JMIR Publications

ABSTRACT

In recent years, a range of novel smart-phone derived data streams about human mobility have become available on a near real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been used as a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic’s importation and transmission routes, or to identify hotspots. However, nearly universally in the literature, the representativeness of these data –how they relate to the underlying real-world human mobility – has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. By analyzing travel trajectories extracted from an exceptionally comprehensive sample of 318 million mobile phone users, representing an entire nation, we found a significant difference in the demographic composition of those who travel and the overall population. We show that this difference strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to non-representativeness for accurate epidemiological surveillance and forecasting.