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Research Article
Joseph M Kungu
College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Back ground: Corona Virus Disease 2019 (COVID 19) in Uganda was first reported in a male traveler from Dubai on 21st March, 2020 shortly after WHO had announced the condition as a global pandemic. Timely laboratory diagnosis of COVID -19 for all samples from both symptomatic and asymptomatic patients was observed as key in containing the pandemic and breaking the chain of transmission. However, there was a challenge of limited resources required for testing SARS-COV-2 in low and middle income countries. To mitigate this, a study was conducted to evaluate a sample pooling strategy for COVI-19 using real time PCR. The cost implication and the turn around time of pooled sample testing versus individual sample testing were also compared.
Methods: In this study, 1260 randomly selected samples submitted to Uganda Virus Research Institute for analysis were batched in pools of 5, 10, and 15. The pools were then extracted using a Qiagen kit. Both individual and pooled RNA were screened for the SARS-COV-2 E gene using a Berlin kit.
Results: Out of 1260 samples tested, 21 pools were positive in pools of 5 samples, 16 were positive in pools of 10 and 14 were positive in pools of 15 samples. The study also revealed that the pooling strategy helps to save a lot on resources, time and expands diagnostic capabilities without affecting the sensitivity of the test in areas with low SARS-COV-2 prevalence.
Conclusion: This study demonstrated that the pooling strategy for COVID-19 reduced on the turnaround time and there was a substantial increase in the overall testing capacity with limited resources as compared to individual testing.
Keywords
COVID-19, sample pooling, Uganda
Figure 1
No competing interests reported.
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This is a preprint. It has not completed peer review.
Research Article
Joseph M Kungu
College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 15 Mar, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Infectious Diseases
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Back ground: Corona Virus Disease 2019 (COVID 19) in Uganda was first reported in a male traveler from Dubai on 21st March, 2020 shortly after WHO had announced the condition as a global pandemic. Timely laboratory diagnosis of COVID -19 for all samples from both symptomatic and asymptomatic patients was observed as key in containing the pandemic and breaking the chain of transmission. However, there was a challenge of limited resources required for testing SARS-COV-2 in low and middle income countries. To mitigate this, a study was conducted to evaluate a sample pooling strategy for COVI-19 using real time PCR. The cost implication and the turn around time of pooled sample testing versus individual sample testing were also compared.
Methods: In this study, 1260 randomly selected samples submitted to Uganda Virus Research Institute for analysis were batched in pools of 5, 10, and 15. The pools were then extracted using a Qiagen kit. Both individual and pooled RNA were screened for the SARS-COV-2 E gene using a Berlin kit.
Results: Out of 1260 samples tested, 21 pools were positive in pools of 5 samples, 16 were positive in pools of 10 and 14 were positive in pools of 15 samples. The study also revealed that the pooling strategy helps to save a lot on resources, time and expands diagnostic capabilities without affecting the sensitivity of the test in areas with low SARS-COV-2 prevalence.
Conclusion: This study demonstrated that the pooling strategy for COVID-19 reduced on the turnaround time and there was a substantial increase in the overall testing capacity with limited resources as compared to individual testing.
Figure 1
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