JMIR Publications

ABSTRACT

Background:

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.

Objective:

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.

Methods:

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.

Results:

We show that this difference strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics.

Conclusions:

Our findings imply that it is necessary to measure and quantify the inherent biases related to non-representativeness for accurate epidemiological surveillance and forecasting.