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Stochastic Spatial Metapopulation Modelling of HPAI Control and Poultry Restocking on Jolly Island

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A stochastic model of HPAI finds 24 May 2026 as first safe restocking date and a 16.7% burden cut from preventive culling.

desk verdict A clean, honest simulation framework for HPAI control-plus-restocking, but the numbers are scenario products: simulated burden runs ~3.5x the observed 560 outbreaks and no fit to data is shown. read the letter →

arxiv 2608.12956 v1 pith:NZZRIAA4 submitted 2026-08-13 q-bio.PE math.DS

Hammed O. Fatoyinbo , Indranil Ghosh , Parul Tiwari , Peter O. Olanipekun , Afeez Abidemi , Ryan H. L. Ip This is my paper · ORCID
classification q-bio.PEmath.DS MSC 92D30
keywords highlypathogenicavianinfluenzametapopulationmodelstochasticSEIRpreventivecullingconfinementpoultryrestockingepidemicreboundinfectious-farm-days
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to show that epidemic control and post-outbreak recovery for a highly pathogenic avian influenza outbreak can be assessed inside one stochastic, spatially structured model. Using a synthetic island outbreak, it claims that preventive culling lowers mean cumulative burden by about 16.7%, from 16,362.7 to 13,631.9 infectious-farm-days, and that restocking risk falls as the epidemic resolves: under the model's assumptions, 24 May 2026 is the first date whose rebound probability meets the 0.20 threshold. The value of the claim, if true, is operational: a model of this kind could compare culling, confinement, and restocking policies during an active outbreak instead of treating them as separate decisions. The authors are careful that the numbers are scenario-conditional and not a universal calendar rule.

What carries the argument

The carrying object is a stochastic, discrete-time, county–production SEIR metapopulation model: counties are patches, each county–production stratum (Broiler-2, organic duck, Other) has susceptible/exposed/infectious/removed counts, and each county has a shared environmental contamination compartment. The force of infection is the sum of local within-county transmission, environmental exposure weighted by a high-risk-zone hazard multiplier, movement-mediated pressure from recorded farm movements, and spatial pressure through an exponential distance-decay kernel $K_{c,c'} = \exp(-d_{c,c'}/d_0)$ with $d_0 = 2000$ m. New exposures are Poisson draws scaled by the susceptible fraction, progression and recovery are binomial, scheduled reactive and preventive culls remove farms after transitions, and restocking adds susceptible farms up to baseline capacity. The restocking criterion is the rebound probability: the fraction of 200 stochastic runs in which the maximum post-restocking infectious count exceeds the restocking-day count by more than five farms, compared with a prespecified threshold of 0.20.

What would settle it

Estimate the model's parameters from the observed 560 outbreaks, or hold out the later part of the epidemic and forecast it; if the calibrated or out-of-sample epidemic peak, timing, and spatial pattern differ materially from the simulation's, the scenario-based burden reduction and restocking date are not supported. A simpler check is to re-run the restocking analysis with rebound tolerance set to 0 or 10 farms and see whether 24 May 2026 remains the first safe date.

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Extended reading notes

Core claim

The core discovery is the integrated result: a county–production SEIR metapopulation model with four transmission pathways (local, environmental, movement-mediated, and distance-decayed spatial) produces a geographically concentrated epidemic, and in that model control and recovery decisions trade off against each other. All recorded preventive culling reduces ensemble-mean cumulative burden from 16,362.7 to 13,631.9 infectious-farm-days, a 16.7% reduction, with the largest proportional fall among Broiler-2 farms (18.2%) and the smallest among organic ducks (5.1%). Restocking reintroduces susceptible farms; earlier dates create secondary waves, and on 15 March 2026 even restoring 10% of empty capacity leaves rebound probability near 0.64, above the 0.20 threshold. The first candidate date satisfying the threshold is 24 May 2026, with estimated rebound probability 0.180, and capacity-based restocking (adding only a fraction of genuinely empty capacity) lowers cumulative burden by 8.45% and rebound probability from 0.780 to 0.533 relative to applying the fraction to baseline population.

Load-bearing premise

The load-bearing premise is that the hand-set transmission, shedding, decay, spatial, and threshold parameters describe the real outbreak; the paper never calibrates the model to the observed 560 outbreak records, so the 16.7% reduction and the 24 May safe date stand only if those scenario values are right.

