Pith Number

pith:Q75UUA2K

pith:2025:Q75UUA2KF45OJB2G37QL3GCK5D

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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models in Epidemiological Research

Jan Hasenauer, Lorenzo Contento, Martin K\"uhn, Nils Wassmuth, Vincent Wieland

Likelihood-free Bayesian methods accurately estimate parameters in stochastic SIS, SIR and SEIR epidemic models from noisy data.

arxiv:2512.02528 v4 · 2025-12-02 · q-bio.QM

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\usepackage{pith}
\pithnumber{Q75UUA2KF45OJB2G37QL3GCK5D}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Our analysis highlights how these likelihood-free methods provide accurate and robust inference capabilities... Results on an Ethiopian cohort study demonstrate operational robustness under real-world noise and irregular data sampling.

C2weakest assumption

That the chosen observation models and noise structures in the simulation study adequately represent the irregularities and biases present in real epidemiological surveillance data.

C3one line summary

Simulation study and Ethiopian cohort data show that particle MCMC and conditional normalizing flows both deliver accurate parameter estimates and forecasts for stochastic compartmental epidemic models with intractable likelihoods.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-12T01:08:18.903384Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

87fb4a034a2f3ae48746dfe0bd984ae8e525370f640f95e90f5772758324fb03

Aliases

arxiv: 2512.02528 · arxiv_version: 2512.02528v4 · doi: 10.48550/arxiv.2512.02528 · pith_short_12: Q75UUA2KF45O · pith_short_16: Q75UUA2KF45OJB2G · pith_short_8: Q75UUA2K

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/Q75UUA2KF45OJB2G37QL3GCK5D \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 87fb4a034a2f3ae48746dfe0bd984ae8e525370f640f95e90f5772758324fb03

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0e7c3368249953d055cc01cde90fc4aad930d2833745a2ac6e89ae696a221974",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "q-bio.QM",
    "submitted_at": "2025-12-02T08:45:31Z",
    "title_canon_sha256": "59b5540b855fd92259f246c5715f99798281eff4170b95e5e056a7c59994f98a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.02528",
    "kind": "arxiv",
    "version": 4
  }
}