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arxiv: 2604.18762 · v1 · submitted 2026-04-20 · 💻 cs.DB

Recognition: unknown

The Public Health and Environmental Surveillance Open Data Model (PHES-ODM) Version 3: An Open, Relational Data Model and Interoperability Framework for Wastewater Surveillance

Carol Bennett, Douglas Manuel, Eugen-Sorin Sion, Janet Lin, Jean-David Therrien, Martin Wellman, Mathew Thomson, Nikho Hizon, Peter Van Rolleghem

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:08 UTC · model grok-4.3

classification 💻 cs.DB
keywords wastewater surveillanceopen data modelinteroperabilitypublic healthdata standardizationenvironmental surveillancerelational databaseFAIR data principles
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The pith

Version 3 of the wastewater surveillance data model resolves interoperability barriers through added tables and mapping capabilities.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper describes version 3 of an open relational data model for wastewater surveillance data. It seeks to fix problems of fragmented systems and inconsistent metadata that limit the long-term usefulness of this surveillance method. Enhancements include new tables for public health actions, links to external data sources, and records of analytical processes, plus tools to map data from other formats and handle complex relationships between data elements. If successful, this would support more transparent and standardized data practices aligned with principles for reusable information. A reader would care because effective data sharing is essential for using wastewater monitoring to track public health trends across regions.

Core claim

The paper claims that its version 3 model overcomes barriers to data utility by introducing new tables for public health actions, external repository linkages, and analytical workflow documentation, while adding support for complex relational linkages across sites, samples, measures, and populations, and providing mapping tools for other data formats. It positions this as a scalable, modular infrastructure that balances robustness with usability and is adaptable to diverse wastewater and environmental surveillance programs, as shown by comparisons to other standards.

What carries the argument

The open relational data model with added tables for public health actions, external linkages, workflow documentation, and mapping tools that handle long and wide formats while supporting relational connections.

If this is right

  • Programs can link data across multiple sites and populations more effectively.
  • Analytical workflows can be documented for improved transparency and reproducibility.
  • Data can be mapped and converted between this model and other standards or formats.
  • Support for long-term, ethical data use in public health surveillance is strengthened.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Adoption could enable better cross-border collaboration on environmental monitoring without needing custom data translations.
  • The structure might be extended to cover additional types of surveillance data beyond wastewater.
  • Over time, this could reduce the effort required to integrate new data sources into public health systems.

Load-bearing premise

That the new tables, linkages, and mapping tools will resolve fragmented data systems and inconsistent practices when adopted and implemented correctly by various programs.

What would settle it

If independent programs adopting the model still face difficulties merging or querying their combined data sets due to unresolved incompatibilities, the improvements have not fully addressed the barriers.

Figures

Figures reproduced from arXiv: 2604.18762 by Carol Bennett, Douglas Manuel, Eugen-Sorin Sion, Janet Lin, Jean-David Therrien, Martin Wellman, Mathew Thomson, Nikho Hizon, Peter Van Rolleghem.

Figure 1
Figure 1. Figure 1: Flow of data in the PHES-ODM from sampling site through sample collection to measurement. Site infor [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Expanded version of Figure 1, including a data stream for polygons and the keys used to link data entities [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Entity relationship diagram illustrating the relational structure of the PHES-ODM, showing primary and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of entity relationship diagrams for the minimal PHES-ODM structure (left) and the full model [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Entity relationship diagram of the phActions table and its integration into the PHES-ODM (Figure 5a), with [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example of the same measurement data in “long” vs “wide” formatting (Figure 6a), and illustration of the [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The PHES-ODM as a Rosetta Stone between wastewater surveillance data standards, with mapping tools [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Entity relationship diagram of the accessions table (Figure 8a), with example data entries illustrating linkages [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Entity relationship diagram of the calculations table with example data entries illustrating multi-step and [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Site-level categorical classification and spatial resolution examples illustrating the new siteLevel field [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Polygon relationship examples showing overlapping and nested spatial geometries, with example data entry [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
read the original abstract

