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Hybrid SEIR-TFT framework enhances pandemic forecasting with explainable AI

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a hybrid forecasting framework that combines an SEIR epidemiological model with a Temporal Fusion Transformer (TFT) to predict pandemic spread. This approach integrates epidemiological data, intervention signals, mobility, and demographics to provide multi-horizon infection forecasts. By utilizing SHAP analysis, the framework offers transparency into feature contributions and region-specific drivers of disease dynamics, outperforming standalone SEIR models in accuracy and stability. AI

IMPACT This framework offers a more interpretable and accurate approach to pandemic forecasting, potentially aiding public health policy decisions.

RANK_REASON The cluster contains an academic paper detailing a new methodological framework for forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Hybrid SEIR-TFT framework enhances pandemic forecasting with explainable AI

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The cluster contains an academic paper detailing a new methodological framework for forecasting. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ashish Anton Abraham, Gopi Krishnan R, Aiswarya Anil Nair, Ajo Babu George, Kunal Agarwal ·

    Hybrid SEIR Temporal Fusion Transformer Framework with SHAP Analysis for Interpretable Pandemic Forecasting

    arXiv:2610.31673v1 Announce Type: cross Abstract: Forecasting pandemic spread across heterogeneous regions demands models that are both accurate and interpretable for public health action. This paper proposes a hybrid forecasting framework that integrates an SEIR epidemiological …

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