Skip to content

Nowcasting officially reported highly pathogenic avian influenza outbreak counts under heterogeneous reporting delays

Oct 2026 · Frontiers in Veterinary Science · Vol 13 · 36 references
Data-Driven Disease Surveillance Influenza Virus Research Studies

Abstract

Official animal disease series are often incomplete due to delays in reporting. This retrospective observational study evaluated whether increased model complexity enhances probabilistic nowcasts of eventual officially reported outbreak-equivalent counts. We reconstructed historical information states from a fixed extraction from the World Organisation for Animal Health (WOAH) World Animal Health Information System (WAHIS), which included 46,860 highly pathogenic avian influenza (HPAI) and 72,690 African swine fever (ASF) outbreaks. Twelve prespecified methods were assessed over 78 locked cutoffs for 2024–2025 using the Weighted Interval Score (WIS), calibration diagnostics, block bootstrap, simulation, and a separately frozen USDA APHIS transfer evaluation. The primary HPAI analysis included 35,880 observations from 115 country–host units. At week closure, only 7.6% of eventual HPAI and 6.7% of ASF outbreak equivalents were visible, with completeness varying by host, region, year, season, and workload. The prespecified primary hypothesis was not supported: the delay-aware temporal network scored 6.9% worse than the hierarchical Bayesian comparator, and the relational graph changed WIS by only 0.4%. Gradient-boosted quantile regression improved WIS by 18.5% (absolute difference −0.0397, 95% interval −0.0553 to −0.0255), but the improvement reached 26.0% among high-volume reporters and reversed to a 6.3% deficit among units with minimal reporting history. Conditioning on the partial count was 24.7% worse than a matched no-delay network at cohort age 0 and performed better thereafter. The slower-reporting ASF system reversed the pooled contrast, whereas the APHIS module improved WIS by 10.1% after system-specific retraining. Adding an explicit mass at zero enhanced the transparent comparator by 18.8% and resulted in no detectable difference from gradient boosting; the neural model remained 17.4% worse under the same recalibration. In exploratory analyses, gradient boosting cleared the prespecified 5% gate under all six cohort definitions and all three maturity horizons. Performance deteriorated markedly for high-volume countries and regions excluded from training. These estimates pertain to eventual officially reported outbreak equivalents under the surveillance conditions studied, not undetected infections.

View source

Similar papers

#computer vision Conference Aug 2008

Scrum in a Multiproject Environment: An Ethnographically-Inspired Case Study on the Adoption Challenges

Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...

A. Marchenko, P. Abrahamsson · 59 citations · ⚡11
#computer vision Open access Sep 2012

Making the leap to a software platform strategy: Issues and challenges

A comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms is provided, namely: business challenges, organizational challenges, technical challenges, and people challenges.

Yaser Ghanam, F. Maurer, P. Abrahamsson · 41 citations · ⚡3
#machine learning Open access Mar 2024

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.

Yang Yue, Shu Li, Yihua Cheng et al. · 15 citations

PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction

PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.

Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al. · 13 citations · ⚡1
#data science Open access Oct 2026

Less can be more for predicting properties with large language models

Abstract Predicting properties from coordinate–category data—sets of vectors paired with categorical information—is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been...

Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka · 13 citations

Related blog posts

Microsoft Research Blog Jul 13, 2026

Verifying Rust cryptography in SymCrypt, from standards to code

Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.