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Community movement and COVID-19: a global study using Google's Community Mobility Reports
1Institute of Tropical Medicine, Eberhard Karls University, University Clinics Tübingen, Wilhelmstr. 27, 72074, Tübingen, Germany.
Epidemiology and Infection
|November 13, 2020
Summary
Google Community Mobility Reports (CMR) reveal a negative correlation between reduced mobility and COVID-19 cases in many regions. Integrating this mobility data significantly improved disease modeling and prediction accuracy.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- The COVID-19 pandemic necessitated understanding factors influencing disease spread.
- Google's Community Mobility Reports (CMR) provide granular data on population movement changes during the pandemic.
Purpose of the Study:
- To investigate the association between community mobility data and COVID-19 incidence.
- To determine if mobility data can enhance infectious disease modeling and prediction.
Main Methods:
- Cross-correlation analysis of global CMR data with COVID-19 confirmed case numbers.
- Development and comparison of epidemiological models with and without CMR data integration.
- Utilizing Bayesian Information Criteria for model selection and evaluation.
Main Results:
- A significant negative correlation was observed between reduced mobility and COVID-19 case incidence in Europe and North America.
- This negative correlation was consistent across most continents, excluding South America.
- Models incorporating CMR data demonstrated superior performance in explaining and predicting case numbers compared to traditional models.
Conclusions:
- Community mobility data is a valuable predictor of COVID-19 transmission dynamics.
- Integrating mobility data into epidemiological models significantly enhances their accuracy and predictive power.
- Distributed lag models incorporating mobility data offer the most robust predictions for future COVID-19 trends.
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