Norovirus GII wastewater monitoring for epidemiological surveillance

Jan. 26, 2024
Study appears in PLOS WATER.

In a new study, researchers assessed the comparability of high spatiotemporal resolution HuNoV GII levels in wastewater from 5 WWTPs, to syndromic, outbreak and search trend data over the span of a year.

Their results suggest that wastewater monitoring of HuNoV GII leads or concurs with other epidemiological monitoring methods, but correlations between wastewater and other data sources varied by the degree of overlap between the sewershed and the population catchment of the other data source. For example, the cross correlation values obtained when comparing state syndromic data to wastewater HuNoV GII values varied greatly between WWTPs (0.48 to 0.88). The lowest value was seen for the JS WWTP, which collects from a population of only about 90,000 individuals and represents less than 1% of the state population. JS HuNoV GII wastewater values exhibited a sharp peak in early March, unlike many other WWTPs that had elevated levels over a more extended time period. This combination of low population overlap and pronounced outbreak peak in JS likely accounts for the lower correlation values seen for JS. Similar to the state-level syndromic data, NoroSTAT aggregates clinical data across large geographical areas. There is a broad interest in defining the lead time of wastewater data compared to conventional surveillance data. Due to their poor geographic overlap with community wastewater data, the aggregate state syndromic data and national NoroSTAT data are not ideal for assessing the potential lead times of wastewater data. Although the more conventional epidemiological approaches are valuable and can help with forecasting, wastewater-based surveillance can provide a more focused regional picture of the norovirus cases compared to the state data and NoroSTAT that covers such large areas.

Overall, the results suggest that smaller populations and/or closer overlap between the wastewater and syndromic or case populations results in closer temporal correlation.

Read the journal article here