Wastewater surveillance (WWS) has emerged as a valuable tool for public health surveillance, particularly since the COVID-19 pandemic. Its long-term utility is constrained, however, by fragmented data systems, inconsistent metadata practices, and poor interoperability. The Public Health and Environmental Surveillance Open Data Model (PHES-ODM) was developed as an open, collaborative framework to standardize WWS data and support transparent, ethical data use aligned with FAIR principles. Adopted by the Public Health Agency of Canada and adapted by the EU Sewage Sentinel System, the model is now used in over 25 countries. This paper introduces version 3 of the model, which addresses persistent barriers to interoperability and data utility. Key enhancements include new tables for public health actions, external repository linkages (e.g., GISAID, GenBank), and analytical workflow documentation, as well as support for complex relational linkages across sites, samples, measures, and populations. Tools for mapping across other data formats, including PHA4GE and the US CDC National Wastewater Surveillance System, and for supporting long and wide data formats are also introduced. We compare PHES-ODM against six other WWS data standards across 25 features. Balancing robustness with usability, PHES-ODM v3 provides a scalable, modular infrastructure adaptable to diverse WWS and environmental surveillance programs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper presents PHES-ODM Version 3, an open relational data model for wastewater surveillance (WWS). It describes enhancements including new tables for public health actions, external repository linkages (e.g., GISAID, GenBank), analytical workflow documentation, support for complex relational linkages across sites/samples/measures/populations, and mapping tools to other formats such as PHA4GE and US CDC NWSS. The model is positioned as addressing interoperability barriers, with a feature-by-feature comparison to six other WWS standards across 25 features, and notes prior adoption in over 25 countries by agencies including the Public Health Agency of Canada and EU Sewage Sentinel System.

Significance. If the design choices prove effective in practice, PHES-ODM v3 could provide a useful modular, FAIR-aligned infrastructure for standardizing WWS data and supporting ethical sharing across diverse programs. The explicit 25-feature comparison to existing standards is a concrete strength that aids evaluation by potential adopters. Existing real-world adoption of v2 adds practical relevance. However, the assessed significance is constrained by the absence of any demonstrated gains in interoperability or data utility.

major comments (2)
  1. [Abstract] Abstract: The central claim that the new tables for public health actions, external linkages, analytical workflows, complex relational linkages, and mapping tools 'address persistent barriers to interoperability and data utility' is load-bearing for the paper's contribution but rests solely on descriptive design and the feature comparison, with no pilot mappings, before/after completeness metrics, loss-of-information analysis on real WWS datasets, or adoption/usability data from the 25 countries using v2.
  2. [Section describing the comparison to other standards] Section describing the comparison to other standards: The 25-feature comparison is presented as evidence of improvement, yet it remains qualitative and does not report any quantitative evaluation (e.g., successful mapping rates, queryability gains, or metadata consistency improvements) when applying the new v3 elements to formats such as CDC NWSS or PHA4GE.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and for highlighting areas where the strength of evidence for our claims could be clarified. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the new tables for public health actions, external linkages, analytical workflows, complex relational linkages, and mapping tools 'address persistent barriers to interoperability and data utility' is load-bearing for the paper's contribution but rests solely on descriptive design and the feature comparison, with no pilot mappings, before/after completeness metrics, loss-of-information analysis on real WWS datasets, or adoption/usability data from the 25 countries using v2.

    Authors: We agree that the abstract phrasing overstates the evidential basis. This manuscript presents the v3 model specification, its design rationale, and a qualitative feature comparison; it does not contain new empirical evaluations such as pilot mappings or quantitative metrics on real datasets. The reference to adoption in over 25 countries reflects prior use of v2 and is not accompanied by detailed usability data in this paper. We will revise the abstract to state that the enhancements are designed to address interoperability barriers, as indicated by the expanded feature coverage and prior adoption, rather than claiming demonstrated gains. revision: yes

  2. Referee: [Section describing the comparison to other standards] Section describing the comparison to other standards: The 25-feature comparison is presented as evidence of improvement, yet it remains qualitative and does not report any quantitative evaluation (e.g., successful mapping rates, queryability gains, or metadata consistency improvements) when applying the new v3 elements to formats such as CDC NWSS or PHA4GE.

