Speaker
Description
Wastewater-based epidemiology (WBE) offers a promising approach to assess populationhealth by analysing health related [SM1] markers in sewage. Interpreting such data at fine spatial scalesrequires accurate [DS2] numbers of the contributing population. However, allocating population information tosewersheds is complicated by the lack of spatial congruence between administrative boundaries and sewernetworks. To date, no standardized method exists for resolving this mismatch.
This study presents and evaluates two novel approaches for estimating sub-sewershed populations:
Proportional Building-based Population Estimation (PBPE) and Spatial Grid Population Estimation(SGPE). PBPE redistributes population data from administrative units proportionally to the number ofresidential buildings intersecting each sub-sewershed, while SGPE applies inverse-distance weighting tointerpolate population density across a hexagonal grid informed by residential land-use data.
Both estimators were implemented for a large German metropolitan area comprising 195 sub-sewersheds.Their performance was assessed by comparing estimated populations [DS3] to reported reference data and toa simple spatial overlay baseline. Despite differing data requirements and assumptions, both methodsproduced consistent and plausible results across all parameters.
PBPE tends to offer higher precision when detailed building data are available, while SGPE provides a flexibleand transferable alternative when such data are incomplete or unavailable. The comparison highlights trade-offs between data granularity, computational complexity, and estimation stability. By providing reproducible andscalable estimation frameworks, this study contributes to improving small-scale population inference for WBEand other spatially disaggregated health applications. The proposed methods enhance the interpretability ofbiometrically relevant indicators derived from wastewater data and support the integration of WBE into publichealth surveillance and environmental epidemiology.
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