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First identification of the SARS-COV-2/XBB.1.5 sublineage among indigenous COVID-19 cases through the influenza sentinel surveillance system in Niger.

ABSTRACT: The emergence of the Omicron variant in November 2021, has caused panic worldwide due to the rapid evolution and the ability of the virus to escape the immune system. Since, several Omicron sublineages (BA.1 to BA.5) and their descendent recombinant lineages have been circulating worldwide. Furthermore, in December 2022, a new Omicron subvariant XBB.1.5 characterized by an unusual mutation in the spike protein evolved in the United States and rapidly spread to the other continents. Our study reports on the first cases of XBB.1.5 sublineage among indigenous Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV-2) positive cases detected through the influenza sentinel surveillance system in Niger. All influenza suspected cases were tested for both influenza and SARS-COV-2 using the Centre for Disease Control and prevention (CDC) Influenza SARS-COV-2 Multiplex quantitative Reverse-Transcription Polymerase Chain Reaction (qRT-PCR) Assay. SARS-COV-2 positive samples with cycle threshold ≤28 were selected for whole genome sequencing subsequently using the Oxford Nanopore Midnight protocol with rapid barcoding on a MinIon MK1B device. A total of 51 SARS-COV-2 positive samples were confirmed between December 2022 and March 2023. We successfully obtained 19 sequences with a predominance of the XBB.1/XBB.1.5 sublineages (73.7 %). In addition, a recombinant XBD sequence was also first-ever identified in early March 2023. Our findings support the need to strengthen the influenza sentinel surveillance for routine Coronavirus Disease 2019 (COVID-19) surveillance and SARS-COV-2 variants monitoring in Niger.

SUBMITTER: Lagare A

PROVIDER: S-EPMC10637901 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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First identification of the SARS-COV-2/XBB.1.5 sublineage among indigenous COVID-19 cases through the influenza sentinel surveillance system in Niger.

Lagare Adamou A Faye Martin M Issa Moussa M Hamidou Oumou O Bienvenu Baruani B Mohamed Abdoulkarim A Aoula Balki B Moumouni Katoumi K Hassane Fatima F Otto Younoussa Adamou YA Tambwe Didier D K DDK Tassiou Elh Ibrahim EI Seini Haoua H Faye Ousmane O Jambou Ronan R

Heliyon 20231019 11

The emergence of the Omicron variant in November 2021, has caused panic worldwide due to the rapid evolution and the ability of the virus to escape the immune system. Since, several Omicron sublineages (BA.1 to BA.5) and their descendent recombinant lineages have been circulating worldwide. Furthermore, in December 2022, a new Omicron subvariant XBB.1.5 characterized by an unusual mutation in the spike protein evolved in the United States and rapidly spread to the other continents. Our study rep ...[more]

PMID: 37954288

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Project description:BackgroundLittle is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa.ObjectiveWe aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance.MethodsFrom April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness.ResultsOf the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82).ConclusionsInfluenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance.

Dataset Information

First identification of the SARS-COV-2/XBB.1.5 sublineage among indigenous COVID-19 cases through the influenza sentinel surveillance system in Niger.

Publications

First identification of the SARS-COV-2/XBB.1.5 sublineage among indigenous COVID-19 cases through the influenza sentinel surveillance system in Niger.

Similar Datasets

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