    Authors: The 25-feature comparison is a qualitative matrix that documents presence or absence of support for each feature across seven standards. This format is intended to help readers evaluate coverage without requiring implementation. Quantitative assessments, such as mapping success rates or queryability gains on actual datasets, would require a separate empirical study and are outside the scope of this model-description paper. We will add an explicit statement in the comparison section noting the qualitative nature of the analysis and that quantitative validation is left for future work. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive data model proposal with external comparisons and no derivations or self-referential reductions

full rationale

The paper presents PHES-ODM v3 as an open data model with new tables and mapping tools to improve interoperability in wastewater surveillance. Its central claims rest on feature descriptions, a comparison table against six independent external standards, and references to prior adoptions by agencies like PHAC and EU systems. No equations, predictions, or first-principles derivations exist that could reduce to fitted inputs or self-citations by construction. The model enhancements are presented as design choices rather than outputs derived from the paper's own prior results. External benchmarks and adoption claims provide independent grounding, satisfying the criteria for a self-contained non-circular presentation.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper introduces a data model without free parameters or new physical entities; it relies on standard relational database modeling practices and the domain assumption that standardized open models improve data interoperability in surveillance programs.

axioms (2)
  • standard math Standard relational database principles for linking tables across sites, samples, measures, and populations
    Invoked implicitly in the description of complex relational linkages and table structures.
  • domain assumption FAIR principles as the target for data management
    Stated as the alignment goal for transparent and ethical data use.

pith-pipeline@v0.9.0 · 5582 in / 1324 out tokens · 48497 ms · 2026-05-10T03:08:22.450947+00:00 · methodology

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Reference graph

Works this paper leans on

61 extracted references · 30 canonical work pages

  1. [1]

    I., Almansoori, S., Alameri, S., Adlan, A., Odivilas, G., Chattaway, M

    Singh, S., Ahmed, A. I., Almansoori, S., Alameri, S., Adlan, A., Odivilas, G., Chattaway, M. A., Salem, S. B., Brudecki, G., & Elamin, W. (2024). A narrative review of wastewater surveillance: pathogens of concern, applications, detection methods, and challenges. Frontiers in Public Health, 12 , 1445961. https://doi.org/10.3389/fpubh.2024.1445961

    work page doi:10.3389/fpubh.2024.1445961 2024

  2. [2]

    van der Drift, A., Welling, A., Arntzen, V ., Nagelkerke, E., van der Beek, R., & de Roda Husman, A. (2025). Wastewater surveillance studies on pathogens and their use in public health decision-making: a scoping review. Science of The Total Environment, 993, 179982. https://doi.org/10.1016/j.scitotenv.2025.179982

    work page doi:10.1016/j.scitotenv.2025.179982 2025

  3. [3]

    G., Tariqi, A

    Naughton, C., Roman, F., Alvarado, A. G., Tariqi, A. Q., Deeming, M. A., Kadonsky, K. F., Bibby, K., Bivins, A., Medema, G., Ahmed, W., Katsivelis, P ., Allan, V ., Sinclair, R., & Rose, J. B. (2023). Show us the data: global COVID-19 wastewater monitoring efforts, equity, and gaps. FEMS Microbes, 4 , xtad003. https://doi.org/10.1093/femsmc/xtad003

    work page doi:10.1093/femsmc/xtad003 2023

  4. [4]

    COVIDPoops19. (2024). ArcGIS dashboard. https://www.arcgis.com/apps/dashboards/c778145ea5bb4daeb58d31afee389082

    2024

  5. [5]

    B., Wade, M

    Keshaviah, A., Diamond, M. B., Wade, M. J., & Scarpino, S. V . (2023). Wastewater monitor- ing can anchor global disease surveillance systems. The Lancet Global Health, 11 (6), e976–e981. https://doi.org/10.1016/S2214-109X(23)00170-5

    work page doi:10.1016/s2214-109x(23)00170-5 2023

  6. [6]

    B., Whistler, T., Rando, K., Nwachukwu, C., & Y ousif, M

    Diamond, M. B., Whistler, T., Rando, K., Nwachukwu, C., & Y ousif, M. (2024). Policy dimen- sions of global wastewater surveillance. Bulletin of the World Health Organization, 102 (9), 622–622A. https://doi.org/10.2471/BLT.24.292245

    work page doi:10.2471/blt.24.292245 2024

  7. [7]

