  The COVID19 pandemic highlighted the importance of non-traditional data
sources, such as mobile phone data, to inform effective public health
interventions and monitor adherence to such measures. Previous studies showed
how socioeconomic characteristics shaped population response during
restrictions and how repeated interventions eroded adherence over time. Less is
known about how different population strata changed their response to repeated
interventions and how this impacted the resulting mobility network. We study
population response during the first and second infection waves of the COVID-19
pandemic in Chile and Spain. Via spatial lag and regression models, we
investigate the adherence to mobility interventions at the municipality level
in Chile, highlighting the significant role of wealth, labor structure,
COVID-19 incidence, and network metrics characterizing business-as-usual
municipality connectivity in shaping mobility changes during the two waves. We
assess network structural similarities in the two periods by defining mobility
hotspots and traveling probabilities in the two countries. As a proof of
concept, we simulate and compare outcomes of an epidemic diffusion occurring in
the two waves. Our analysis reveals the resilience of the mobility network
across waves. We test the robustness of our findings recovering similar results
for Spain. Finally, epidemic modeling suggests that historical mobility data
from past waves can be leveraged to inform future disease spatial invasion
models in repeated interventions. This study highlights the value of historical
mobile phone data for building pandemic preparedness and lessens the need for
real-time data streams for risk assessment and outbreak response. Our work
provides valuable insights into the complex interplay of factors driving
mobility across repeated interventions, aiding in developing targeted
mitigation strategies.