Editorial extensions

If this is right

  • Preventive culling across all production classes averts roughly 2,731 infectious-farm-days, so similar models would be expected to show the largest benefit when culling is directed at production classes with the highest burden.
  • Moving confinement earlier on the calendar shrinks the simulated epidemic: peak mean infectious farms rise from 190.30 to 363.73 when the start date moves from 31 December 2025 to 14 February 2026, so delayed confinement is predicted to nearly double the peak.
  • Environmental transmission is a major amplifier in the model: cutting its coefficients to 10% of baseline lowers the peak from 264.52 to 31.03 infectious farms, so interventions that reduce environmental exposure should be a priority.
  • Restocking is safest after the epidemic has largely resolved; under the model assumptions 24 May 2026 is the first date meeting the 0.20 rebound threshold, while even a 10% restock on 15 March 2026 remains risky.
  • Restricting restocking to genuinely empty capacity is predicted to cut rebound risk substantially (from 0.780 to 0.533 on 15 March), but risk stays above threshold, so capacity limits alone do not make early restocking safe.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the 24 May safe date is not a property of the virus but of the chosen scenario parameters and the $\tau=5$ rebound tolerance; a systematic parameter sweep would probably shift the safe window by weeks, so the headline date should be read as a demonstration of method rather than a forecast.
  • Editorial inference: because farms inside a county–production stratum are treated as identical, the model cannot tell whether restocking should favour low-risk farms first; allowing farm-level heterogeneity in biosecurity is a natural test of whether phased restocking could beat the uniform threshold rule.
  • Editorial inference: the rebound metric counts only crossing the restocking-day infectious count by more than five farms; changing that tolerance would change which dates count as safe, and an economic framing could instead pick the date that minimises expected losses from both resurgence and idle capacity.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper presents a stochastic, discrete-time SEIR metapopulation model of HPAI spread among poultry farms on the fictional Jolly Island, using synthetic challenge data. Farms are aggregated by county and production class (Broiler-2, organic duck, Other), with local, environmental, movement-mediated, and distance-dependent transmission, plus reactive/preventive culling, confinement, and restocking. The main reported results are: preventive culling lowers mean cumulative burden from 16,362.7 to 13,631.9 infectious-farm-days (16.7%); earlier and broader confinement reduce epidemic peaks; and under the model assumptions the first restocking date satisfying a rebound-probability threshold of 0.20 is 24 May 2026. The paper repeatedly notes that parameters are scenario values and that outputs are conditional model-based projections.

Significance. The framework is transparent and internally consistent: equations are clearly specified, stochastic runs are paired by seed for policy comparisons, and sensitivity analyses cover confinement timing, environmental transmission, and restocking intensity. The authors are explicit that the transmission coefficients are scenario choices and that the 95% intervals reflect only stochastic variation. If the model were calibrated to the observed epidemic, the integrated treatment of control and restocking would be a useful contribution. As it stands, the quantitative headlines rest on an unvalidated model whose simulated burden appears to exceed the observed burden by a factor of about 3.5, so the significance is primarily as an illustrative methods demonstration rather than an evidence-based decision tool.

major comments (3)
  1. [Section 3.3, Table 2 vs Section 2.2] The mean cumulative burden under all recorded preventive culling, the scenario matching the actual intervention history, is 13,631.9 infectious-farm-days (Table 2). With 560 observed outbreaks and a mean infectious period of 1/γ = 7 days (Table 1), the observed burden is at most 560×7 ≈ 3,920 infectious-farm-days, and probably less because culling truncates infectious periods. The paper never plots simulated against observed incidence or reports any goodness-of-fit statistic. This is load-bearing: if the model overpredicts the residual infectious burden and the March–April 'plateau and modest secondary increase' (§3.1) that is absent from the observed data (§2.2), then the estimated rebound probabilities and the 24 May safe date are shifted conservatively (later) and the 16.7% reduction is a scenario property rather than an evidence-based estimate.
  2. [Section 2.4.5 vs Sections 2.5 and 3.1] The restocking analysis is run with confinement beginning on 14 February 2026, while the baseline confinement used everywhere else (Table 1; Figures 4, 13, 14) begins on 14 January 2026. The safe restocking date and rebound probabilities depend on the epidemic state at the restocking date, which is strongly affected by confinement timing (§3.4.2); the inconsistency must be resolved or justified before the 24 May 2026 result can be reproduced.
  3. [Section 4.3 and Abstract] The paper acknowledges in §4.3 that parameters are scenario values rather than estimated and that intervals exclude parameter uncertainty, but the abstract and introduction nonetheless describe the outputs as supporting 'evidence-based decisions' and state that the model is used to 'reconstruct the epidemic trajectory' (§1). These claims go beyond what an uncalibrated scenario model can support. Either calibrate the model to the observed outbreak series (at minimum, compare simulated and observed daily incidence and adjust parameters) or reframe the paper explicitly as an illustrative scenario analysis and remove the evidence-based language.
minor comments (6)
  1. [Section 2.4.5 and Figures 13-15] The text says 200 runs are the default, but Figures 13-15 each report 100 runs; state the number of runs in the main text for each analysis, not only in captions.
  2. [Section 3.1] The phrase 'As observed in Figure 4' is misleading because Figure 4 shows simulated output; use 'As shown in Figure 4'.
  3. [Table 1] The units for β_within, β_env, β_move, and β_spatial are described only as 'simulation-scale'; give the effective dimensions or an example of the scale of each λ component so that the parameters are interpretable.
  4. [Section 2.4.5] The rebound definition Z_r uses a run-specific baseline I_r(t_r); a fixed tolerance τ=5 is therefore more permissive when restocking occurs at high incidence, so a sensitivity analysis over τ would help readers assess the robustness of the safe-date classification.
  5. [Data Availability] The text mentions the 'Flockbusters' GitHub repository but provides no URL or accession; include one.
  6. [References] Reference [51] appears to be formatted differently from the surrounding references and should be checked.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: parameter values are declared scenario inputs; the safe restocking date and burden reductions emerge from simulation rather than being imposed by fitting.

