A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs
arxiv(2024)
摘要
This paper provides a comprehensive survey of recent advancements in
leveraging machine learning techniques, particularly Transformer models, for
predicting human mobility patterns during epidemics. Understanding how people
move during epidemics is essential for modeling the spread of diseases and
devising effective response strategies. Forecasting population movement is
crucial for informing epidemiological models and facilitating effective
response planning in public health emergencies. Predicting mobility patterns
can enable authorities to better anticipate the geographical and temporal
spread of diseases, allocate resources more efficiently, and implement targeted
interventions. We review a range of approaches utilizing both pretrained
language models like BERT and Large Language Models (LLMs) tailored
specifically for mobility prediction tasks. These models have demonstrated
significant potential in capturing complex spatio-temporal dependencies and
contextual patterns in textual data.
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