Abstract:
Changing patterns of human aggregation are thought to drive
annual and multiannual outbreaks of infectious diseases, but the
paucity of data about travel behavior and population flux over time
has made this idea difficult to test quantitatively. Current measures
of human mobility, especially in low-income settings, are often
static, relying on approximate travel times, road networks, or cross sectional surveys. Mobile phone data provide a unique source of
information about human travel, but the power of these data to
describe epidemiologically relevant changes in population density
remains unclear. Here we quantify seasonal travel patterns using
mobile phone data from nearly 15 million anonymous subscribers in
Kenya. Using a rich data source of rubella incidence, we show that
patterns of population travel (fluxes) inferred from mobile phone
data are predictive of disease transmission and improve significantly
on standard school term time and weather covariates. Further,
combining seasonal and spatial data on travel from mobile phone
data allows us to characterize seasonal fluctuations in risk across
Kenya and produce dynamic importation risk maps for rubella.
Mobile phone data therefore offer a valuable previously unidenti fied source of data for measuring key drivers of seasonal epidemics.
