An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance

Abstract

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 (R_s = 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 (R_s = 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.

Keywords: RT-qPCR; SARS-CoV-2; influenza A; student absenteeism; wastewater.

Figure 1

Results of time-step Spearman's rank (R_s) correlation analysis with a 7-day lag to a 7-day lead. Maximum Spearman's rank correlation for each averaging time highlighted if R_s > 0.50 (strong correlation) or R_s > 0.30 but < 0.50 (moderate correlation) (A) PMMoV-normalized influenza A (M1 gene) viral signal vs. influenza A cases by reported date, (B) PMMoV-normalized influenza A (M1 gene) viral signal vs. % change in elementary school absences due to illness, (C) PMMoV-normalized influenza A (M1 gene) viral signal vs. % change in secondary school absences due to illness, (D) PMMoV-normalized SARS-CoV-2 (N2 gene) viral signal vs. % change in elementary school absences due to illness, and (E) PMMoV-normalized SARS-CoV-2 (N2 gene) viral signal vs. % change in secondary school absences due to illness.

Figure 2

PMMoV-normalized viral wastewater signals vs. clinical surveillance metrics over the study period (1 September 2022 to 13 December 2022). (A) daily PMMoV-normalized influenza A (M1 gene, WHO) viral signal plotted with the number of new influenza A cases by reported date. (B) Daily PMMoV-normalized influenza A (M1 gene, WHO) viral signal plotted with daily % change in elementary school absences due to unspecified illness. (C) Daily averaged PMMoV-normalized influenza A (M1 gene, WHO) viral signal plotted with daily % change in secondary school absences due to unspecified illness. (D) Daily PMMoV-normalized SARS-CoV-2 (N2 gene, CDC) viral signal plotted with daily % change in elementary and secondary school absences due to unspecified illness.