full rationale

The derivation chain is self-contained. Table 1 introduces the transmission coefficients as "scenario parameters rather than statistically estimated quantities" (§2.5), and §4.3 states "The model parameters were specified as scenario values rather than formally estimated from the observed epidemic." No target outcome—neither the 16.7% burden reduction nor the 24 May 2026 safe date—is used to calibrate any parameter or threshold. The rebound criterion Z_r = 1[max_{t≥t_r} I_r(t) > I_r(t_r)+τ] and the safety threshold α=0.20 are prespecified in §2.4.5, and the safe date emerges from 200-run ensemble probabilities. The preventive-culling comparison varies only the recorded preventive-culling schedule while holding parameters, seeds, and all other assumptions fixed (§2.4.1), so the reduction is a model output, not a fitted quantity. The capacity-based versus original restocking comparison is an algebraic consequence of A_capacity = floor(f·G) ≤ floor(f·N) = A_original, but the paper presents it as a scenario comparison rather than as an independently discovered empirical law, and no fitted value is renamed as a prediction. The self-citations [22,23,51] are background epidemiological references and are not load-bearing for the model equations. The absence of an observed-vs-simulated goodness-of-fit comparison is a correctness and external-validity limitation, not circularity: the paper explicitly labels its quantitative claims as conditional scenario outcomes.

Assumptions & free parameters 11 free parameters · 6 assumptions · 0 invented entities

All quantitative outputs depend on hand-specified scenario parameters and domain aggregation assumptions. No target outcome is used to fit parameters, so circularity is low, but external validity is unestablished.

free parameters (11)
  • Local transmission coefficients β_within_p = (0.30, 0.20, 0.15)
    Hand-chosen scenario values for Broiler-2, organic duck, and Other classes; not estimated from data.
  • Environmental transmission coefficients β_env_p = (0.10, 0.08, 0.15)
    Hand-chosen; sensitivity analysis multiplies by 0.1, 0.5, and 1.0.
  • Environmental shedding coefficients κ_env_p = (0.30, 0.25, 0.40)
    Hand-chosen contamination per infectious farm-day.
  • Movement transmission coefficients β_move_p = (4, 3, 2) × 10^-4
    Hand-chosen per movement-volume unit.
  • Spatial transmission coefficients β_spatial_p = (0.05, 0.04, 0.03)
    Hand-chosen per spatially weighted infectious farm.
  • Environmental decay rate δ_env = 0.20 per day
    Hand-chosen daily proportion of contamination removed.
  • Spatial decay distance d_0 = 2000 m
    Hand-chosen scale of the exponential distance kernel.
  • Latent and infectious period probabilities σ and γ = 1/3 and 1/7 per day
    Assumed mean latent and infectious durations of 3 and 7 days.
  • Restocking fraction f_restock = 0.20 (capacity-based)
    Prespecified primary scenario.
  • Safety threshold α and rebound tolerance τ = 0.20 and 5 farms
    Prespecified decision rule, not derived from optimization.
  • Hazard-risk-zone multiplier h_i = 1.5 inside zone, 1.0 outside
    Assumed farm-level multiplier for environmental exposure.
assumptions (6)
  • domain assumption Farms within a county-production stratum are epidemiologically homogeneous.
    Required to aggregate state variables; stated in Section 2.3.
  • domain assumption Environmental contamination is well-mixed at county level and shared across production classes.
    A single Env_c compartment per county is used; Section 2.3.
  • domain assumption Recorded farm movements represent the movement-mediated transmission network, and unrecorded contacts are excluded.
    Movement tensor M_t,c',c,p is used; Section 2.3.
  • ad hoc to paper The epidemic is seeded only by the first five confirmed farms, with no external introductions afterward.
    Seeding rule in Section 2.3 affects the entire trajectory and restocking window.
  • ad hoc to paper Culling removes farms sequentially from S, E, I, R compartments in that order, as an accounting rule rather than an epidemiological prioritization.
    Section 2.3, acknowledged by the authors.
  • standard math Disease progression and infection counts follow Poisson and Binomial draws with fixed daily probabilities.
    Stochastic process definitions in Section 2.3.

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Pith. "Pith review of Stochastic Spatial Metapopulation Modelling of HPAI Control and Poultry Restocking on Jolly Island." pith.science (2026). https://pith.science/paper/NZZRIAA4

@misc{pith2026260812956,
  author       = {Pith},
  title        = {Pith review of: Stochastic Spatial Metapopulation Modelling of HPAI Control and Poultry Restocking on Jolly Island},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NZZRIAA4}},
  note         = {Machine review of arXiv:2608.12956}
}
read the original abstract

