Resilience of mobility network to dynamic population response across COVID-19 interventions: evidences from Chile

TL;DR

This study analyzes how mobility networks in Chile and Spain responded to COVID-19 interventions across two waves, revealing resilience and the potential of historical mobile data to inform future epidemic models.

Contribution

It demonstrates the resilience of mobility networks across pandemic waves and shows how historical mobile data can improve epidemic modeling and intervention strategies.

Findings

Mobility networks remained structurally similar across waves.

Socioeconomic factors significantly influenced mobility responses.

Historical mobility data can inform future epidemic spread models.

Abstract

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.