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Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases
OBJECTIVES: In most African countries, confirmed COVID-19 case counts underestimate the number of new SARS-CoV-2 infection cases. We propose a multiplying factor to approximate the number of biologically probable new infections from the number of confirmed cases. METHODS: Each of the first thousand...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655282/ https://www.ncbi.nlm.nih.gov/pubmed/37978439 http://dx.doi.org/10.1186/s12874-023-02077-2 |
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author | Diarra, YM Wimba, PM Katchunga, PB Bengehya, J Miganda, B Oyimangirwe, M Tshilolo, L Ahuka, SM Iwaz, J Étard, JF Écochard, R Vanhems, P Rabilloud, M |
author_facet | Diarra, YM Wimba, PM Katchunga, PB Bengehya, J Miganda, B Oyimangirwe, M Tshilolo, L Ahuka, SM Iwaz, J Étard, JF Écochard, R Vanhems, P Rabilloud, M |
author_sort | Diarra, YM |
collection | PubMed |
description | OBJECTIVES: In most African countries, confirmed COVID-19 case counts underestimate the number of new SARS-CoV-2 infection cases. We propose a multiplying factor to approximate the number of biologically probable new infections from the number of confirmed cases. METHODS: Each of the first thousand suspect (or alert) cases recorded in South Kivu (DRC) between 29 March and 29 November 2020 underwent a RT-PCR test and an IgM and IgG serology. A latent class model and a Bayesian inference method were used to estimate (i) the incidence proportion of SARS-CoV-2 infection using RT-PCR and IgM test results, (ii) the prevalence using RT-PCR, IgM and IgG test results; and, (iii) the multiplying factor (ratio of the incidence proportion on the proportion of confirmed –RT-PCR+– cases). RESULTS: Among 933 alert cases with complete data, 218 (23%) were RT-PCR+; 434 (47%) IgM+; 464 (~ 50%) RT-PCR+, IgM+, or both; and 647 (69%) either IgG + or IgM+. The incidence proportion of SARS-CoV-2 infection was estimated at 58% (95% credibility interval: 51.8–64), its prevalence at 72.83% (65.68–77.89), and the multiplying factor at 2.42 (1.95–3.01). CONCLUSIONS: In monitoring the pandemic dynamics, the number of biologically probable cases is also useful. The multiplying factor helps approximating it. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02077-2. |
format | Online Article Text |
id | pubmed-10655282 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106552822023-11-17 Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases Diarra, YM Wimba, PM Katchunga, PB Bengehya, J Miganda, B Oyimangirwe, M Tshilolo, L Ahuka, SM Iwaz, J Étard, JF Écochard, R Vanhems, P Rabilloud, M BMC Med Res Methodol Research OBJECTIVES: In most African countries, confirmed COVID-19 case counts underestimate the number of new SARS-CoV-2 infection cases. We propose a multiplying factor to approximate the number of biologically probable new infections from the number of confirmed cases. METHODS: Each of the first thousand suspect (or alert) cases recorded in South Kivu (DRC) between 29 March and 29 November 2020 underwent a RT-PCR test and an IgM and IgG serology. A latent class model and a Bayesian inference method were used to estimate (i) the incidence proportion of SARS-CoV-2 infection using RT-PCR and IgM test results, (ii) the prevalence using RT-PCR, IgM and IgG test results; and, (iii) the multiplying factor (ratio of the incidence proportion on the proportion of confirmed –RT-PCR+– cases). RESULTS: Among 933 alert cases with complete data, 218 (23%) were RT-PCR+; 434 (47%) IgM+; 464 (~ 50%) RT-PCR+, IgM+, or both; and 647 (69%) either IgG + or IgM+. The incidence proportion of SARS-CoV-2 infection was estimated at 58% (95% credibility interval: 51.8–64), its prevalence at 72.83% (65.68–77.89), and the multiplying factor at 2.42 (1.95–3.01). CONCLUSIONS: In monitoring the pandemic dynamics, the number of biologically probable cases is also useful. The multiplying factor helps approximating it. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02077-2. BioMed Central 2023-11-17 /pmc/articles/PMC10655282/ /pubmed/37978439 http://dx.doi.org/10.1186/s12874-023-02077-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Diarra, YM Wimba, PM Katchunga, PB Bengehya, J Miganda, B Oyimangirwe, M Tshilolo, L Ahuka, SM Iwaz, J Étard, JF Écochard, R Vanhems, P Rabilloud, M Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases |
title | Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases |
title_full | Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases |
title_fullStr | Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases |
title_full_unstemmed | Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases |
title_short | Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases |
title_sort | estimating the number of probable new sars-cov-2 infections among tested subjects from the number of confirmed cases |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655282/ https://www.ncbi.nlm.nih.gov/pubmed/37978439 http://dx.doi.org/10.1186/s12874-023-02077-2 |
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