Highly pathogenic avian influenza (HPAI) outbreaks require rapid control during active transmission and evidence-based decisions on the safe restocking of depopulated farms. We developed a stochastic spatial SEIR-based metapopulation model for a synthetic HPAI outbreak on the fictional Jolly Island. Farms were classified as `Broiler-2', `organic duck', or `Other' production systems. The model incorporated local, environmental, movement-mediated, and distance-dependent transmission, together with reactive and preventive culling, production-specific confinement, and capacity-based restocking. The simulated epidemic was geographically concentrated and differed substantially among production classes. Preventive culling reduced mean cumulative burden from 16,362.7 to 13,631.9 infectious-farm-days, with an overall reduction of 16.7\%. Earlier confinement substantially reduced epidemic magnitude, while stronger environmental transmission increased the epidemic peak. Restocking risk declined as the epidemic approached resolution. Under the model assumptions, 24 May 2026 was the first candidate date satisfying the predefined rebound-probability threshold of 0.20. For restocking on 15 March 2026, none of the tested restocking fractions met this criterion. Capacity-based restocking reduced cumulative burden by 8.45\% and rebound probability from 0.780 to 0.533, compared with restocking relative to the baseline population. These findings demonstrate the value of integrating epidemic control and post-outbreak recovery within a single modelling framework. Timely confinement, targeted preventive culling, and phased capacity-based restocking may reduce both epidemic burden and resurgence risk, although operational decisions should also incorporate surveillance, biosecurity, economic considerations, and regulatory requirements.

Figures

Figures reproduced from arXiv: 2608.12956 by the authors.

Figure 1
Figure 1. The main farm animal production was poultry, with laying hens, Stage 1 and 2 broiler chickens, organic meat ducks and conventional meat ducks being the main production systems. Due to the movement of migratory birds, part of the east coast of the island was categorised as the high-risk zone. 40.5°N 41.0°N 41.5°N 42.0°N 42.5°N 29.5°W 29.0°W 28.5°W 28.0°W 27.5°W 27.0°W Species chicken duck Spatial distribution of outb… view at source ↗
Figure 2
Figure 2. Number of daily new confirmed HPAI cases by species over the study period. The three vertical dashed lines indicate the end of the three data release phases [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Locations of infected farms across a selection of days over the study period. The days since the first suspicion (20 Dec 2025) are labelled at the top-left of each plot. Chicken farms are labelled in orange while duck farms are labelled in green. through contaminated surroundings. Transmission was represented through four pathways: local within-county transmission, environmental exposure, movement-mediated transmiss… view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: shows the ensemble-mean number of infectious farms from 22 December 2025 to 30 June 2026 under scenarios with and without all recorded preventive culling. In both scenarios, the number of infectious farms remained relatively low during the initial simulation period bef…
Figure 5
Figure 5. Figure 5: Simulated HPAI epidemic trajectories by production class under scenarios with (blue) and without (red) preventive culling. Panels show Broiler-2 farms (A), organic duck farms (B), and Other production systems (C). Solid lines represent ensemble means, and shaded areas …
Figure 6
Figure 6. Figure 6: Mean cumulative HPAI burden by county under the preventive-culling scenario. Panel (a) shows the overall cumulative burden, summed across production classes and the full simulation period. Panel (b) partitions the county-level burden into contributions from Broiler-2 (…
Figure 7
Figure 7. Figure 7: County-level cumulative HPAI burden across all production classes during consecutive five-week periods. Values represent ensemble mean cumulative infectious-farm-days within each period. The periods were 22 December 2025–25 January 2026, 26 January–1 March 2026, 2 Marc…
Figure 8
Figure 8. Figure 8: County-level cumulative HPAI burden among Broiler-2 farms during consecutive five-week periods. Values represent ensemble mean cumulative infectious-farm-days within each period. The final period, 15–30 June 2026, was shorter than five weeks. The baseline confinement c…
Figure 9
Figure 9. Figure 9: County-level cumulative HPAI burden among organic duck farms during consecutive five-week periods. Values represent ensemble mean cumulative infectious-farm-days within each period. The final period, 15–30 June 2026, was shorter than five weeks. 3.3.1. Production-targe…
Figure 10
Figure 10. Figure 10: County-level cumulative HPAI burden among Other production systems during consecutive five-week periods. Values represent ensemble mean cumulative infectious-farm-days within each period. The final period, 15–30 June 2026, was shorter than five weeks. Without preventi…
Figure 11
Figure 11. Figure 11: Boxplots showing the distribution of cumulative infectious burden under no preventive culling, duck-targeted preventive culling, and chicken-targeted preventive culling under the baseline confinement configuration. Confinement began on 14 January 2026 and interrupted …
Figure 12
Figure 12. Figure 12: Simulated HPAI epidemic trajectories under production-targeted preventive-culling policies and alternative confinement configurations. Each panel compares no preventive culling (black), duck-targeted preventive culling (orange), and chicken-targeted preventive culling…
Figure 13
Figure 13. Figure 13: Simulated epidemic trajectories under alternative production-specific confinement configurations. The scenarios were Broiler-2-only confinement (blue), organic-duck-only confinement (orange), baseline confinement of both Broiler-2 and organic duck farms (green), and f…
Figure 14
Figure 14. Figure 14: shows that delaying confinement increased the magnitude of the simulated epidemic. When confinement began on 31 December 2025, the peak ensemble-mean number of infectious farms was 190.30. Delaying confinement by one week, to 7 January 2026, increased the peak to 229.…
Figure 15
Figure 15. Figure 15: Sensitivity of the simulated epidemic to environmental-transmission strength. The production-specific environmental-transmission coefficients, 𝛽env,𝑝, were multiplied uniformly by 0.1 (purple), 0.5 (yellow), or 1.0 (green), where 1.0 represents the baseline values. Co…
Figure 16
Figure 16. Figure 16: Simulated epidemic trajectories under alternative candidate restocking dates. The black line represents the ensemble-mean epidemic trajectory without restocking. Red trajectories represent candidate dates classified as risky, whereas green trajectories represent dates…
Figure 17
Figure 17. Figure 17: Estimated probability of epidemic rebound across candidate restocking dates. Panel (a) presents the estimated rebound probability and corresponding classification for each date. Black borders identify dates satisfying the predefined safety criterion. Panel (b) shows t…
Figure 18
Figure 18. Figure 18: Sensitivity of epidemic rebound probability to the proportion of available capacity restored on 15 March 2026. Restocking fractions ranged from 10% to 100% of available capacity. The dashed black line indicates the predefined safety threshold of 20%, and the dotted gr…
Figure 19
Figure 19. Figure 19: Comparison of epidemic trajectories under the original and capacity-based restocking formulations. Restocking was implemented on 15 March 2026 at a fraction of 20%. Solid lines represent ensemble-mean infectious trajectories, and shaded areas represent 95% simulation …

