  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 for public health decision-makers. Our work introduces
PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs)
that reformulates real-time forecasting of disease spread as a text reasoning
problem, with the ability to incorporate real-time, complex, non-numerical
information that previously unattainable in traditional forecasting models.
This approach, through a unique AI-human cooperative prompt design and time
series representation learning, encodes multi-modal data for LLMs. The model is
applied to the COVID-19 pandemic, and trained to utilize textual public health
policies, genomic surveillance, spatial, and epidemiological time series data,
and is subsequently tested across all 50 states of the U.S. Empirically,
PandemicLLM is shown to be a high-performing pandemic forecasting framework
that effectively captures the impact of emerging variants and can provide
timely and accurate predictions. The proposed PandemicLLM opens avenues for
incorporating various pandemic-related data in heterogeneous formats and
exhibits performance benefits over existing models. This study illuminates the
potential of adapting LLMs and representation learning to enhance pandemic
forecasting, illustrating how AI innovations can strengthen pandemic responses
and crisis management in the future.
