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Predictive power of wastewater for nowcasting infectious disease transmission: a retrospective case study of five sewershed areas in Louisville, Kentucky
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1 01 2024
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Source: Environ Res. 240(Pt 2):117395
[PDF-1.74 MB]
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Alternative Title:Environ Res
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Description:Background
Epidemiological nowcasting traditionally relies on count surveillance data. The availability and quality of such count data may vary over time, limiting representation of true infections. Wastewater data correlates with traditional surveillance data and may provide additional value for nowcasting disease trends.
Methods
We obtained SARS-CoV-2 case, death, wastewater, and serosurvey data for Jefferson County, Kentucky (USA), between August 2020 and March 2021, and parameterized an existing nowcasting model using combinations of these data. We assessed the predictive performance and variability at the sewershed level and compared the effects of adding or replacing wastewater data to case and death reports.
Findings
Adding wastewater data minimally improved the predictive performance of nowcasts compared to a model fitted to case and death data (Weighted Interval Score (WIS) 0.208 versus 0.223), and reduced the predictive performance compared to a model fitted to deaths data (WIS 0.517 versus 0.500). Adding wastewater data to deaths data improved the nowcasts agreement to estimates from models using cases and deaths data. These findings were consistent across individual sewersheds as well as for models fit to the aggregated total data of 5 sewersheds. Retrospective reconstructions of epidemiological dynamics created using different combinations of data were in general agreement (coverage > 75%).
Interpretation
These findings show wastewater data may be valuable for infectious disease nowcasting when clinical surveillance data are absent, such as early in a pandemic or in low-resource settings where systematic collection of epidemiologic data is difficult.
Funding
CDC, Louisville-Jefferson County Metro Government, and other funders.
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Pubmed ID:37838198
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Pubmed Central ID:PMC10863376
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Volume:240
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