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Works this paper leans on

54 extracted references · 43 canonical work pages

  1. [1]

    Anderson, R. M. and May, R. M. (1991).Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford

    work page 1991

  2. [2]

    Allen, L. J. S. (2008). An introduction to stochastic epidemic models. In Brauer, F., van den Driessche, P., and Wu, J. (eds.),Mathematical Epidemiology, pp. 81–130. Springer, Berlin. doi:10.1007/978-3-540-78911-6_3

    doi:10.1007/978-3-540-78911-6_3 2008

  3. [3]

    J., and Vespignani, A

    Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J. J., and Vespignani, A. (2009). Multiscale mobility networks and the spatial spreading of infectious diseases.Proc. Natl. Acad. Sci. U.S.A., 106(51), 21484–21489. doi:10.1073/pnas.0906910106

    doi:10.1073/pnas.0906910106 2009

  4. [4]

    A., and Martínez-López, B

    Belkhiria, J., Alkhamis, M. A., and Martínez-López, B. (2016). Application of species distribution modeling for avian influenza surveillance in the United States considering the North America migratory flyways.Sci. Rep., 6, 33161. doi:10.1038/srep33161

    work page doi:10.1038/srep33161 2016

  5. [5]

    J., Hagenaars, T

    Boender, G. J., Hagenaars, T. J., Bouma, A., Nodelijk, G., Elbers, A. R. W., de Jong, M. C. M., and van Boven, M. (2007). Risk maps for the spread of highly pathogenic avian influenza in poultry.PLoS Comput. Biol., 3(4), e71. doi:10.1371/journal.pcbi.0030071

    doi:10.1371/journal.pcbi.0030071 2007

  6. [6]

    A., Liu, R., and Wu, J

    Bourouiba, L., Gourley, S. A., Liu, R., and Wu, J. (2011). The interaction of migratory birds and domestic poultry and its role in sustaining avian influenza.SIAM J. Appl. Math., 71(2), 487–516. doi:10.1137/100803110

    work page doi:10.1137/100803110 2011

  7. [7]

    Briand,F.X.,Martenot,C.,Massin,P.,Cherbonnel,M.,Orosco,A.,Souchaud,F.,etal.(2026).Re-emergenceofahighlypathogenicavianin- fluenzaH5N1virusofclade2.3.4.4binpoultryinFrance.Infection, Genetics and Evolution,142,105958.doi:10.1016/j.meegid.2026.105958

    work page arXiv 2026

  8. [8]

    Microbiol., 136(1–2), 20–26

    Brown,J.D.,Goekjian,G.,Poulson,R.,Valeika,S.,andStallknecht,D.E.(2009).Avianinfluenzavirusinwater:Infectivityisdependenton pH, salinity and temperature.Vet. Microbiol., 136(1–2), 20–26. doi:10.1016/j.vetmic.2008.10.027. Fatoyinbo et al.:Preprint submitted to ElsevierPage 30 of 32 HPAI Modelling on Jolly Island

    work page doi:10.1016/j.vetmic.2008.10.027 2009

Show all 54 references
  1. [9]

    R., Magstadt, D

    Burrough, E. R., Magstadt, D. R., Petersen, B., Timmermans, S. J., Gauger, P. C., Zhang, J., et al. (2024). Highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States.Emerg. Infect. Dis., 30(7), 1335–1343. doi:10.3...