    Rockefeller Foundation. (n.d.). Wastewater surveillance . Retrieved March 3, 2026, from https://www.rockefellerfoundation.org/initiatives/wastewater-surveillance/

    2026

  8. [8]

    Bill & Melinda Gates Foundation. (n.d.). Enterics, diagnostics, genomics & epidemiology (EDGE) . Retrieved March 3, 2026, from https://www.gatesfoundation.org/our-work/programs/global-health/enterics- diagnostics-genomics-and-epidemiology

    2026

  9. [9]

    GLOW ACON (Global Consortium for Wastewater and Environmental Surveillance for Public Health). (n.d.). Retrieved March 3, 2026, from https://glowacon.org

    2026

  10. [10]

    G., Amadei, C

    Manuel, D. G., Amadei, C. A., Campbell, J. R., Brault, J.-M., & V eillard, J. (2022). Strengthening public health surveillance through wastewater testing: An essential investment for the COVID-19 pandemic and future health threats. World Bank. https://doi.org/10.1596/36852

    work page doi:10.1596/36852 2022

  11. [11]

    G., Paul, P ., Emerson, C., Grefenstette, J., Wilder, R., Herbst, A

    van Panhuis, W. G., Paul, P ., Emerson, C., Grefenstette, J., Wilder, R., Herbst, A. J., Heymann, D., & Burke, D. S. (2014). A systematic review of barriers to data sharing in public health. BMC Public Health, 14 , 1144. https://doi.org/10.1186/1471-2458-14-1144

    work page doi:10.1186/1471-2458-14-1144 2014

  12. [12]

    Scientific data3(1), 1–9 (2016)

    Wilkinson, M., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

    work page doi:10.1038/sdata.2016.18 2016

  13. [13]

    G., Therrien, J.-D., Thomson, M., Sion, E.-S., Maere, T., Nicolaï, N., V anrolleghem, P

    Manuel, D. G., Therrien, J.-D., Thomson, M., Sion, E.-S., Maere, T., Nicolaï, N., V anrolleghem, P . A., & the PHES-ODM Research Group/Big Life Lab. (2021). PHES-ODM (V ersion 1.0.0)[Computer software]. OSF. https://doi.org/10.17605/OSF.IO/49Z2B

    work page doi:10.17605/osf.io/49z2b 2021

  14. [14]

    G., & V anrolleghem, P

    Therrien, J.-D., Thomson, M., Sion, E.-S., Lee, I., Maere, T., Nicolaï, N., Manuel, D. G., & V anrolleghem, P . A. (2024). A comprehensive, open-source data model for wastewater-based epidemiology. Water Science and Technology, 89(1), 1–19. https://doi.org/10.2166/wst.2023.409

    work page doi:10.2166/wst.2023.409 2024

  15. [15]

    Mathieu, E., Ritchie, H., Rodés-Guirao, L., Appel, C., Gavrilov, D., Giattino, C., Hasell, J., Macdonald, B., Dattani, S., Beltekian, D., Ortiz-Ospina, E., & Roser, M. (2020). COVID-19 pandemic. Our World in Data . Retrieved March 3, 2026, from https://ourworldindata.org/coronavirus

    2020

  16. [16]

    USCDC (United States Centers for Disease Control and Prevention). (n.d.). National Wastewater Surveil- lance System (NWSS). Retrieved March 3, 2026, from https://www.cdc.gov/nwss/index.html

    2026

  17. [17]

    J., Mangat, C

    Joung, M. J., Mangat, C. S., Mejia, E., Nagasawa, A., Nichani, A., Perez-Iratxeta, C., Peterson, S. W., & Champredon, D. (2022). Coupling wastewater-based epidemiological surveillance and modelling of SARS- CoV -2/COVID-19.medRxiv. https://doi.org/10.1101/2022.06.26.22276912

    work page doi:10.1101/2022.06.26.22276912 2022

  18. [18]

    OClair Environnement. (2021). CETo:Connect.Predict.Prevent. Retrieved March 3, 2026, from https://ceto.ca/

    2021

  19. [19]

