评价。
方法:使用主题挖掘的关键字来识别2020年1月至2022年2月之间从以下数据库进行的裁判研究:PubMed,Scopus,MEDLINE通过Proquest,CINHAL通过EBSCOHost和冠状病毒研究数据库通过ProQuest。还对参考研究列表进行了手动搜索。
方法:同行评审的研究证明了空间和时间方法对COVID-19结局的应用。
方法:使用基于关键评估和数据提取的标准化提取表,用于预测模型研究的系统综述清单,以提取纳入研究的元数据。根据研究的方法学相关性和质量,使用经过验证的评分标准来评估研究。
结果:在五个数据库的2065次点击中,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。艾滋病毒流行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%)实施了空间和时空模型的不同表述,其中包含了COVID-19的未测量因素以及时间和空间的微妙影响。对7项研究(32%)进行了聚类分析,以探索COVID-19的变异,并确定观察到的模式是否是随机的。
结论:非洲的COVID-19模型仍处于起步阶段,和一系列的空间和时空方法已经在不同的环境中采用。加强常规数据系统对于产生估计和理解导致脆弱人群空间变化和大流行进展时间变化的因素仍然至关重要。
未经评估:CRD42021279767。
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.