A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs

TL;DR

This survey reviews recent machine learning approaches, especially Transformer-based models and LLMs, for predicting human mobility during epidemics, highlighting their potential to improve disease spread modeling and response strategies.

Contribution

It provides a comprehensive overview of Transformer and LLM applications in epidemic-related mobility prediction, emphasizing recent advancements and future directions.

Findings

Transformers effectively model complex spatio-temporal mobility patterns.

Pretrained LLMs show promise in epidemic mobility forecasting.

Survey highlights gaps and opportunities in current approaches.

Abstract

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.