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arxiv: 2605.06168 · v1 · submitted 2026-05-07 · 📊 stat.AP

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Scalable model selection for count time series with structural breaks: application to solid-organ transplantation during and after COVID-19 in the USA and Italy

Elena Del Sordo, Emiliano Ceccarelli, Emilio Porcu, Francesca Puoti, Giovanna Jona Lasinio, Giuseppe Iuppa, Libia Lara-Carrion, Silvia Testa, Silvia Trapani, Tobia Filosi

Pith reviewed 2026-05-08 03:37 UTC · model grok-4.3

classification 📊 stat.AP
keywords count time seriesstructural breaksmodel selectionsolid organ transplantationCOVID-19forecastingPoisson regressionnegative binomial
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The pith

Solid organ transplant donation counts follow unconditional time series, with pandemic shifts captured by pre-specified level or slope indicators and negligible added value from COVID burden covariates.

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

The paper fits Poisson and negative-binomial count time-series models to weekly solid organ transplant data from the USA and Italy, incorporating autoregressive dynamics, holiday effects, and binary or ramp indicators for the pandemic period. Candidate models are screened in a fixed portfolio and chosen by BIC within successive training windows, then evaluated on expanding-window forecasts at 4-, 8-, and 12-week horizons using RMSE, interval coverage, and width. Across donor and organ strata the selected specifications absorb the pandemic disruption and show deceased-donor series returning toward pre-pandemic baselines in both countries while the US living-donor series converges more slowly; auxiliary COVID incidence and mortality variables contribute almost no incremental predictive power beyond the autoregressive and calendar components.

Core claim

Donation time series behave as unconditional processes whose structural breaks are adequately represented by a small set of pre-specified pandemic-period indicators; once those breaks are included, further covariates for COVID burden or mortality add statistically negligible forecast improvement, allowing attention to shift to post-pandemic operational factors such as hospital capacity and public perception.

What carries the argument

BIC selection over a pre-defined portfolio of Poisson and negative-binomial count models that embed short-term autoregression, calendar dummies, and pandemic level or slope indicators.

If this is right

  • Deceased-donor activity in both countries can be expected to track pre-pandemic baselines once the initial pandemic shift is accounted for.
  • US living-donor series require allowance for a slower post-pandemic return when planning capacity.
  • Forecasts at 4- to 12-week horizons remain well calibrated without external COVID burden data.
  • Model selection can be performed scalably by restricting attention to the autoregressive-plus-calendar-plus-break specification class.

Where Pith is reading between the lines

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

  • If the series are truly unconditional, transplant-system resilience depends more on internal operational factors than on real-time external pandemic metrics.
  • The same BIC-screened count-model framework could be applied directly to other healthcare count processes that experienced one-time system shocks.
  • Public-health interventions aimed at hospital staffing or donor registration campaigns may now be more relevant than further COVID-specific adjustments.

Load-bearing premise

The pre-specified pandemic-period level and slope indicators are sufficient to capture all relevant structural breaks, and BIC-selected models generalize reliably out of sample.

What would settle it

A statistically significant reduction in out-of-sample RMSE or improvement in 95 % predictive-interval coverage when COVID incidence or mortality covariates are added to the BIC-selected baseline model would falsify the claim of negligible incremental value.

Figures

Figures reproduced from arXiv: 2605.06168 by Elena Del Sordo, Emiliano Ceccarelli, Emilio Porcu, Francesca Puoti, Giovanna Jona Lasinio, Giuseppe Iuppa, Libia Lara-Carrion, Silvia Testa, Silvia Trapani, Tobia Filosi.