    2024

  2. [10]

    L., Nooruzzaman, M., Covaleda, L

    Butt, S. L., Nooruzzaman, M., Covaleda, L. M., and Diel, D. G. (2024). Hot topic: Influenza A H5N1 virus exhibits a broad host range, including dairy cows.JDS Commun., 5, S13–S19. doi:10.3168/jdsc.2024-0638

    2024 doi

  3. [11]

    S., Pohlmann, A., Baillie, S

    Caliendo, V., Lewis, N. S., Pohlmann, A., Baillie, S. R., Banyard, A. C., Beer, M., et al. (2022). Transatlantic spread of highly pathogenic avian influenza H5N1 by wild birds from Europe to North America in 2021.Sci. Rep., 12, 11729. doi:10.1038/s41598-022-13447-z

    2022 doi

  4. [12]

    C., Frye, E

    Caserta, L. C., Frye, E. A., Butt, S. L., Laverack, M., Nooruzzaman, M., Covaleda, L. M., et al. (2024). Spillover of highly pathogenic avian influenza H5N1 virus to dairy cattle.Nature, 634, 669–676. doi:10.1038/s41586-024-07849-4

    2024 doi

  5. [13]

    Available at:https://www.cdc.gov/bird-flu/spotlights/H5N1-response-02262025.html(Accessed 15 July 2026)

    CentersforDiseaseControlandPrevention(2025).CDCA(H5N1)birdfluresponseupdateFebruary26,2025.Centers for Disease Control and Prevention. Available at:https://www.cdc.gov/bird-flu/spotlights/H5N1-response-02262025.html(Accessed 15 July 2026)

    2025

  6. [14]

    L., Rattenborg, E., de Jong, M

    Chang, Y., Gonzales, J. L., Rattenborg, E., de Jong, M. C. M., and Conrady, B. (2026). Modeling the spillover risk of highly pathogenic avian influenza from wild birds to cattle in Denmark: A data-driven risk assessment framework.Prev. Vet. Med., 251, 106844. doi:10.1016/j.pre...

    2026

  7. [15]

    Charostad, J., Rukerd, M. R. Z., Mahmoudvand, S., Bashash, D., Hashemi, S. M. A., Nakhaie, M., and Zandi, K. (2023). A comprehensive review of highly pathogenic avian influenza (HPAI) H5N1: An imminent threat at doorstep.Travel Med. Infect. Dis., 55, 102638. doi:10.1016/j.tmai...

    2023

  8. [16]

    Colizza, V., Pastor-Satorras, R., and Vespignani, A. (2007). Reaction-diffusion processes and metapopulation models in heterogeneous networks.Nat. Phys., 3, 276–282. doi:10.1038/nphys560

    2007 doi

  9. [17]

    Dorigatti, I., Mulatti, P., Rosà, R., Pugliese, A., and Busani, L. (2010). Modelling the spatial spread of H7N1 avian influenza virus among poultry farms in Italy.Epidemics, 2(1), 29–35. doi:10.1016/j.epidem.2010.01.002

    2010 doi

  10. [18]

    M., El-Nakeep, S., Shayestegan, F., Tazerji, S

    Duarte, P. M., El-Nakeep, S., Shayestegan, F., Tazerji, S. S., Malik, Y. S., Roncada, P., Tilocca, B., et al. (2024). Addressing the recent transmission of H5N1 to new animal species and humans, warning of the risks and its relevance in One-Health.Ger. J. Microbiol., 4(2), 39–...

    2024 doi

  11. [19]

    A., and Ward, A

    Dunning, J., Firth, J. A., and Ward, A. I. (2025). The spread of highly pathogenic avian influenza virus is a social network problem.PLoS Pathog., 21(7), e1013233. doi:10.1371/journal.ppat.1013233

    2025 doi

  12. [21]

    O., Rivas, A

    Fasina, F. O., Rivas, A. L., Bisschop, S. P., Stegeman, A. J., and Hernandez, J. A. (2011). Identification of risk factors associated with highly pathogenic avian influenza H5N1 virus infection in poultry farms in Nigeria during the epidemic of 2006–2007.Prev. Vet. Med., 98(2–...

    2011 doi

  13. [22]

    Fatoyinbo,H.O.,Tiwari,P.,Ip,R.H.L.,andMiranda,V.(2025).MultivariableanalysisofhighlypathogenicH5N1andH5Nxavianinfluenza in wild birds and poultry in Asian subregions.Comp. Immunol. Microbiol. Infect. Dis., 123, 102387. doi:10.1016/j.cimid.2025.102387

    2025

  14. [23]

    O., Tiwari, P., Olanipekun, P

    Fatoyinbo, H. O., Tiwari, P., Olanipekun, P. O., and Ghosh, I. (2025). A mathematical model of HPAI transmission between dairy cattle and wild birds with environmental effects.arXiv:2508.12201. doi:10.48550/arXiv.2508.12201

    2025 doi arXiv

  15. [24]

    M., Donnelly, C

    Ferguson, N. M., Donnelly, C. A., and Anderson, R. M. (2001). Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain.Nature, 413, 542–548. doi:10.1038/35097116

    2001 doi

  16. [25]

    Garner, M. G. and Hamilton, S. A. (2011). Principles of epidemiological modelling.Rev. Sci. Tech., 30(2), 407–416. doi:10.20506/rst.30.2.2045

    2011 doi

  17. [26]

