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Climate-Informed Deep Learning for Spatio-Temporal Forecasting of Climate-Sensitive Diseases

2026-03-24

Abstract excerpt

<h4>Background</h4> Effective public health planning and intervention strategies necessitate an understanding of the temporal and geographic distribution of disease incidences. This requires robust frameworks for disease incidence forecasting. However, due to variations in cases and temporal dynamics, grasping the distinct patterns of climate-sensitive diseases poses significant challenges, including identifying...

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Literature Corpus work
ac318f14-4544-598a-999f-7833c03d55a4
DOI
10.64898/2026.03.20.26348930
Open publication

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