Performance agreement between UVRI and initial testing laboratories.

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.

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

Targets and genes reported by platforms of various testing laboratories in Uganda.

Key: ORF1 = open reading frame one. E-gene = Envelope gene. N-gene = Nucleocapsid gene. IC = Internal Control. ORF1ab = Open Reading Frame 1 ab.</p

Real time PCR testing platforms being used for SARS CoV2 detection by various Ugandan laboratories.

Real time PCR testing platforms being used for SARS CoV2 detection by various Ugandan laboratories.</p

S1 Data -

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

Comparison of CT values of samples with discrepant results between the UVRI COVID19 National Reference Laboratory and the primary testing laboratories.

Comparison of CT values of samples with discrepant results between the UVRI COVID19 National Reference Laboratory and the primary testing laboratories.</p

Epidemiology and Surveillance of Influenza Viruses in Uganda between 2008 and 2014

<div><p>Introduction</p><p>Influenza surveillance was conducted in Uganda from October 2008 to December 2014 to identify and understand the epidemiology of circulating influenza strains in out-patient clinic attendees with influenza-like illness and inform control strategies.</p><p>Methodology</p><p>Surveillance was conducted at five hospital-based sentinel sites. Nasopharyngeal and/or oropharyngeal samples, epidemiological and clinical data were collected from enrolled patients. Real-time reverse transcription polymerase chain reaction (RT-PCR) was performed to identify and subtype influenza strains. Data were double-entered into an Epi Info 3.5.3 database and exported to STATA 13.0 software for analysis.</p><p>Results</p><p>Of the 6,628 patient samples tested, influenza virus infection was detected in 10.4% (n = 687/6,628) of the specimens. Several trends were observed: influenza circulates throughout the year with two peaks; the major one from September to November and a minor one from March to June. The predominant strains of influenza varied over the years: Seasonal Influenza A(H3) virus was predominant from 2008 to 2009 and from 2012 to 2014; Influenza A(H1N1)pdm01 was dominant in 2010; and Influenza B virus was dominant in 2011. The peaks generally coincided with times of higher humidity, lower temperature, and higher rainfall.</p><p>Conclusion</p><p>Influenza circulated throughout the year in Uganda with two major peaks of outbreaks with similar strains circulating elsewhere in the region. Data on the circulating strains of influenza and its patterns of occurrence provided critical insights to informing the design and timing of influenza vaccines for influenza prevention in tropical regions of sub-Saharan Africa.</p></div

Average temperatures and monthly number of flu cases.

<p>Average temperatures and monthly number of flu cases.</p

Humidity and monthly number of flu cases.

<p>Humidity and monthly number of flu cases.</p

Rainfall and monthly number of flu cases.

<p>Rainfall and monthly number of flu cases.</p