Figure 1
Figure 1. Figure 1: Number of weekly transplants in the USA from deceased ((a),(c),(e)) and living view at source ↗
Figure 2
Figure 2. Figure 2: Number of weekly transplants in Italy from deceased ((a),(c),(e)) and living view at source ↗
Figure 3
Figure 3. Figure 3: USA; (a) Observed (continuous) and estimated (dashed) weekly deaths by all causes. view at source ↗
Figure 4
Figure 4. Figure 4: Italy; (a) Observed (continuous) and estimated (dashed) weekly deaths by all causes. view at source ↗
Figure 5
Figure 5. Figure 5: USA’s SOT from deceased donors: coefficients estimates on the exponential scale view at source ↗
Figure 6
Figure 6. Figure 6: Italy’s SOT from deceased donors: coefficients estimates on the exponential scale view at source ↗
Figure 7
Figure 7. Figure 7: USA’s SOT from living donors: coefficients estimates on the exponential scale view at source ↗
Figure 8
Figure 8. Figure 8: Italy’s SOT from living donors: coefficients estimates on the exponential scale ((a) view at source ↗
read the original abstract

Weekly healthcare activity data are typically non-negative counts with temporal dependence and occasional system-wide disruptions, settings in which Gaussian time-series models may be inadequate. Solid organ transplant (SOT) activity provides a representative case study of a count process affected by a large external shock. We analyse weekly SOT counts in the USA and Italy from 2014 to October 2024, stratified by donor type (deceased vs living) and organ (kidney and liver). We fit Poisson and negative-binomial count time-series models incorporating short-term dynamics, calendar effects (holiday weeks), and pre-specified pandemic-period level and/or slope indicators. Candidate specifications are screened within a pre-defined portfolio and selected using BIC within each training window. Forecasting performance is evaluated with an expanding-window design at horizons $h\in\{4,8,12\}$ weeks. Alongside RMSE, we report empirical coverage of nominal $95\%$ predictive intervals and interval widths to summarise calibration and forecast uncertainty. Across strata, selected models capture substantial pandemic-period deviations and varying post-period trajectories. Deceased-donor series are broadly consistent with a return towards pre-pandemic baselines in both countries, whereas the US living-donor series shows a more gradual convergence in this application. Within the explored model class and validation protocol, auxiliary covariates representing COVID burden and mortality add limited incremental predictive contribution beyond autoregressive and calendar components. Our analysis shows that donation time series represent an unconditional phenomenon, with auxiliary variables having a statistically negligible impact on donations, thus allowing a focus on more practical aspects related to ongoing challenges in the post-pandemic era, such as hospital overloads and changes in public perception.

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 / 2 minor

Summary. The manuscript presents a protocol for scalable model selection in count time series subject to structural breaks, using Poisson and negative-binomial specifications that include autoregressive terms, calendar effects, and pre-specified pandemic-period level and/or slope indicators. Candidate models are screened in a fixed portfolio and chosen by BIC within each training window; performance is assessed via expanding-window forecasts at horizons 4, 8, and 12 weeks, reporting RMSE, 95% predictive-interval coverage, and interval width. Applied to weekly solid-organ transplant counts (deceased vs. living donors, kidney and liver) in the USA and Italy (2014–October 2024), the selected models indicate substantial pandemic deviations with varying post-pandemic trajectories; auxiliary COVID-burden and mortality covariates add negligible incremental value, supporting the claim that donation series behave as an unconditional process.

Significance. If the central empirical findings hold, the work supplies a transparent, reproducible workflow for handling count data with known external shocks in healthcare settings, together with concrete evidence on post-COVID recovery patterns that can guide operational planning around hospital capacity and public-perception factors. The expanding-window design, multi-metric evaluation, and explicit cross-country, cross-organ stratification are genuine strengths that enhance applicability.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (model specification): the claim that auxiliary covariates exert 'statistically negligible impact' and that the series are 'unconditional' is load-bearing for the policy conclusion, yet rests on the pre-specified single pandemic-level and/or slope indicators being sufficient to absorb all break structure. No alternative break specifications (multiple change points, data-driven detection, or time-varying coefficients) are reported; if residual wave-specific or intensity-varying effects remain, the auxiliaries could still improve fit and forecasts once the break component is relaxed.
  2. [§4] §4 (forecast evaluation): the expanding-window RMSE, coverage, and width results are computed after BIC selection that includes the pre-specified indicators; this design does not isolate whether the auxiliaries would remain negligible if the break structure were allowed to be more flexible or if the indicators were omitted entirely.
minor comments (2)
  1. [Table 1] Table 1 (or equivalent data summary): the exact number of training windows and the precise start/end dates of each expanding-window evaluation should be stated explicitly so that the reported coverage and width statistics can be reproduced.
  2. [§3] Notation: the negative-binomial dispersion parameter is listed among the free parameters but its estimation method (profile likelihood, joint MLE, etc.) is not detailed; a brief sentence would remove ambiguity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below, clarifying the rationale for our pre-specified break approach while acknowledging limitations in scope.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (model specification): the claim that auxiliary covariates exert 'statistically negligible impact' and that the series are 'unconditional' is load-bearing for the policy conclusion, yet rests on the pre-specified single pandemic-level and/or slope indicators being sufficient to absorb all break structure. No alternative break specifications (multiple change points, data-driven detection, or time-varying coefficients) are reported; if residual wave-specific or intensity-varying effects remain, the auxiliaries could still improve fit and forecasts once the break component is relaxed.

