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arxiv: 2605.22352 · v1 · pith:DKSPP3B3new · submitted 2026-05-21 · 🧬 q-bio.PE · math.ST· stat.AP· stat.CO· stat.ME· stat.TH

Spatiotemporal dynamics and ecological risk factors of highly pathogenic avian influenza A(H5N1) in Canadian wildlife: A One Health surveillance analysis

Hammed Olawale Fatoyinbo , Hoyeon Jeong This is my paper

Pith reviewed 2026-05-22 02:02 UTC · model grok-4.3

classification 🧬 q-bio.PE math.STstat.APstat.COstat.MEstat.TH
keywords avian influenzaH5N1wildlife surveillancespatiotemporal dynamicsOne HealthCanadarisk factorsviral lineages
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The pith

Year, season and reassortant Eurasian-North American lineages predict higher H5N1 detection counts in Canadian wildlife.

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

This paper examines 2,657 surveillance records of highly pathogenic avian influenza A(H5N1) in Canadian wildlife from 2022 to 2026. Detections occurred mainly in waterfowl and raptors, with mammals also affected. The highest burden was in 2022 and during autumn and spring seasons, with hotspots in Ontario, Alberta and British Columbia. Negative binomial mixed models show that year, season and lineage are key predictors, with reassortant lineages linked to more detections. These results can guide prioritized surveillance efforts under a One Health approach.

Core claim

The study of Canadian wildlife surveillance data reveals that reassortant Eurasian-North American H5N1 lineages dominate detections and are strongly associated with higher counts, while year and season also serve as significant predictors in statistical models of detection numbers.

What carries the argument

Negative Binomial mixed models that incorporate year, season, and viral lineage as predictors of detection counts across provinces and host groups.

If this is right

  • Surveillance resources should concentrate on Ontario, Alberta, and British Columbia where detection burden was highest.
  • Increased monitoring during autumn and spring migration periods would align with observed seasonal peaks.
  • Key avian groups such as waterfowl and raptors, along with mammalian wildlife, merit focused attention.
  • Reassortant Eurasian-North American lineages should receive priority tracking because of their link to higher counts.
  • Risk-based One Health programs can use these predictors for earlier detection and response.

Where Pith is reading between the lines

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

  • The lineage-count association may reflect higher transmissibility or environmental persistence of reassortant viruses in wild bird populations.
  • Applying the same modeling approach to other North American or Eurasian countries could reveal shared migration-driven risk patterns.
  • Adding finer-scale host movement or contact network data could test whether specific ecological traits drive the observed spatial clusters.
  • Ongoing genomic sequencing of detections would allow tracking whether new reassortants continue to dominate future seasons.

Load-bearing premise

The 2,657 surveillance records accurately represent true detection patterns without major biases from uneven testing effort, reporting differences across provinces, or under-sampling of certain host groups.

What would settle it

A new dataset collected with uniform testing effort across all provinces and host groups that shows no association between reassortant lineages and elevated detection counts would falsify the main modeling result.

read the original abstract

Highly pathogenic avian influenza A(H5N1) has expanded geographically and ecologically, affecting wild birds, mammalian wildlife, domestic animals, and humans. Wildlife surveillance provides critical early warning for One Health preparedness, yet national-scale analyses integrating host ecology, spatial patterns, seasonality, viral lineage, and risk factors remain limited. This study analysed Canadian wildlife HPAI A(H5N1) surveillance records from 2022 to 2026 to characterise spatiotemporal dynamics and identify factors associated with detection counts. A retrospective analysis of 2,657 detections across 13 provinces and territories was conducted using descriptive epidemiology, spatial clustering methods, and Negative Binomial mixed models. Detections were predominantly avian, with waterfowl and raptors as the major host groups, while mammals accounted for a smaller but epidemiologically important proportion. Detection burden was highest in 2022, with increased activity in autumn and spring. Ontario, Alberta, and British Columbia were identified as major hotspots, with evidence of local clustering in parts of the Prairie region. Reassortant Eurasian-North American lineages dominated detections and were strongly associated with higher detection counts. Modelling results identified year, season, and lineage as key predictors. These findings support risk-based One Health surveillance prioritising high-burden regions, migration-associated periods, key avian host groups, reassortant viral lineages, and continued monitoring of mammalian wildlife.

