In one paragraphArticle in American journal of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
23 authors.
Lina Cristancho-FajardoMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, INSERM U1332, CNRS UMR2000, Paris, France.ORCID 0000-0001-7216-4037 Antoine Nkuba-NdayeInstitut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo.ORCID 0000-0003-2850-7498 Nathanaël HozéMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, INSERM U1332, CNRS UMR2000, Paris, France.ORCID 0000-0002-3977-8966 Emilande GuichetTransVIHMI, University of Montpellier, Institut de Recherche pour le Développement (IRD), INSERM, Montpellier, France.ORCID 0000-0003-0380-2044 Yves Asuni IziaMédecins Sans Frontières, Paris, France.
Francis Ateba NdongoDivision of Operational Research in Health, Ministry of Public Health of Cameroon, Yaoundé, Cameroon.ORCID 0000-0002-0745-1715 Placide Mbala-KingebeniInstitut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo.ORCID 0000-0003-1556-3570 Abou Aissata SoumahCentre de Recherche et de Formation en Infectiologie de Guinée (CERFIG), Université Gamal Abdel Nasser de Conakry, Conakry, Guinea.ORCID 0009-0009-1027-2302 Paul Tshiminyi-MunkambaInstitut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo.
Mamadou Saliou Kalifa DialloTransVIHMI, University of Montpellier, Institut de Recherche pour le Développement (IRD), INSERM, Montpellier, France.ORCID 0000-0002-9742-8702 Anne-Cécile Zoung-Kanyi BissekDivision of Operational Research in Health, Ministry of Public Health of Cameroon, Yaoundé, Cameroon.ORCID 0009-0004-2300-0748 Sheila Makiala-MandandaInstitut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo.ORCID 0000-0002-4261-4399 Abdoulaye ToureCentre de Recherche et de Formation en Infectiologie de Guinée (CERFIG), Université Gamal Abdel Nasser de Conakry, Conakry, Guinea.ORCID 0000-0003-2269-6611 Steve Ahuka-MundekeInstitut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo.ORCID 0009-0009-5210-864X Ahidjo AyoubaTransVIHMI, University of Montpellier, Institut de Recherche pour le Développement (IRD), INSERM, Montpellier, France.ORCID 0000-0002-5081-1632 Jean-François EtardTransVIHMI, University of Montpellier, Institut de Recherche pour le Développement (IRD), INSERM, Montpellier, France.ORCID 0000-0002-4873-0788 Martine PeetersTransVIHMI, University of Montpellier, Institut de Recherche pour le Développement (IRD), INSERM, Montpellier, France.ORCID 0000-0001-6738-8730 Eric DelaporteTransVIHMI, University of Montpellier, Institut de Recherche pour le Développement (IRD), INSERM, Montpellier, France.ORCID 0000-0002-1822-9853 Simon CauchemezMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, INSERM U1332, CNRS UMR2000, Paris, France.ORCID 0000-0001-9186-4549 Funding
Agence Française de Développement and the Ministère de l'Europe et des Affaires Etrangères, Francethe European Commission under the EU4Health programme 2021-2027 101102733-DURABLEthe European Union's Horizon 2020 research and innovation program under VEOthe INCEPTION projectthe Investissement d'Avenir program, the Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases ANR-10-LABX-62-IBEID
6 · The paper itselfAbstract
Household transmission studies provided key insights on SARS-CoV-2 transmission in high-income countries but were rarely implemented in Africa. To help fill this gap, we analyzed SARS-CoV-2 seroprevalence studies with a household-based recruitment, focusing on households with ≤7 members, in four Sub-Saharan African cities: Kinshasa (82 households, 370 individuals), Lubumbashi (225 households, 970 individuals), Conakry (149 households, 649 individuals), and Yaoundé (311 households, 1183 individuals), between late 2020 and mid-2021. Using an extended chain-binomial model accounting for missing serology, we estimated both the probability of community-acquired infection and within-household transmission. The proportion infected in the community rose sharply over time, reaching up to 73% by June 2021. Household transmission varied by location, with secondary attack rates ranging from 8.9% to 26.7%, and households accounting for 9% to 28% of infections. Simulations showed that including households with missing serology improved the precision of estimates without introducing bias. Secondary attack rate estimates were consistent with findings from South Africa and slightly lower than global pooled estimates, mostly from high-income settings, suggesting different transmission dynamics in African contexts. Our approach for handling missing serology can improve transmission estimates accuracy.
Indexed as
COVID-19Family CharacteristicsAdolescentAdultAfrica South of the SaharaChildFemaleHumansMaleMiddle AgedSARS-CoV-2Seroepidemiologic StudiesYoung Adultchain-binomial modelCOVID-19household transmissionmissing dataserosurveys
Identifiers
PMID41347851
PMCPMC13149013
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC
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