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arxiv: 2604.18521 · v2 · submitted 2026-04-20 · 💻 cs.LG · cs.AI· q-bio.PE

Recognition: unknown

IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem

Ana I. Bento, Aniruddha Adiga, Anshul Chiranth, Bryan Lewis, Geoffrey Fox, Harry Hochheiser, Jingyuan Chou, Madhav Marathe, Shaun Truelove, Srini Venkatramanan

Pith reviewed 2026-05-10 05:23 UTC · model grok-4.3

classification 💻 cs.LG cs.AIq-bio.PE
keywords infectious disease forecastingbenchmark datasetoutbreak segmentationmachine learning modelsepidemiological time seriesprobabilistic forecastingMLP models
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The pith

A benchmark dataset of over 10,000 segmented infectious disease outbreaks reveals MLP models as most robust for short-term forecasting.

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

The paper introduces IDOBE as a large collection of epidemiological time series data spanning multiple diseases and locations to support standardized evaluation of outbreak forecasting methods. It uses derivative-based segmentation to extract thousands of individual outbreaks from historical records and evaluates 11 baseline models on multi-week ahead predictions during different phases of outbreaks. A sympathetic reader would care because current forecasting often lacks common benchmarks, making it hard to compare methods especially when facing new outbreaks with little prior data. The work shows that machine learning approaches like MLPs perform robustly across the board while statistical methods may have advantages early on.

Core claim

IDOBE compiles epidemiological time series from multiple sources over a century, applies derivative-based segmentation to create over 10,000 outbreaks for 13 diseases with outcomes like cases and hospitalizations, quantifies diversity with information-theoretic measures, and benchmarks 11 models for 1- to 4-week forecasts, finding MLP-based methods have the most robust performance overall with statistical methods slightly better in the pre-peak phase.

What carries the argument

The derivative-based segmentation method that isolates individual outbreaks from continuous time series data by detecting changes in the rate of case increases.

If this is right

  • Standardized benchmarking becomes possible for comparing new forecasting algorithms against established baselines.
  • Focus on pre-peak performance can be prioritized for statistical methods in early detection scenarios.
  • Models can be tested on diverse outbreak shapes and scales from historical data.
  • Probabilistic forecasts can be evaluated using scores like NWIS in addition to point forecast metrics.

Where Pith is reading between the lines

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

  • Releasing the dataset publicly could accelerate development of ensemble methods tailored to outbreak dynamics.
  • Similar segmentation approaches might apply to other time series domains like financial crashes or ecological population booms.
  • Extending to real-time data integration could test how well models handle reporting delays.

Load-bearing premise

The derivative-based segmentation reliably isolates true outbreaks without systematic bias from noise, reporting delays, or varying surveillance intensity.

What would settle it

If re-running the segmentation with added noise or different derivative thresholds produces substantially different sets of outbreaks and changes the relative performance rankings of the models.

Figures

Figures reproduced from arXiv: 2604.18521 by Ana I. Bento, Aniruddha Adiga, Anshul Chiranth, Bryan Lewis, Geoffrey Fox, Harry Hochheiser, Jingyuan Chou, Madhav Marathe, Shaun Truelove, Srini Venkatramanan.

Figure 1
Figure 1. Figure 1: Timeseries corresponding to different diseases. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example segmentation of timeseries into individual outbreaks (pink vertical dashed lines indicate the cutpoints). [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Analytical measures on the IDOBE dataset [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Forecast performance by NWIS pre- and post [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistical and machine learning models have become the norm for real-time forecasting, standardized benchmark datasets for evaluating such methods are lacking. Further, there is limited understanding on performance of these methods for novel outbreaks with limited historical data. In this paper, we propose IDOBE, a curated collection of epidemiological time series focused on outbreak forecasting. IDOBE compiles from multiple data repositories spanning over a century of surveillance and across U.S. states and global locations. We perform derivative-based segmentation to generate over 10,000 outbreaks covering multiple outcomes such as cases and hospitalizations for 13 diseases. We consider a variety of information-theoretic and distributional measures to quantify the epidemiological diversity of the dataset. Finally, we perform multi-horizon short-term forecasting (1- to 4-week-ahead) through the progression of the outbreak using 11 baseline models and report on their performance. In addition to standard metrics such as NMSE and MAPE for point forecasts, we include probabilistic scoring rules such as Normalized Weighted Interval Score (NWIS) to quantify the performance. We find that MLP-based methods have the most robust performance, with statistical methods having a slight edge during the pre-peak phase. IDOBE dataset along with baselines are released publicly on https://github.com/NSSAC/IDOBE to enable standardized, reproducible benchmarking of outbreak forecasting methods.

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 introduces IDOBE, a benchmark dataset for infectious disease outbreak forecasting. It compiles multi-source epidemiological time series spanning over a century, applies derivative-based segmentation to produce over 10,000 outbreak segments for 13 diseases and multiple outcomes (cases, hospitalizations), quantifies diversity with information-theoretic and distributional metrics, evaluates 11 baseline models on 1-4 week ahead forecasting using NMSE, MAPE, and NWIS, and finds MLP methods most robust overall while statistical methods edge out in pre-peak phase. The dataset and baselines are released publicly.

