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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Jan 21, 2021
Date Accepted: Mar 4, 2021
Date Submitted to PubMed: Mar 31, 2021

The final, peer-reviewed published version of this preprint can be found here:

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis

Chu AM, Chan JN, Tsang JT, Tiwari A, So MK

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis

JMIR Public Health Surveill 2021;7(3):e27317

DOI: 10.2196/27317

PMID: 33711799

PMCID: 8088858

Analyzing cross-country pandemic connectedness in COVID-19: Network analysis using a spatial-temporal database

  • Amanda M.Y. Chu; 
  • Jacky N.L. Chan; 
  • Jenny T.Y. Tsang; 
  • Agnes Tiwari; 
  • Mike K.P. So

ABSTRACT

Background:

Communicable diseases, such as coronavirus disease 2019, pose a major threat to public health across the globe. To effectively curb the spread of communicable diseases, timely prediction of pandemic risk is essential.

Objective:

Our objective is to analyze travel data retrieved from the online Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation (CAPSCA) dashboard, which contains up-to-date and comprehensive meta information about civil flights from 193 national governments according to the airport, country, city, latitude, and longitude of flight origin and destination. Unlike official travel data sources, such as the Federal Aviation Administration (FAA) and the International Air Transport Association (IATA), the travel data of CAPSCA is free and publicly available.

Methods:

Because air travel is a common route of communicable disease dissemination and network analysis is a powerful way to estimate pandemic risk, a spatial-temporal database allowing us to analyze cross-country pandemic connectedness is important. This database can construct useful travel data records for network statistics other than common descriptive statistics. In this study, we display analytical results by time series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness.

Results:

We find similar patterns in the time series plots of worldwide daily flights from January to early-March in 2019 and 2020. A sharp drop in daily flight numbers recorded in mid-March 2020 was likely related to large-scale air travel restrictions due to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of pandemic risk. When COVID-19 cases began to appear across the globe, high network density and reciprocity in early March were early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March. The spatial-temporal map of connectedness in Europe on 13 March 2020 shows the highest level of connectedness between the European countries, reflecting the severe outbreak of the COVID-19 pandemic in late March and early April. As a quality control, we use the aggregated international flight counts from April to October 2020 to compare the official reported counts by the ICAO with the data collected from the CAPSCA dashboard, and find high consistency between the two datasets.

Conclusions:

The flexible design of the database gives users access to network connectedness at different periods, places, and spatial levels by various network statistics calculation methods according to their needs. The database can facilitate early recognition of the pandemic risk of current communicable diseases and newly emerged communicable diseases in the future.


 Citation

Please cite as:

Chu AM, Chan JN, Tsang JT, Tiwari A, So MK

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis

JMIR Public Health Surveill 2021;7(3):e27317

DOI: 10.2196/27317

PMID: 33711799

PMCID: 8088858

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