Systematic analysis of the effectiveness of adding human mobility data to covid-19 case prediction linear models

TL;DR

This paper systematically evaluates the impact of human mobility data on covid-19 case prediction models, finding limited and short-term improvements in predictive accuracy.

Contribution

It provides a comprehensive analysis across datasets and prediction horizons, clarifying the limited benefits of mobility data in covid-19 modeling.

Findings

Mobility data improves predictions only for about two months at the start.

Performance gains are small, with at most 0.3 correlation improvement.

Long-term benefits of mobility data are negligible.

Abstract

Human mobility data has been extensively used in covid-19 case prediction models. Nevertheless, related work has questioned whether mobility data really helps that much. We present a systematic analysis across mobility datasets and prediction lookaheads and reveal that adding mobility data to predictive models improves model performance only for about two months at the onset of the testing period, and that performance improvements -- measured as predicted vs. actual correlation improvement over non-mobility baselines -- are at most 0.3.