dPCR-First Wastewater Epidemiology: Harmonized State-Space Models that Respect Assay Physics

Lison et al. (2025) show that normal/log-normal error assumptions are violated for environmental dPCR; they offer a dPCR-specific likelihood that handles concentration-dependent noise and non-detects. McLeod et al. (2025) focus on real-time outlier detection in dPCR data. We propose a unifying state-space framework that: (a) ingests raw droplet counts via the Lison et al. likelihood, (b) flags and downweights anomalies using McLeod et al.-style online outlier detection, and (c) introduces cross-lab, cross-instrument harmonization using ideas borrowed from CT lung density bias correction (simultaneous correction for volume/noise/scanner bias in Radiology, 2024). The model yields platform-calibrated latent concentration trajectories that can be linked to incidence via mechanistic shedding/transport submodels. This is novel relative to current wastewater workflows because it (i) enforces the correct measurement model end-to-end, (ii) systematically handles non-detects and outliers in real time, and (iii) corrects inter-lab bias. The result is sharper outbreak tracking and fairer comparisons across jurisdictions—crucial when wastewater guides public health resource allocation.

References:

1. Real-time outlier detection in digital PCR data for wastewater-based pathogen surveillance. R. E. McLeod, T. R. Julian, T. Stadler, A. Lison (2025). medRxiv.
2. Improving inference in environmental surveillance by modeling the statistical features of digital PCR. A. Lison, Timothy R. Julian, T. Stadler (2025). bioRxiv.
3. Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction.. Gonzalo Vegas Sánchez-Ferrero, A. Diaz, S. Ash, D. Baraghoshi, Matthew Strand, J. Crapo, Edwin K. Silverman, S. Humphries, G. Washko, David A. Lynch, R. San José Estépar (2024). Radiology.