{
  "id": 18478,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1886896",
  "title": "Predicting influenza in the post-COVID era: assessing LSTM, GRU, and transformer robustness to covariate shift",
  "summary": "Forecasting influenza has become increasingly challenging due to post-COVID disruptions in seasonality and strain circulation. This work compares the performance of Long Short Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and transformer models in forecasting influenza spread using multivariate epidemiological and environmental data, with a focus on robustness under post-COVID non-stationarity. We compare LSTM, GRU, and transformer architectures within a multivariate deep learning fra",
  "authors": "Atiqa Naeem Alam Din",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T00:00:00.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/18478",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1886896",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}