21 research outputs found

    Evaluation of the performance of 25 SARS-CoV-2 serological rapid diagnostic tests using a reference panel of plasma specimens at the Uganda Virus Research Institute.

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    INTRODUCTION: Serological testing is needed to better understand the epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Rapid diagnostic tests (RDTs) have been developed to detect specific antibodies, IgM and IgG, to the virus. The performance of 25 of these RDTs was evaluated. METHODS: A serological reference panel of 50 positive and 100 negative plasma specimens was developed from SARS-CoV-2 PCR and antibody positive patients and pre-pandemic SARS-CoV-2-negative specimens collected in 2016. Test performance of the 25 RDTs was evaluated against this panel. RESULTS: A total of 10 RDTs had a sensitivity ≥98%, while 13 RDTs had a specificity ≥98% to anti-SARS-CoV-2 IgG antibodies. Four RDTs (Boson, MultiG, Standard Q, and VivaDiag) had both sensitivity and specificity ≥98% to anti-SARS-CoV-2 IgG antibodies. Only three RDTs had a sensitivity ≥98%, while 10 RDTs had a specificity ≥98% to anti-SARS-CoV-2 IgM antibodies. Three RDTs (Autobio, MultiG, and Standard Q) had sensitivity and specificity ≥98% to combined IgG/IgM. The RDTs that performed well also had perfect or almost perfect inter-reader agreement. CONCLUSIONS: This evaluation identified three RDTs with a sensitivity and specificity to IgM/IgG antibodies of ≥98% with the potential for widespread antibody testing in Uganda

    Field evaluation of the performance of a SARS-CoV-2 antigen rapid diagnostic test in Uganda using nasopharyngeal samples.

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    OBJECTIVES: There is a high demand for SARS-CoV-2 testing to identify COVID-19 cases. Real-time quantitative PCR (qRT-PCR) is the recommended diagnostic test but a number of constraints prevent its widespread implementation, including cost. The aim of this study was to evaluate a low cost and easy to use rapid antigen test for diagnosing COVID-19 at the point of care. METHODS: Nasopharyngeal swabs from suspected COVID-19 cases and low-risk volunteers were tested with the STANDARD Q COVID-19 Ag Test and the results were compared with the qRT-PCR results. RESULTS: In total, 262 samples were collected, including 90 qRT-PCR positives. The majority of samples were from males (89%) with a mean age of 34 years and only 13 (14%) of the positives were mildly symptomatic. The sensitivity and specificity of the antigen test were 70.0% (95% confidence interval (CI): 60-79) and 92% (95% CI: 87-96), respectively, and the diagnostic accuracy was 84% (95% CI: 79-88). The antigen test was more likely to be positive for samples with qRT-PCR Ct values ≤29, with a sensitivity of 92%. CONCLUSIONS: The STANDARD Q COVID-19 Ag Test performed less than optimally in this evaluation. However, the test may still have an important role to play early in infection when timely access to molecular testing is not available but the results should be confirmed by qRT-PCR

    A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

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    The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants

    The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

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    Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

    The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

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    INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

    Viral causes of Influenza Like Illness in Uganda, 2008 to 2017.

