Outfitting the quest for spatial spread of infections: A review of mobility datasets for population health modelling.

Abstract

Understanding the spatial dynamics of infectious disease spread is essential for modeling population health. A key component in such modeling is human mobility data, which informs how infections propagate across time and space. This review provides a comprehensive survey of both real-world and synthetic mobility datasets that have been used in the context of infectious disease modeling. Through the survey, we identified 57 publicly available datasets-52 real-world and 5 synthetic-offering a structured overview of current data sources. Additionally, because real-world data are often inaccessible due to privacy or technical constraints, we provide a concise overview of pseudo-mobility data generation methodologies to contextualize the synthetic datasets and guide future data-production efforts. The review highlights the need to advance synthetic data generation methodologies and improve the accessibility of high-resolution mobility data for future research in this domain.