Evidence map›Paper›PMID 41911220›Full record

ArticlePLoS computational biology2026

WEPP: Phylogenetic placement achieves near-haplotype resolution in wastewater-based epidemiology.

Pranav Gangwar, Pratik Katte, Manu Bhat, Yatish Turakhia

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Pranav GangwarDepartment of Electrical and Computer Engineering, University of California, San Diego, California, United States of America.ORCID https://orcid.org/0009-0002-9738-900X
Pratik KatteDepartment of Biomolecular Engineering, University of California, Santa Cruz, California, United States of America.
Manu BhatDepartment of Electrical and Computer Engineering, University of California, San Diego, California, United States of America.
Yatish TurakhiaDepartment of Electrical and Computer Engineering, University of California, San Diego, California, United States of America.ORCID https://orcid.org/0000-0001-5600-2900

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

COVID-19HaplotypesPhylogenySARS-CoV-2WastewaterWastewater-Based Epidemiological MonitoringComputational BiologyHumansSoftwareWastewater

Identifiers

PMID41911220
PMCPMC13048486

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.