Wastewater-based epidemiology (WBE) is a cost-effective, unbiased, and time-efficient tool for public health surveillance. Although widely adopted since the COVID-19 pandemic, WBE remains underutilized in genomic epidemiology, as most tools are limited to lineage-level resolution and focus only on estimating lineage abundances from wastewater sequencing reads. Here, we present WEPP, a pathogen-agnostic pipeline that improves both the resolution and capabilities of WBE analysis. WEPP uses phylogenetic placement of sequencing reads onto mutation-annotated trees (MATs)-daily updated phylogenies of all globally available clinical sequences and their inferred ancestors-to sensitively and precisely identify a subset of haplotypes likely present in a sample. It also reports the abundance of each haplotype and lineage, and flags "unaccounted alleles"- those found in the sample but not explained by selected haplotypes-that may indicate novel variants. WEPP includes a powerful interactive dashboard for high-resolution visual analysis, allowing users to explore haplotype and lineage abundances, read-to-haplotype mappings, and unaccounted alleles within a global phylogenetic context. Applied to wastewater samples from multiple cities and pathogens, WEPP uncovered biological insights sometimes missed by other tools and enabled new WBE applications previously confined to clinical sequencing, such as identifying (i) intra-lineage haplotype clusters, (ii) multiple cluster introductions in a city, (iii) early haplotype detection up to five weeks before clinical confirmation, (iv) mutations from novel variants, and (v) circulating lineages missed by clinical surveillance. With these capabilities, WEPP can transform wastewater-based epidemiology into a more powerful tool for monitoring and managing infectious disease outbreaks.

Existing symmetry discovery methods predominantly focus on global transformations across the entire system or space, but they fail to consider the symmetries in local neighborhoods. This may result in the reported symmetry group being a misrepresentation of the true symmetry. In this paper, we formalize the notion of local symmetry as atlas equivariance. Our proposed pipeline, automatic local symmetry discovery (AtlasD), recovers the local symmetries of a function by training local predictor networks and then learning a Lie group basis to which the predictors are equivariant. We demonstrate AtlasD is capable of discovering local symmetry groups with multiple connected components in top-quark tagging and partial differential equation experiments. The discovered local symmetry is shown to be a useful inductive bias that improves the performance of downstream tasks in climate segmentation and vision tasks. Our code is publicly available at https://github.com/Rose-STL-Lab/AtlasD.