Evidence map›Paper›PMID 40051513›Full record

ArticleFrontiers in public health2025

Spatiotemporal prevalence of COVID-19 and SARS-CoV-2 variants in Africa.

Li-Ping Gao, Can-Jun Zheng, Ting-Ting Tian, Alie Brima Tia, Michael K Abdulai, Kang Xiao, Cao Chen, Dong-Lin Liang, Qi Shi, Zhi-Guo Liu and 1 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Li-Ping GaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Can-Jun ZhengChinese Center for Disease Control and Prevention, Beijing, China.
Ting-Ting TianNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Alie Brima TiaSierra Leone-China Friendship Biological Safety Laboratory, Freetown, Sierra Leone.
Michael K AbdulaiSierra Leone-China Friendship Biological Safety Laboratory, Freetown, Sierra Leone.
Kang XiaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Cao ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Dong-Lin LiangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Qi ShiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhi-Guo LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiao-Ping DongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has caused significant public health and socioeconomic crises across Africa; however, the prevalent patterns of COVID-19 and the circulating characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in the continent remain insufficiently documented. Methods: In this study, national data on case numbers, infection incidences, mortality rates, the circulation of SARS-CoV-2 variants, and key health indexes were collected from various official and professional sources between January 2020 and December 2023 were analyzed with SaTScan and geographically weighted regression (GWR). Results: The prevalent profiles and circulating features of SARS-CoV-2 across the African continent, including its five regions and all African countries, were analyzed. Four major waves of the epidemic were observed. The first wave was closely associated with the introduction of the early SARS-CoV-2 strain while the subsequent waves were linked to the emergence of specific variants, including variants of concern (VOCs) Alpha, Beta, variants of interest (VOIs) Eta (second wave), VOC Delta (third wave), and VOC Omicron (fourth wave). SaTScan analysis identified four large spatiotemporal clusters that affected various countries. A significant number of countries (50 out of 56) reported their first cases during February 2020 and March 2020, predominantly involving individuals with confirmed cross-continental travel histories, mainly from Europe. In total, 12 distinct SARS-CoV-2 VOCs and VOIs were identified, with the most prevalent being VOCs Omicron, Delta, Beta, Alpha, and VOI Eta. Unlike the dominance of VOC Delta during the third wave and Omicron during the fourth wave, VOC Alpha was relatively rare in the Southern regions but more common in the other four regions. At the same time, Beta predominated in the Southern region and Eta in the Western region during the second wave. Additionally, relatively higher COVID-19 case incidences and mortalities were reported in the Southern and Northern African regions. Spearman rank correlation and geographically weighted regression (GWR) analyses of COVID-19 incidences against health indexes in 52 African countries indicate that countries with higher national health expenditures and better personnel indexes tended to report higher case incidences. Discussion: This study offers a detailed overview of the COVID-19 pandemic in Africa. Strengthening the capacity of health institutions across African countries is essential for the timely detection of new SARS-CoV-2 variants and, consequently, for preparedness against future COVID-19 pandemics and other potentially infectious disease outbreaks.

Indexed as

COVID-19SARS-CoV-2AfricaHumansIncidencePandemicsPrevalenceSpatio-Temporal AnalysisAfricaCOVID-19health indexesSARS-CoV-2variant

Identifiers

PMID40051513
PMCPMC11882590

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.