Global and local mobility as a barometer for COVID-19 dynamics.

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• Infectious disease transmission, including COVID-19, is significantly influenced by human interactions and mobility patterns.
• Quantifying these interactions is challenging due to evolving behaviors and regulations.
• Mobility data offers a potential proxy for human activity and contact rates.

Purpose of the Study:

• To investigate the relationship between human mobility (air traffic and driving) and COVID-19 dynamics across ten European countries.
• To determine if mobility data can be used to stratify disease phases and forecast outbreak trends.
• To assess the predictive power of local mobility for short-term COVID-19 forecasting.

Main Methods:

• Analysis of global air traffic data and local driving mobility metrics.
• Correlation analysis between mobility variations and COVID-19 epidemiological data.
• Time-lag analysis to identify the predictive window for mobility indicators.

Main Results:

• A strong coupling was observed between variations in air traffic, driving mobility, and COVID-19 disease dynamics.
• Maximal correlation between local driving mobility and disease dynamics was found with a time lag of approximately [Formula: see text] days.
• Local mobility trends demonstrated the ability to forecast COVID-19 outbreak dynamics up to two weeks in advance.

Conclusions:

• Mobility data, particularly local driving patterns, serves as a valuable indicator for understanding and predicting infectious disease spread.
• Real-time monitoring of mobility trends enables timely adjustments to public health interventions and control strategies.
• This approach offers a data-driven method for proactive management of epidemics like COVID-19.