Significance. Should the segmentation be shown to produce representative outbreaks without systematic bias, this work would provide a valuable public resource for standardized benchmarking of forecasting methods, particularly for novel outbreaks with limited data. The scale (>10k segments), diversity quantification, inclusion of probabilistic scoring, and public release are positive aspects that could facilitate reproducible research in epidemic forecasting.

major comments (2)
  1. [Abstract and Methods] Abstract and Methods (segmentation procedure): The derivative-based segmentation to generate over 10,000 outbreaks is presented without validation against known historical outbreaks, sensitivity analysis to noise/reporting delays/surveillance intensity, or details on threshold selection (noted as free parameters). This is load-bearing for the central claim of a reliable benchmark for novel outbreaks, as unvalidated segments risk including spurious events or missing real ones.
  2. [Results] Results (baseline evaluations): The claim that MLP-based methods have the most robust performance (with statistical methods edging pre-peak) is reported without error bars, confidence intervals, or statistical significance tests across segments. Combined with missing implementation details, this makes the performance comparisons only moderately verifiable and weakens support for the empirical conclusions.
minor comments (2)
  1. [Abstract] The abstract states that diversity is quantified but does not specify how the measures address potential inconsistencies in century-spanning multi-source data (e.g., changes in reporting practices).
  2. [Methods] Implementation details for the 11 baselines and exact segmentation code would improve reproducibility, even with the GitHub release.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential value of IDOBE as a standardized benchmark for infectious disease forecasting. We address each major comment point by point below, indicating revisions where appropriate to strengthen the manuscript while maintaining its core contributions.

read point-by-point responses

  1. Referee: [Abstract and Methods] The derivative-based segmentation to generate over 10,000 outbreaks is presented without validation against known historical outbreaks, sensitivity analysis to noise/reporting delays/surveillance intensity, or details on threshold selection (noted as free parameters). This is load-bearing for the central claim of a reliable benchmark for novel outbreaks, as unvalidated segments risk including spurious events or missing real ones.

    Authors: We acknowledge the referee's concern regarding the segmentation procedure's validation. In the revised manuscript, we will expand the Methods section with: (i) explicit details on threshold selection, including the specific values chosen and their epidemiological rationale; (ii) a sensitivity analysis examining the impact of threshold variations, added noise, and simulated reporting delays on the number and characteristics of generated segments; and (iii) qualitative examples demonstrating that the segmentation recovers well-known historical outbreaks for multiple diseases. While exhaustive quantitative validation against every documented outbreak in the literature is beyond the scope of a single revision (given the dataset scale and focus on novel outbreaks), these additions will address the core issues of transparency and robustness. We will also discuss limitations of the approach for surveillance intensity variations. revision: partial

  2. Referee: [Results] The claim that MLP-based methods have the most robust performance (with statistical methods edging pre-peak) is reported without error bars, confidence intervals, or statistical significance tests across segments. Combined with missing implementation details, this makes the performance comparisons only moderately verifiable and weakens support for the empirical conclusions.

    Authors: We agree that the results would benefit from greater statistical rigor and transparency. In the revision, we will add error bars (standard deviation across segments) and confidence intervals to all reported performance metrics (NMSE, MAPE, NWIS). We will also include statistical significance tests (e.g., Wilcoxon signed-rank tests with multiple-comparison correction) to evaluate differences between model classes, particularly for the pre-peak phase comparison. Implementation details for all 11 baselines (hyperparameters, training procedures, and code) will be expanded in the main text and fully documented in the public repository. These changes will make the empirical claims more verifiable and strengthen support for the conclusions. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical data compilation and baseline evaluation

full rationale

The paper compiles time series from external repositories, applies a described derivative-based segmentation procedure to extract >10,000 outbreak segments, computes standard information-theoretic diversity measures on those segments, and evaluates 11 off-the-shelf baseline models using conventional point and probabilistic metrics. No equation or result is obtained by fitting a parameter to the target quantity itself, no self-citation chain supplies a load-bearing uniqueness theorem, and no ansatz is smuggled in to redefine the output as the input. The segmentation step is a preprocessing choice whose validity can be assessed externally; it does not reduce the benchmark's reported performance numbers to a tautology by construction. This is a standard self-contained empirical benchmark paper.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The contribution rests on the quality and consistency of external surveillance repositories plus the validity of the derivative segmentation procedure; no new physical entities are postulated.

free parameters (1)
  • derivative thresholds for segmentation
    Threshold values used to detect outbreak start and end points from time-series derivatives; exact values not stated in abstract.
axioms (1)
  • domain assumption Surveillance data from the compiled repositories accurately reflect underlying incidence without major systematic biases
    The paper aggregates data from multiple repositories but provides no discussion of reporting delays, under-ascertainment, or cross-source inconsistencies.

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