SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load
The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Global Health, Epidemiology and Genomics |
| Online Access: | http://dx.doi.org/10.1155/2022/6499217 |
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| author | Martin Skarzynski Erin M. McAuley Ezekiel J. Maier Anthony C. Fries Jameson D. Voss Richard R. Chapleau |
| author_facet | Martin Skarzynski Erin M. McAuley Ezekiel J. Maier Anthony C. Fries Jameson D. Voss Richard R. Chapleau |
| author_sort | Martin Skarzynski |
| collection | DOAJ |
| description | The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be “severe” (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the “mild” category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = −0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values. |
| format | Article |
| id | doaj-art-9def315f32984a029cd9d7d62652acc0 |
| institution | Kabale University |
| issn | 2054-4200 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Global Health, Epidemiology and Genomics |
| spelling | doaj-art-9def315f32984a029cd9d7d62652acc02025-02-03T07:24:27ZengWileyGlobal Health, Epidemiology and Genomics2054-42002022-01-01202210.1155/2022/6499217SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral LoadMartin Skarzynski0Erin M. McAuley1Ezekiel J. Maier2Anthony C. Fries3Jameson D. Voss4Richard R. Chapleau5Booz Allen HamiltonBooz Allen HamiltonBooz Allen HamiltonUS Air Force School of Aerospace MedicineUS Air Force Medical Readiness AgencyUS Air Force School of Aerospace MedicineThe 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be “severe” (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the “mild” category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = −0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.http://dx.doi.org/10.1155/2022/6499217 |
| spellingShingle | Martin Skarzynski Erin M. McAuley Ezekiel J. Maier Anthony C. Fries Jameson D. Voss Richard R. Chapleau SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load Global Health, Epidemiology and Genomics |
| title | SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load |
| title_full | SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load |
| title_fullStr | SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load |
| title_full_unstemmed | SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load |
| title_short | SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load |
| title_sort | sars cov 2 genome based severity predictions correspond to lower qpcr values and higher viral load |
| url | http://dx.doi.org/10.1155/2022/6499217 |
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