    Authors: Our protocol is explicitly designed around pre-specified pandemic indicators to capture known, system-wide shocks in a scalable and interpretable manner suitable for healthcare count data. The candidate portfolio includes models both with and without these indicators; BIC selection within each training window favors the break-inclusive specifications, indicating that they absorb the primary structural changes. Within this class, the auxiliary covariates add negligible predictive value. We did not examine data-driven multiple change points or time-varying coefficients, as these would depart from the transparent, pre-specified framework emphasized in the paper. We will add clarifying language in the abstract and §3 to state that conclusions hold conditional on the explored break specifications. revision: partial

  2. Referee: [§4] §4 (forecast evaluation): the expanding-window RMSE, coverage, and width results are computed after BIC selection that includes the pre-specified indicators; this design does not isolate whether the auxiliaries would remain negligible if the break structure were allowed to be more flexible or if the indicators were omitted entirely.

    Authors: The expanding-window protocol evaluates the full model-selection workflow as it would be applied in practice, where pandemic timing is known a priori and the selection procedure can choose to include or exclude the indicators. Because the portfolio contains indicator-free models and these are not selected by BIC, the results already indicate that the break terms are required for good performance. A separate evaluation that forces omission of the indicators or substitutes alternative break structures was not performed, as it lies outside the pre-specified protocol. We will insert a brief discussion in §4 noting this boundary condition on the reported negligible impact of auxiliaries. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines a pre-specified portfolio of Poisson/negative-binomial models that include fixed pandemic level/slope indicators plus autoregressive and calendar terms, screens candidates by BIC on each training window, and evaluates forecasts via independent expanding-window out-of-sample metrics (RMSE, coverage, width). The claim that auxiliary COVID covariates add limited incremental value is an empirical result obtained inside that fixed model class and validation protocol; it does not reduce by construction to the inputs, nor does it rely on self-citation load-bearing, uniqueness theorems, or renaming of known results. The out-of-sample design separates selection from evaluation, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 0 invented entities

The analysis rests on standard assumptions for count distributions and pre-specified break indicators whose effects are estimated from data; no new entities are postulated.

free parameters (3)
  • Pandemic level and slope coefficients
    Pre-specified indicators whose values are fitted within each training window to capture COVID breaks.
  • Autoregressive coefficients
    Short-term dynamics parameters estimated from data in selected models.
  • Negative binomial dispersion parameter
    Fitted to handle overdispersion in count data.
axioms (2)
  • domain assumption Weekly transplant counts follow a Poisson or negative binomial distribution conditional on the mean structure including autoregressive terms, calendar effects, and pandemic indicators.
    Core modeling assumption stated in the abstract for the candidate specifications.
  • domain assumption BIC provides a reliable basis for selecting models that perform well in out-of-sample forecasting within this class.
    Used to screen specifications inside each training window.

pith-pipeline@v0.9.0 · 5648 in / 1459 out tokens · 51091 ms · 2026-05-08T03:37:29.890890+00:00 · methodology

discussion (0)

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