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

arXiv:2404.16921v1 Announce Type: new
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 …

abstract arxiv cs.cl cs.lg diseases epidemic epidemics human llms machine machine learning machine learning techniques mobility modeling paper patterns people prediction survey transformer transformer models transformers type understanding