An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance
Seasonal influenza is an annual public health challenge that strains healthcare systems, yet population-level prevalence remains under-reported using standard clinical surveillance methods. Wastewater surveillance (WWS) of influenza A can allow for reliable flu surveillance within a community by lev...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1141136/full |
| _version_ | 1797771033596395520 |
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| author | Tomas de Melo Golam Islam Denina B. D. Simmons Jean-Paul Desaulniers Andrea E. Kirkwood |
| author_facet | Tomas de Melo Golam Islam Denina B. D. Simmons Jean-Paul Desaulniers Andrea E. Kirkwood |
| author_sort | Tomas de Melo |
| collection | DOAJ |
| description | Seasonal influenza is an annual public health challenge that strains healthcare systems, yet population-level prevalence remains under-reported using standard clinical surveillance methods. Wastewater surveillance (WWS) of influenza A can allow for reliable flu surveillance within a community by leveraging existing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) WWS networks regardless of the sample type (primary sludge vs. primary influent) using an RT-qPCR-based viral RNA detection method for both targets. Additionally, current influenza A outbreaks disproportionately affect the pediatric population. In this study, we show the utility of interpreting influenza A WWS data with elementary student absenteeism due to illness to selectively interpret disease spread in the pediatric population. Our results show that the highest statistically significant correlation (Rs = 0.96, p = 0.011) occurred between influenza A WWS data and elementary school absences due to illness. This correlation coefficient is notably higher than the correlations observed between influenza A WWS data and influenza A clinical case data (Rs = 0.79, p = 0.036). This method can be combined with a suite of pathogen data from wastewater to provide a robust system for determining the causative agents of diseases that are strongly symptomatic in children to infer pediatric outbreaks within communities. |
| first_indexed | 2024-03-12T21:31:38Z |
| format | Article |
| id | doaj.art-8ee36a58bec94253abcd457af384447a |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-12T21:31:38Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-8ee36a58bec94253abcd457af384447a2023-07-27T18:40:53ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-07-011110.3389/fpubh.2023.11411361141136An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillanceTomas de MeloGolam IslamDenina B. D. SimmonsJean-Paul DesaulniersAndrea E. KirkwoodSeasonal influenza is an annual public health challenge that strains healthcare systems, yet population-level prevalence remains under-reported using standard clinical surveillance methods. Wastewater surveillance (WWS) of influenza A can allow for reliable flu surveillance within a community by leveraging existing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) WWS networks regardless of the sample type (primary sludge vs. primary influent) using an RT-qPCR-based viral RNA detection method for both targets. Additionally, current influenza A outbreaks disproportionately affect the pediatric population. In this study, we show the utility of interpreting influenza A WWS data with elementary student absenteeism due to illness to selectively interpret disease spread in the pediatric population. Our results show that the highest statistically significant correlation (Rs = 0.96, p = 0.011) occurred between influenza A WWS data and elementary school absences due to illness. This correlation coefficient is notably higher than the correlations observed between influenza A WWS data and influenza A clinical case data (Rs = 0.79, p = 0.036). This method can be combined with a suite of pathogen data from wastewater to provide a robust system for determining the causative agents of diseases that are strongly symptomatic in children to infer pediatric outbreaks within communities.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1141136/fullinfluenza ASARS-CoV-2student absenteeismwastewaterRT-qPCR |
| spellingShingle | Tomas de Melo Golam Islam Denina B. D. Simmons Jean-Paul Desaulniers Andrea E. Kirkwood An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance Frontiers in Public Health influenza A SARS-CoV-2 student absenteeism wastewater RT-qPCR |
| title | An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance |
| title_full | An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance |
| title_fullStr | An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance |
| title_full_unstemmed | An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance |
| title_short | An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance |
| title_sort | alternative method for monitoring and interpreting influenza a in communities using wastewater surveillance |
| topic | influenza A SARS-CoV-2 student absenteeism wastewater RT-qPCR |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1141136/full |
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