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    Introduction: Respiratory pathogens continue to present an ever increasing threat to public health (1,2). Influenza, Respiratory syncytial virus, human metapneumovirus and other respiratory viruses are major etiological agents for influenza like illnesses (ILI) (3-5). Establishment of viral causes of ILI is critical for prevention and mitigation strategies to disease threats. Makerere University Walter Reed Project (MUWRP) together with the Ugandan Ministry of Health and partners undertook surveillance to determine viral causes of influenza-like illness in Uganda.Methods: From 2008, MUWRP established hospital-based sentinel sites for surveillance activities. A total of five hospital-based sites were established, where patients aged 6 months or older presenting with ILI were enrolled. Consents were obtained as required, and a throat and/ or nasopharyngeal swab collected. Samples were screened by PCR for viral causes.Results: From October 2008 to March 2017 a total of 9,472 participants were enrolled in the study from five hospital-based surveillance sentinel sites. Majority of participants were children under 5 years n= 8,169 (86.2%). 615 (6.5%) samples tested positive for influenza A, while 385 (4.1%) tested positive for influenza B viruses and 10 (0.1%) were co-infections between influenza A and B. Of the 2,062 influenza negative samples, results indicated positivity for the following organisms; adenoviruses (9.4%), respiratory syncytial B (7.3%), parainfluenza-3 (4.5%), parainfluenza-1 (4.3%), respiratory syncytial A (3.5%), human bocavirus (1.7%), human metapneumovirus (1.7%), human coronavirus (1.5%), parainfluenza-4 (1.4%) and parainfluenza-2 (0.9%) by PCR.Conclusions: Influenza viruses account for about 11% of the causes of influenza like illness, with influenza A being the dominant type. Among the other viral causes of ILI, adenoviruses were the most dominant. Detection of other viral causes of ILI is an indication of the public health threats posed by respiratory pathogens

    Performance agreement between UVRI and initial testing laboratories.

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    Key: UVRI:Uganda Virus Research Institute. Examina: Examina Medical Laboratory. MAIA: MAIA medicals. T & F: Test and Fly Laboratory. KRRH: Kabale Regional Referral Hospital. S.Day: Same Day Laboratory. BCH: Bwindi Community Hospital. M.SAFE: Medsafe Hospital Limited. GUV: Gulu University multifunctional Laboratory.</p

    Line graphs comparing CT values of N genes of quality control samples from different facilities and the UVRI COVID19 National Reference Laboratory.

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    3A)CT value of Qc samples from Test & fly compared to UVRI, B)CT values of QC samples from Bwindi Community Hospital compared to UVRI, C) CT value of QC samples from MAIA compared to UVRI, D) CT values of QC samples from Mulago NRH compared to UVRI, E)CT values of QC samples from Safari Laboratory compared to that of UVRI, F)CT values of QC samples from Gulu University Laboratory compared to that of UVRI, G)CT values of QC samples from Tenna & Pharma Laboratory compared to that of UVRI.</p

    S1 Data -

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    BackgroundSignificant milestones have been made in the development of COVID19 diagnostics Technologies. Government of the republic of Uganda and the line Ministry of Health mandated Uganda Virus Research Institute to ensure quality of COVID19 diagnostics. Re-testing was one of the methods initiated by the UVRI to implement External Quality assessment of COVID19 molecular diagnostics.Methodparticipating laboratories were required by UVRI to submit their already tested and archived nasopharyngeal samples and corresponding meta data. These were then re-tested at UVRI using the WHO Berlin protocol, the UVRI results were compared to those of the primary testing laboratories in order to ascertain performance agreement for the qualitative & quantitative results obtained. Ms Excel window 12 and GraphPad prism ver 15 was used in the analysis. Bar graphs, pie charts and line graphs were used to compare performance agreement between the reference Laboratory and primary testing Laboratories.ResultsEleven (11) Ministry of Health/Uganda Virus Research Institute COVID19 accredited laboratories participated in the re-testing of quality control samples. 5/11 (45%) of the primary testing laboratories had 100% performance agreement with that of the National Reference Laboratory for the final test result. Even where there was concordance in the final test outcome (negative or positive) between UVRI and primary testing laboratories, there were still differences in CT values. The differences in the Cycle Threshold (CT) values were insignificant except for Tenna & Pharma Laboratory and the UVRI(p = 0.0296). The difference in the CT values were not skewed to either the National reference Laboratory(UVRI) or the primary testing laboratory but varied from one laboratory to another. In the remaining 6/11 (55%) laboratories where there were discrepancies in the aggregate test results, only samples initially tested and reported as positive by the primary laboratories were tested and found to be false positives by the UVRI COVID19 National Reference Laboratory.ConclusionFalse positives were detected from public, private not for profit and private testing laboratories in almost equal proportion. There is need for standardization of molecular testing platforms in Uganda. There is also urgent need to improve on the Laboratory quality management systems of the molecular testing laboratories in order to minimize such discrepancies.</div
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