{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T04:15:57Z","timestamp":1778127357846,"version":"3.51.4"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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 \u22647 members, in four Sub-Saharan African cities: Kinshasa (82 households, 370 individuals), Lubumbashi (225 households, 970 individuals), Conakry (149 households, 649 individuals), and Yaound\u00e9 (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.<\/jats:p>","DOI":"10.1093\/aje\/kwaf269","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T13:13:30Z","timestamp":1764854010000},"page":"1368-1378","source":"Crossref","is-referenced-by-count":0,"title":["Assessing SARS-CoV-2 transmission in African households from the reanalysis of serosurveys"],"prefix":"10.1093","volume":"195","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7216-4037","authenticated-orcid":false,"given":"Lina","family":"Cristancho-Fajardo","sequence":"first","affiliation":[{"name":"Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Universit\u00e9 Paris Cit\u00e9, INSERM U1332, CNRS UMR2000 , Paris 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