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

1 major / 2 minor

Summary. The manuscript reports a retrospective analysis of 2,657 HPAI A(H5N1) detections in Canadian wildlife from 2022 to 2026. It employs descriptive epidemiology, spatial clustering methods, and Negative Binomial mixed models to characterize spatiotemporal dynamics and identify factors associated with detection counts. Key results include highest burden in 2022, elevated activity in autumn and spring, hotspots in Ontario/Alberta/British Columbia, dominance of reassortant Eurasian-North American lineages, and modelling results identifying year, season, and lineage as predictors with reassortants linked to higher counts. The work is framed as supporting risk-based One Health surveillance.

Significance. If the reported associations prove robust after addressing potential sampling biases, the study delivers useful national-scale evidence on HPAI ecology in wildlife. Integration of spatial clustering with regression on a multi-year, multi-province dataset can help prioritize surveillance in high-burden regions, seasons, and host groups while underscoring the role of reassortant lineages.

major comments (1)
  1. [§2.4 (Negative Binomial mixed models)] §2.4 (Negative Binomial mixed models): The model fitted to the 2,657 records does not include an offset or covariate for surveillance effort (e.g., number of samples submitted per province-year or host group). Without this adjustment, the associations between year, season, lineage and detection counts may be confounded by spatially and temporally varying testing intensity, directly undermining the central claim that these are ecological risk factors rather than sampling artifacts.
minor comments (2)
  1. [Abstract] Abstract: No information is provided on data completeness, model diagnostics, sensitivity analyses, or handling of detection biases, which limits assessment of result reliability.
  2. [Results] Results: Clarify the exact random-effects structure of the mixed model and whether province-level effects were intended to capture varying sampling intensity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We address the major comment on the Negative Binomial mixed models below, with an honest assessment of what can and cannot be revised.

read point-by-point responses

  1. Referee: §2.4 (Negative Binomial mixed models): The model fitted to the 2,657 records does not include an offset or covariate for surveillance effort (e.g., number of samples submitted per province-year or host group). Without this adjustment, the associations between year, season, lineage and detection counts may be confounded by spatially and temporally varying testing intensity, directly undermining the central claim that these are ecological risk factors rather than sampling artifacts.

    Authors: We agree that the absence of an explicit adjustment for surveillance effort represents a genuine limitation in interpreting the model coefficients as purely ecological risk factors. The national surveillance dataset used for this analysis consists of confirmed positive detections and does not contain complete denominator data on total samples submitted (including negatives) stratified by province-year and host group for the full 2022–2026 period. Consequently, we could not incorporate an offset term. The mixed-model specification does include random intercepts for province and year to capture some unobserved heterogeneity in testing practices, but this is only a partial mitigation. In the revised manuscript we will add a new paragraph in the Discussion section that explicitly acknowledges this potential confounding, clarifies that reported associations reflect observed detection patterns rather than incidence rates, and recommends that future work incorporate submission-volume data where available through improved inter-jurisdictional data sharing. revision: partial

Circularity Check

0 steps flagged

No circularity: standard empirical analysis of external surveillance data

full rationale

The paper performs a retrospective analysis of 2,657 external Canadian wildlife HPAI surveillance records using descriptive epidemiology, spatial clustering methods, and Negative Binomial mixed models to identify associations with year, season, and lineage. No derivation chain, first-principles result, or prediction is claimed that reduces by construction to fitted inputs or self-citations. The central claims are data-driven statistical outputs from independent records rather than self-referential definitions or renamings. This is a self-contained empirical study with no load-bearing self-citation or ansatz smuggling.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The analysis rests on standard assumptions of count data modeling and spatial statistics applied to surveillance records; no new entities or ad-hoc parameters are introduced beyond typical negative binomial dispersion.

free parameters (1)
  • dispersion parameter
    Estimated within the negative binomial mixed model to account for overdispersion in detection counts.
axioms (1)
  • domain assumption Surveillance detections reflect underlying ecological patterns without systematic bias from testing effort or reporting.
    Required for interpreting model coefficients as true risk associations.

pith-pipeline@v0.9.0 · 5808 in / 1088 out tokens · 41613 ms · 2026-05-22T02:02:16.697581+00:00 · methodology

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Reference graph

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