    Hassman,R.L.,McCabe,I.M.H.,Smith,K.M.,andAllen,L.J.S.(2025).Stochasticmodelsofzoonoticavianinfluenzawithmultiplehosts, environmental transmission, and migration in the natural reservoir.Bull. Math. Biol., 87(1), 1–43. doi:10.1007/s11538-024-01396-9

    2025 doi

  18. [27]

    Iwami, S., Takeuchi, Y., and Liu, X. (2007). Avian–human influenza epidemic model.Math. Biosci., 207(1), 1–25. doi:10.1016/j.mbs.2006.08.001

    2007 doi

  19. [28]

    J., Woolhouse, M

    Keeling, M. J., Woolhouse, M. E. J., Shaw, D. J., Matthews, L., Chase-Topping, M., Haydon, D. T., et al. (2001). Dynamics of the 2001 UK foot and mouth epidemic: Stochastic dispersal in a heterogeneous landscape.Science, 294(5543), 813–817. doi:10.1126/science.1065973

    2001 doi

  20. [29]

    Keeling, M. J. and Rohani, P. (2008).Modeling Infectious Diseases in Humans and Animals. Princeton University Press, Princeton

    2008

  21. [30]

    Kilpatrick,A.M.,Chmura,A.A.,Gibbons,D.W.,Fleischer,R.C.,Marra,P.P.,andDaszak,P.(2006).PredictingtheglobalspreadofH5N1 avian influenza.Proc. Natl. Acad. Sci. U.S.A., 103(51), 19368–19373. doi:10.1073/pnas.0609227103

    2006 doi

  22. [31]

    Lambert, S., Bauzile, B., Mugnier, A., Durand, B., Vergne, T., and Paul, M. C. (2023). A systematic review of mechanistic models used to study avian influenza virus transmission and control.Vet. Res., 54, 96. doi:10.1186/s13567-023-01219-0

    2023 doi

  23. [32]

    What is the pandemic potential of avian influenza A(H5N1)?Lancet Infect

    The Lancet Infectious Diseases (2024). What is the pandemic potential of avian influenza A(H5N1)?Lancet Infect. Dis., 24(5), 437. doi:10.1016/S1473-3099(24)00238-X

    2024 doi

  24. [33]

    Lebarbenchon,C.,Feare,C.J.,Renaud,F.,Thomas,F.,andGauthier-Clerc,M.(2010).Persistenceofhighlypathogenicavianinfluenzaviruses in natural ecosystems.Emerg. Infect. Dis., 16(7), 1057–1062. doi:10.3201/eid1607.090389

    2010

  25. [34]

    Commun., 14, 5489

    Leguia,M.,Garcia-Glaessner,A.,Muñoz-Saavedra,B.,Juarez,D.,Barrera,P.,Calvo-Mac,C.,etal.(2023).Highlypathogenicavianinfluenza A(H5N1) in marine mammals and seabirds in Peru.Nat. Commun., 14, 5489. doi:10.1038/s41467-023-41182-0

    2023 doi

  26. [35]

    Liu,R.,Duvvuri,V.R.S.K.,andWu,J.(2008).SpreadpatternformationofH5N1-avianinfluenzaanditsimplicationsforcontrolstrategies. Math. Model. Nat. Phenom., 3(7), 161–179. doi:10.1051/mmnp:2008048

    2008 doi

  27. [36]

    doi:10.1126/science.1122438

    Olsen,B.,Munster,V.J.,Wallensten,A.,Waldenström,J.,Osterhaus,A.D.M.E.,andFouchier,R.A.M.(2006).Globalpatternsofinfluenza A virus in wild birds.Science, 312(5772), 384–388. doi:10.1126/science.1122438

    2006 doi

  28. [37]

    and Sanad, Y

    Owusu, H. and Sanad, Y. M. (2025). Comprehensive insights into highly pathogenic avian influenza H5N1 in dairy cattle: Transmission dynamics, milk-borne risks, public health implications, biosecurity recommendations, and One Health strategies for outbreak control. Fatoyinbo et...

    2025 doi

  29. [38]

    S., Bunn, D

    Pandit, P. S., Bunn, D. A., Pande, S. A., and Aly, S. S. (2013). Modeling highly pathogenic avian influenza transmission in wild birds and poultry in West Bengal, India.Sci. Rep., 3, 2175. doi:10.1038/srep02175

    2013 doi

  30. [39]

    S., Hicks, J., Pham, A., Morel, G., Murillo, C

    Rawson, T., Morgenstern, C., Knock, E. S., Hicks, J., Pham, A., Morel, G., Murillo, C. A., Sanderson, M. W., Forchini, G., FitzJohn, R., Hauck, K., and Ferguson, N. (2025). A mathematical model of H5N1 influenza transmission in US dairy cattle.Nat. Commun., 16, 4308. doi:10.10...