    Shionogi & Shimadzu. (2025). AdvanSentinel. Retrieved March 3, 2026, from https://advansentinel.com/en

    2025

  20. [20]

    S., & Coe, K

    Pepe, R. S., & Coe, K. (2025). Data dictionaries: Essential tools for the ethical and transparent use of integrated data. International Journal of Population Data Science, 10 (2). https://doi.org/10.23889/ijpds.v10i2.2956

    work page doi:10.23889/ijpds.v10i2.2956 2025

  21. [21]

    D’Ignazio, C., & Klein, L. F. (2020). The numbers don’t speak for themselves. In Data feminism (pp. 36–57). MIT Press

    2020

  22. [22]

    Regenstrief Institute. (n.d.). LOINC. Retrieved March 3, 2026, from https://loinc.org/ 21 A P REPRINT

    2026

  23. [23]

    Harrington, J. L. (2009). Why good design matters. In Relational database design and implementation (3rd ed., pp. 45–50). Morgan Kaufmann

    2009

  24. [24]

    Watt, A. (2014). The entity relationship data model. In Database Design – 2nd Edition (pp. 33–48). BCcam- pus

    2014

  25. [25]

    Helleiner, E. (2024). Economic globalization’s polycrisis. International Studies Quarterly, 68 (2), sqae024. https://doi.org/10.1093/isq/sqae024

    work page doi:10.1093/isq/sqae024 2024

  26. [26]

    PHES-EF. (n.d.). Public Health Environmental Surveillance Evaluation Framework . Retrieved March 3, 2026, from https://phes-ef.org/

    2026

  27. [27]

    European Commission Joint Research Centre. (n.d.). Guidance on wastewater surveillance. EU Wastewater Observatory for Public Health . Retrieved March 3, 2026, from https://wastewater- observatory.jrc.ec.europa.eu/#/guidance/3

    2026

  28. [28]

    Esri. (n.d.). Polygon. GIS Dictionary . Retrieved March 3, 2026, from https://support.esri.com/en-us/gis- dictionary/polygon

    2026

  29. [29]

    Corcho, O., Eriksson, M., Kurowski, K., Ojstersek, M., Choirat, C., van de Sanden, M., & Cop- pens, F. (2021). EOSC interoperability framework. Publications Office of the European Union. https://doi.org/10.2777/620649

    work page doi:10.2777/620649 2021

  30. [30]

    V ogt, L. (2025). The CLEAR principle. Journal of Biomedical Semantics, 16 (1), 18. https://doi.org/10.1186/s13326-025-00340-7

    work page doi:10.1186/s13326-025-00340-7 2025

  31. [31]

    D., McLinden, T., Sereda, P ., Y onkman, A

    Emerson, S. D., McLinden, T., Sereda, P ., Y onkman, A. M., Trigg, J., Peterson, S., Hogg, R. S., Salters, K. A., Lima, V . D., & Barrios, R. (2024). Secondary use of routinely collected administrative health data. International Journal of Population Data Science, 9 (1), 1–12. https://doi.org/10.23889/ijpds.v9i1.2407

    work page doi:10.23889/ijpds.v9i1.2407 2024

  32. [32]

    J., & Berg, M

    Kapitan, D., Heddema, F., Dekker, A., Sieswerda, M., V erhoeff, B. J., & Berg, M. (2025). Data interoperabil- ity in context. Journal of Medical Internet Research, 27 , e66616. https://doi.org/10.2196/66616

    work page doi:10.2196/66616 2025

  33. [33]

    Narayanan, A., Toubiana, V ., Barocas, S., Nissenbaum, H., & Boneh, D. (2012). A critical look at decentral- ized personal data architectures. arXiv. https://arxiv.org/abs/1202.4503

    work page arXiv 2012

  34. [34]

    (2026, February 20)

    Gomstyn, A., & Jonker, A. (2026, February 20). What is data interoperability? IBM Think. Retrieved March 3, 2026, from https://www.ibm.com/think/topics/data-interoperability

    2026

  35. [35]

