AI RESEARCH
Leveraging graph neural networks and mobility data for COVID-19 forecasting
arXiv CS.LG
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ArXi:2501.11711v2 Announce Type: replace The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of complex spatio-temporal architectures versus simpler temporal baselines remains a subject of debate. Here, we show that structural sparsification of the input graph and temporal granularity are determining factors for the effectiveness of Graph Neural Networks (GNNs