The complex networks of social interaction that comprise human societies are highly variable in space and time. Heterogeneity is driven by a combination of seasonally predictable events (e.g., religious migrations; patterns of tourism) and idiosyncratic phenomena (e.g., natural disasters; political upheaval).
Epidemiologists have long appreciated the fundamental relationship between human mobility and infectious disease transmission. Patterns of social interaction influence transmission dynamics within a population, whereas the degree of connectivity between populations governs pathogen dispersal across geographic landscapes.
Most traditional methods of estimating human migration (e.g., cross-border surveys; census data) lack the temporal resolution needed to investigate seasonal patterns of human mobility. Other methods either lack the spatial resolution necessary to quantify sub-regional travel (e.g., air traffic volume data) or require a degree of investment that can be difficult to scale (e.g., GPS data loggers; travel history surveys).
The development of scalable approaches that leverage high-resolution data sources (e.g., remote-sensing technology, anonymized call data records, social media data) to estimate human travel on fine-grained timescales would enhance existing efforts to anticipate patterns of pathogen propagation across space and time.