    Christen, P ., & Schnell, R. (2023). Thirty-three myths and misconceptions about population data. Interna- tional Journal of Population Data Science, 8 (1). https://doi.org/10.23889/ijpds.v8i1.2115

    work page doi:10.23889/ijpds.v8i1.2115 2023

  36. [36]

    H., & Croghan, J

    Wang, X., Williams, C., Liu, Z. H., & Croghan, J. (2019). Big data management challenges in health research. Briefings in Bioinformatics, 20 (1), 156–167. https://doi.org/10.1093/bib/bbx086

    work page doi:10.1093/bib/bbx086 2019

  37. [37]

    NORMAN Network. (2020). SARS-CoV -2 in wastewater (NORMAN Database System). Retrieved March 3, 2026, from https://www.norman-network.com/nds/sars_cov_2/

    2020

  38. [38]

    Global Water Pathogens Project. (2020). Wastewater SPHERE. Retrieved March 3, 2026, from https://sphere.waterpathogens.org/

    2020

  39. [39]

    J., Timme, R

    Griffiths, E. J., Timme, R. E., Mendes, C. I., Page, A. J., Alikhan, N.-F., Fornika, D., Maguire, F., Campos, J., Park, D., Olawoye, I. B., Oluniyi, P . E., Anderson, D., Christoffels, A., Gonçalves da Silva, A., Cameron, R., Dooley, D., Katz, L. S., Black, A., Karsch-Mizrachi, I., . . . MacCannell, D. R. (2022). Future-proofing and maximizing the utility o...

    work page doi:10.1093/gigascience/giac003 2022

  40. [40]

    S., Barclay, C., Cameron, R., Dooley, D., Gill, I., Abraham, D., et al

    Paull, J. S., Barclay, C., Cameron, R., Dooley, D., Gill, I., Abraham, D., et al. (2025). Fixing the plumbing: Building interoperability between wastewater genomic surveillance datasets and systems using the PHA4GE contextual data specification [Preprint]. OSF Preprints. https://doi.org/10.31219/osf.io/z79vk_v1

    work page doi:10.31219/osf.io/z79vk_v1 2025

  41. [41]

    RKI (Robert Koch Institute). (2025). AMELAG technical guide for wastewater surveillance. Retrieved March 3, 2026, from https://www.rki.de/EN/Topics/Research-and-data/Surveillance-panel/Wastewater- surveillance/Guideline.pdf

    2025

  42. [42]

    RKI (Robert Koch Institute), & UBA (Umweltbundesamt (German Federal Environment Agency)). (2026). Wastewater Surveillance AMELAG [Data set]. Zenodo. https://doi.org/10.5281/zenodo.19091863

    work page doi:10.5281/zenodo.19091863 2026

  43. [43]

    Genomic Standards Consortium. (n.d.). Minimum Information about any (x) Sequence (MIxS), Mini- mum Information about any Metagenome or Environmental Sequence (MIMS), Wastewater/Sludge Exten- sion. MIxS:0016013 – Wastewater surveillance environmental package. Retrieved March 3, 2026, from https://genomicsstandardsconsortium.github.io/mixs/0016013/

    work page arXiv 2026

  44. [44]

    D’Aoust, P .M., Hegazy, N., Ramsay, N.T. et al. SARS-CoV -2 viral titer measurements in Ontario, Canada wastewaters throughout the COVID-19 pandemic. Sci Data 11, 656 (2024). https://doi.org/10.1038/s41597- 024-03414-w

    work page doi:10.1038/s41597- 2024

  45. [45]

    (n.d.-a)

    PHES-ODM (Public Health Environmental Surveillance Open Data Model). (n.d.-a). Wide-names. PHES- ODM documentation. Retrieved March 11, 2026, from https://docs.phes-odm.org/wide-names.html

    2026

  46. [46]

    (n.d.-a)

    Big Life Lab. (n.d.-a). PHES-ODM Mapper [Computer Software]. GitHub. Retrieved March 3, 2026, from https://github.com/Big-Life-Lab/PHES-ODM-Mapper 22 A P REPRINT

    2026

  47. [47]

    Alshehri, M. (n.d.). Background. EpiWeeks documentation . Retrieved March 3, 2026, from https://epiweeks.readthedocs.io/en/stable/background.html

    2026

  48. [48]

