  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.
