Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study
Hao Frank Yang,
Hongru Du,
Jianan Zhao
et al.
Abstract:Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performance… Show more
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