Temporal trends of SARS-CoV-2 seroprevalence during the first wave of the COVID-19 epidemic in Kenya
Open accessPublished version of a preprint
Abstract
Observed SARS-CoV-2 infections and deaths are low in tropical Africa raising questions about the extent of transmission. We measured SARS-CoV-2 IgG by ELISA in 9,922 blood donors across Kenya and adjusted for sampling bias and test performance. By 1st September 2020, 577 COVID-19 deaths were observed nationwide and seroprevalence was 9.1% (95%CI 7.6-10.8%). Seroprevalence in Nairobi was 22.7% (18.0-27.7%). Although most people remained susceptible, SARS-CoV-2 had spread widely in Kenya with apparently low associated mortality.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several serosurveys suggest that Africa experienced large waves of infection but that most cases went unreported, in part because they did not lead to severe disease.
“Several serosurveys suggest that Africa experienced large waves of infection but that most cases went unreported, in part because they did not lead to severe disease.”
From The Lancet Commission on lessons for the future from the COVID-19 pandemic (2022) · cites this paper as [187]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Rapid Decay of Anti–SARS-CoV-2 Antibodies in Persons with Mild Covid-19
Ibarrondo, F. J., Fulcher, J. A., Goodman-Meza, D., Elliott, J., Hofmann, C., Hausner, M. A., Ferbas, K. G., Tobin, N. H., Aldrovandi, G. M., & Yang, O. O. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2025179 · Cited by 1,021 · see citing works
Buss, L. F., Prete, C. A., Abrahim, C. M. M., Mendrone, A., Salomon, T., de Almeida-Neto, C., França, R. F. O., Belotti, M. C., Carvalho, M. P. S. S., Costa, A. G., et al. (2021) · Science · doi:10.1126/science.abe9728 · Cited by 488 · see citing works
Seroprevalence of anti–SARS-CoV-2 IgG antibodies in Kenyan blood donors
Uyoga, S., Adetifa, I. M. O., Karanja, H. K., Nyagwange, J., Tuju, J., Wanjiku, P., Aman, R., Mwangangi, M., Amoth, P., Kasera, K., et al. (2021) · Science · doi:10.1126/science.abe1916 · Cited by 328 · cited 3× · see citing works
“3,5 .”
Mulenga, L. B., Hines, J. Z., Fwoloshi, S., Chirwa, L., Siwingwa, M., Yingst, S., Wolkon, A., Barradas, D. T., Favaloro, J., Zulu, J. E., et al. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(21)00053-x · Cited by 149 · see citing works
Chibwana, M. G., Jere, K. C., Kamng'ona, R., Mandolo, J., Katunga-Phiri, V., Tembo, D., Mitole, N., Musasa, S., Sichone, S., Lakudzala, A., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.16188.1 · Cited by 110 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Lancet Commission on lessons for the future from the COVID-19 pandemic
Sachs, J. D., Karim, S. S. A., Aknin, L., Allen, J., Brosbøl, K., Colombo, F., Barron, G. C., Espinosa, M. F., Gaspar, V., Gaviria, A., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01585-9 · Cited by 842 · see citing works
Lobaloba Ingoba, L., Djontu, J. C., Mfoutou Mapanguy, C. C., Mouzinga, F., Diafouka Kietela, S., Vouvoungui, C., Kuisma, E., Nguimbi, E., & Ntoumi, F. (2022) · IJID Regions · doi:10.1016/j.ijregi.2022.01.002 · Cited by 12 · see citing works
- Authors
- Adetifa, I. M. O., Uyoga, S., Gitonga, J. N., Mugo, D., Otiende, M., Nyagwange, J., Karanja, H. K., Tuju, J., Wanjiku, P., Aman, R., Mwangangi, M., Amoth, P., Kasera, K., Ng’ang’a, W., Rombo, C., Yegon, C., Kithi, K., Odhiambo, E., Rotich, T., Orgut, I., Kihara, S., Bottomley, C., Kagucia, E. W., Gallagher, K. E., Etyang, A., Voller, S., Lambe, T., Wright, D., Barasa, E., Tsofa, B., Bejon, P., Ochola-Oyier, L. I., Agweyu, A., Scott, J. A. G., & Warimwe, G. M.
- Affiliations
- Kenya Medical Research Institute (KE)
- Ministry of Health (KE)
- Government of Kenya (KE)
- Kenya National Blood Transfusion Services (KE)
- London School of Hygiene & Tropical Medicine (GB)
- University of Oxford (GB)
- Corresponding author
- Adetifa, Ifedayo M. O.
- Uyoga, Sophie
- ORCID
- Adetifa, Ifedayo M. O.
- Uyoga, Sophie
- Gitonga, John N.
- Mugo, Daisy
- Otiende, Mark
- Nyagwange, James
- Karanja, Henry K.
- Tuju, James
- Wanjiku, Perpetual
- Aman, Rashid
- Amoth, Patrick
- Kasera, Kadondi
- Ng’ang’a, Wangari
- Rotich, Thomas
- Bottomley, Christian
- Kagucia, Eunice W.
- Gallagher, Katherine E.
- Etyang, Anthony
- Voller, Shirine
- Lambe, Teresa
- Wright, Daniel
- Barasa, Edwine
- Tsofa, Benjamin
- Bejon, Philip
- Agweyu, Ambrose
- Scott, J. Anthony G.
- Warimwe, George M.
- Venue
- Nature Communications
- Published
- 2021-06-25
- Volume
- 12
- Issue
- 1
- Pages
- 3966-3966
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8233334
- OpenAlex
- W3173607783
- Cited by
- 67
- References
- 5
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 11 · 2023: 8 · 2022: 25 · 2021: 12
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Wellcome Trust (220991/Z/20/Z, 203077/Z/16/Z)
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- SARS-CoV-2, COVID-19 pandemic, seroprevalence, IgG, blood donors, Kenya, Nairobi, COVID-19 mortality, population susceptibility, first wave
- MeSH terms
- COVID-19, SARS-CoV-2, Adolescent, Adult, Antibodies, Viral, Bayes Theorem, Enzyme-Linked Immunosorbent Assay, Female, Humans, Immunoglobulin G, Kenya, Male, Middle Aged, Prevalence, Seroepidemiologic Studies, Young Adult, Epidemics, Spike Glycoprotein, Coronavirus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