    I., Chowdhury, F., Calderwood, S

    Levade, I., Khan, A. I., Chowdhury, F., Calderwood, S. B., Ryan, E. T., Harris, J. B., LaRocque, R. C., Bhuiyan, T. R., Qadri, F., Weil, A. A., & Shapiro, B. J. (2021). A combination of metagenomic and cultiva- tion approaches reveals hypermutator phenotypes within Vibrio cholerae–infected patients. mSystems, 6(4), e00889-21. https://doi.org/10.1128/mSyst...

    work page doi:10.1128/msystems.00889-21 2021

  49. [49]

    J., et al

    N’Guessan, A., Tsitouras, A., Sanchez-Quete, F., Goitom, E., Reiling, S. J., et al. (2022). Detection of prevalent SARS-CoV -2 variant lineages in wastewater and clinical sequences from cities in Québec, Canada [Preprint]. medRxiv. https://doi.org/10.1101/2022.02.01.22270170

    work page doi:10.1101/2022.02.01.22270170 2022

  50. [50]

    K., D’Aoust, P

    Hegazy, N., Peng, K. K., D’Aoust, P . M., et al. (2025). V ariability of clinical metrics in small population communities. ACS ES&T Water , 5(4), 1605–1619. https://doi.org/10.1021/acsestwater.4c00958

    work page doi:10.1021/acsestwater.4c00958 2025

  51. [51]

    (2025, September 29)

    USCDC (United States Centers for Disease Control and Prevention). (2025, September 29). About wastewa- ter data. Retrieved March 3, 2026, from https://www.cdc.gov/nwss/about-data.html

    2025

  52. [52]

    W., Kaiser, K

    Brown, A. W., Kaiser, K. A., & Allison, D. B. (2018). Issues with data and analyses. Proceedings of the National Academy of Sciences, 115 (11), 2563–2570. https://doi.org/10.1073/pnas.1708279115

    work page doi:10.1073/pnas.1708279115 2018

  53. [53]

    (n.d.-b)

    PHES-ODM (Public Health Environmental Surveillance Open Data Model). (n.d.-b). PHES-ODM validation documentation. Retrieved March 3, 2026, from https://validate-docs.phes-odm.org/

    2026

  54. [54]

    (n.d.-c)

    PHES-ODM (Public Health Environmental Surveillance Open Data Model). (n.d.-c). PHES-ODM validator. Retrieved March 3, 2026, from https://validate.phes-odm.org/

    2026

  55. [55]

    (n.d.-b)

    Big Life Lab. (n.d.-b). PHES-ODM sharing library. GitHub. Retrieved March 3, 2026, from https://github.com/Big-Life-Lab/PHES-ODM-sharing

    2026

  56. [56]

    PHES-ODM (Public Health Environmental Surveillance Open Data Model). (2026). PHES-ODM documen- tation. Retrieved March 3, 2026, from https://docs.phes-odm.org/

    2026

  57. [57]

    (n.d.-d)

    PHES-ODM (Public Health Environmental Surveillance Open Data Model). (n.d.-d). PHES- ODM video resources [Video collection]. Vimeo. Retrieved March 3, 2026, from https://vimeo.com/user/126292027/folder/6496228

    work page arXiv 2026

  58. [58]

    (n.d.-e)

    PHES-ODM (Public Health Environmental Surveillance Open Data Model). (n.d.-e). ODM discourse forum. Retrieved March 3, 2026, from https://odm.discourse.group/latest

    2026

  59. [59]

    (n.d.-c)

    Big Life Lab. (n.d.-c). PHES-ODM issues. GitHub. Retrieved March 3, 2026, from https://github.com/Big- Life-Lab/PHES-ODM/issues

    2026

  60. [60]

    COVID-19 Data Portal. (n.d.). Partners and working groups. Retrieved March 3, 2026, from https://www.covid19dataportal.org/partners?activeTab=Working%20groups

    2026

  61. [61]

    WHO (World Health Organization). (n.d.). Wastewater and environmental surveillance (WES). Retrieved March 3, 2026, from https://www.who.int/teams/environment-climate-change-and-health/water-sanitation- and-health/sanitation-safety/wastewater 23

    2026