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Evaluating Population Normalization Methods Using Chemical Data for Wastewater-Based Epidemiology: Insights from a

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Wastewater-based epidemiology (WBE) for tracking diseases like COVID-19 can be improved. Normalizing viral data using chemical parameters like chemical oxygen demand (COD) and biochemical oxygen demand (BOD5) offers a reliable alternative when population data is scarce.

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Area of Science:

  • Environmental microbiology
  • Public health surveillance
  • Epidemiology

Background:

  • Wastewater-based epidemiology (WBE) tracks pathogens but faces challenges correlating data with clinical surveillance.
  • Population variability and environmental factors impact wastewater composition, complicating accurate WBE analysis.

Purpose of the Study:

  • To evaluate different normalization methods for SARS-CoV-2 wastewater data.
  • To compare static population estimates with dynamic normalization using physicochemical parameters (COD, BOD5, NH4-N).

Main Methods:

  • Collected wastewater samples from four WWTPs in Tuscany, Italy (Feb 2021-Mar 2023).
  • Analyzed SARS-CoV-2 loads using static and dynamic normalization techniques.
  • Correlated normalized viral loads with clinical COVID-19 case data.

Main Results:

  • Static normalization showed the highest correlation (ρ = 0.405) with clinical cases.
  • Dynamic normalization using COD and BOD5 yielded strong correlations (ρ = 0.378 each).
  • Normalization with NH4-N was less effective.

Conclusions:

  • Chemical parameters like COD and BOD5 provide a viable alternative for viral normalization in WBE.
  • These methods enhance WBE reliability, especially in resource-limited settings without population or flow rate data.
  • Effective normalization is crucial for improving WBE representativeness and public health applications.