A frozen-LLM framework with dual-branch token alignment and spatio-temporal prompts beats prior epidemic forecasting models on four COVID-19 datasets.
Infectious Disease Forecasting in India using LLM's and Deep Learning
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abstract
Many uncontrollable disease outbreaks of the past exposed several vulnerabilities in the healthcare systems worldwide. While advancements in technology assisted in the rapid creation of the vaccinations, there needs to be a pressing focus on the prevention and prediction of such massive outbreaks. Early detection and intervention of an outbreak can drastically reduce its impact on public health while also making the healthcare system more resilient. The complexity of disease transmission dynamics, influence of various directly and indirectly related factors and limitations of traditional approaches are the main bottlenecks in taking preventive actions. Specifically, this paper implements deep learning algorithms and LLM's to predict the severity of infectious disease outbreaks. Utilizing the historic data of several diseases that have spread in India and the climatic data spanning the past decade, the insights from our research aim to assist in creating a robust predictive system for any outbreaks in the future.
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EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting
A frozen-LLM framework with dual-branch token alignment and spatio-temporal prompts beats prior epidemic forecasting models on four COVID-19 datasets.