    2025 doi

  31. [40]

    Riley,S.(2007).Large-scalespatial-transmissionmodelsofinfectiousdisease.Science,316(5829),1298–1301.doi:10.1126/science.1134695

    2007 doi

  32. [41]

    Sah, R., Srivastava, S., Kumar, S., Mehta, R., Donovan, S., Sierra-Carrero, L., et al. (2024). Concerns on H5N1 avian influenza given the outbreak in U.S. dairy cattle.Lancet Reg. Health Am., 35, 100785. doi:10.1016/j.lana.2024.100785

    2024

  33. [42]

    Sangrat,W.,Thanapongtharm,W.,Kasemsuwan,S.,Boonyawiwat,V.,Sajapitak,S.,andPoolkhet,C.(2024).Geospatialandtemporalanalysis ofavianinfluenzariskinThailand:AGIS-basedmulti-criteriadecisionanalysisapproachforenhancedsurveillanceandcontrol.Transbound. Emerg. Dis., 2024(1), 6474182....

    2024 doi

  34. [43]

    J., Bowers, R

    Sharkey, K. J., Bowers, R. G., Morgan, K. L., Robinson, S. E., and Christley, R. M. (2008). Epidemiological consequences of an incursion of highly pathogenic H5N1 avian influenza into the British poultry flock.Proc. R. Soc. B, 275(1630), 19–28. doi:10.1098/rspb.2007.1100

    2008

  35. [44]

    K., and Samanta, G

    Sharma, S., Mondal, A., Pal, A. K., and Samanta, G. P. (2018). Stability analysis and optimal control of avian influenza virus A with time delays.Int. J. Dyn. Control, 6, 1351–1366. doi:10.1007/s40435-017-0379-6

    2018 doi

  36. [45]

    E., Kearney, M

    Stallknecht, D. E., Kearney, M. T., Shane, S. M., and Zwank, P. J. (1990). Effects of pH, temperature, and salinity on persistence of avian influenza viruses in water.Avian Dis., 34(2), 412–418. doi:10.2307/1591429

    1990 doi

  37. [46]

    L., Tana, T., Rawdon, T

    Stanislawek, W. L., Tana, T., Rawdon, T. G., Cork, S. C., Chen, K., Fatoyinbo, H., Cogger, N., et al. (2024). Avian influenza viruses in New Zealand wild birds, with an emphasis on subtypes H5 and H7: Their distinctive epidemiology and genomic properties.PLoS ONE, 19(6), e0303...

    2024 doi

  38. [47]

    Tildesley,M.J.,House,T.A.,Bruhn,M.C.,Curry,R.J.,O’Neil,M.,Allpress,J.L.E.,Smith,G.,andKeeling,M.J.(2010).Impactofspatial clustering on disease transmission and optimal control.Proc. Natl. Acad. Sci. U.S.A., 107(3), 1041–1046. doi:10.1073/pnas.0909047107

    2010 doi

  39. [48]

    Tiensin, T., Nielen, M., Songserm, T., Kalpravidh, W., Chaitaweesub, P., Amonsin, A., et al. (2007). Geographic and temporal distribution of highly pathogenic avian influenza A virus (H5N1) in Thailand, 2004–2005: An overview.Avian Dis., 51(S1), 182–188. doi:10.1637/7635- 042806R.1

    2007 doi

  40. [49]

    Department of Agriculture (2026)

    U.S. Department of Agriculture (2026). HPAI in Poultry.Animal and Plant Health Inspection Service. Available at:https://www.aphis. usda.gov/livestock-poultry-disease/avian/avian-influenza/hpai-poultry(Accessed 31 July 2026)

    2026

  41. [50]

    Vreman, S., Kik, M., Germeraad, E., Heutink, R., Harders, F., Spierenburg, M., Engelsma, M., Rijks, J., van den Brand, J., and Beerens, N. (2023). Zoonotic mutation of highly pathogenic avian influenza H5N1 virus identified in the brain of multiple wild carnivore species. Path...

    2023 doi

  42. [51]

    Fatoyinbo, H. O. and Jeong, H. (2026). Spatiotemporal dynamics and ecological risk factors of high pathogenic avian in- fluenza A(H5N1) in Canadian wildlife: A One Health surveillance analysis.Comp. Immunol. Microbiol. Infect. Dis., 129, 102499. doi:10.1016/j.cimid.2026.102499

    2026

  43. [52]

    and Barr, I

    Wille, M. and Barr, I. G. (2022). Resurgence of avian influenza virus.Science, 376(6592), 459–460. doi:10.1126/science.abo1232

    2022 doi

  44. [53]

    WorldOrganisationforAnimalHealth(2024).Avianinfluenza.Availableat:https://www.woah.org/en/disease/avian-influenza/ (Accessed 15 July 2026)

    2024

  45. [54]

    Ye, X., Lin, S., and Xu, C. (2020). Dynamical analysis of a fractional-order avian-human influenza epidemic model with logistic growth for avian population.J. Algorithms Comput. Technol., 14, 1748302620966704. doi:10.1177/1748302620966704

    2020 doi

  46. [55]

    Modelling Challenge

    WILIMAN-ID Consortium. Modelling Challenge. Available at:https://www.wiliman-id.eu/modelling-challenge/(Accessed 31 July 2026). Fatoyinbo et al.:Preprint submitted to ElsevierPage 32 of 32

    2026

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.