Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning

This is the code for our paper "Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning".

ABSTRACT:

Understanding the complex interplay between human behavior, disease transmission, and non-pharmaceutical interventions during the COVID-19 pandemic could provide valuable insights to focus future public health efforts. Cell phone mobility data offers a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate aggregated and anonymized mobility data which measures how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas, and Washington since the beginning of the pandemic. Using manifold learning techniques, we show that a low-dimensional embedding enables the identification of patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, cluster geographically, reveal subpopulations that likely migrated out of urban areas, and, importantly, link to COVID-19 case counts. The analysis and approach provide local epidemiologists a framework for interpreting mobility data and behavior to inform policy makers’ decision-making aimed at curbing the spread of COVID-19.