ArticleScientific reports2026
Enhancing COVID-19 forecasts with a lightweight multi-head depthwise separable convolution network.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To address the challenge of forecasting COVID-19 with limited data, this paper presents CDSCnet (Chunking Depthwise Separable Convolution network). It is a lightweight model designed for small datasets, employing three fixed convolution heads to capture long-term dependencies without relying on recurrent units. Through a comprehensive comparative analysis with existing models, including CNN-LSTM variants, using COVID-19 datasets from 7 countries, such as India, Brazil, and the United States, we demonstrate the superior performance of CDSCnet. Across both the 8 smooth datasets and the 3 high-noise datasets, CDSCnet achieved optimal prediction accuracy, attaining a maximum MAE reduction exceeding 50% against the reproduced results in Spain task. These experimental results confirm that CDSCnet effectively captures the dynamics of epidemic spread, proving its utility as a reliable decision-support tool for COVID-19.
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