Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study

arXiv:2404.06962v1 Announce Type: new
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 performances and the provision of actionable insights …

abstract arxiv biology case case study challenge complexity covid covid-19 cs.ai cs.lg data demographics disease forecasting intersection language language models large language large language models outbreak pandemic population real-time series study through time series type variables viral