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Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.
Amanda My Chu1, Jacky Nl Chan2, Jenny Ty Tsang1
1Department of Social Sciences, The Education University of Hong Kong, Hong Kong, China (Hong Kong).
JMIR Public Health and Surveillance
|March 12, 2021
Summary
Analyzing global flight data reveals that network connectedness, visualized through travel patterns, can serve as an early warning system for pandemics like COVID-19. High network density in early March 2020 signaled the rapid spread of the virus.
Area of Science:
- Public Health Surveillance
- Epidemiology
- Network Science
Background:
- Communicable diseases, including COVID-19, represent a significant global public health challenge.
- Effective control requires timely surveillance and prediction of pandemic risks.
- Analyzing travel data offers a novel approach to understanding disease spread dynamics.
Purpose of the Study:
- To analyze publicly available travel data for network statistics beyond traditional descriptive measures.
- To visualize pandemic connectedness using time-series plots and spatial-temporal maps.
- To assess the utility of travel network data for early pandemic risk recognition.
Main Methods:
- Utilized data from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, encompassing civil flight information.
- Developed a workflow for travel data collection, network construction, aggregation, and statistical calculation.
- Visualized pandemic connectedness using time-series plots and spatial-temporal maps.
Main Results:
- Observed similar global daily flight patterns in early 2019 and 2020, with a sharp decline in March 2020 due to COVID-19 travel restrictions.
- High network density and reciprocity in early March 2020 served as early indicators of the COVID-19 pandemic.
- Spatial-temporal mapping highlighted significant connectedness in Europe by March 13, 2020, preceding widespread outbreaks.
Conclusions:
- Travel network connectedness is a strong indicator of pandemic risk.
- The study demonstrates the potential of analyzing flight data for early pandemic detection and surveillance.
- This methodology can aid in recognizing current and future communicable disease threats.
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