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

Abstract

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Modelling the impact of travel restrictions on COVID-19 cases in Hong Kong in early 2020

“…Although morbidity and mortality caused by such service suspension are not included in the official COVID-19 statistics, the effects cannot be overlooked. Furthermore, uncontrolled local epidemic can cause outbreaks in other regions with close ties [ 31 ]. The damage brought by a severe local outbreak of COVID-19 is unbearable.…”

Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network

“…Some studies proposed temporal and spatial analysis of COVID-19 spreading focusing on air flows and dispersion of aerosolized virus in polluted areas ( Zheng et al, 2021 ), meteorological variables ( Wang et al, 2021 ), traffic volumes ( Mu et al, 2020 ). Network analyses have been carried out on cross-country pandemic air connectedness ( Chu et al, 2021 ), maritime transport ( Wang et al, 2022 ), high-speed railway ( Zhang et al, 2020a , Zhang et al, 2020b ): the attention was paid on mass public transport. Conversely, in this study the authors investigated how the highway transportation network in the Northern Italy exacerbated the large-scale spreading of COVID-19 during the first wave.…”

Modelling the Risk of Imported COVID-19 Infections at Maritime Ports Based on the Mobility of International-Going Ships

“…A main difference with international air transportation, for instance, is that much more precise data regarding daily flights and passenger flows are available. Passenger arrival and departure contamination tests are also nowadays widely processed; this makes COVID-19 risk evaluations much easier to evaluate [18,19].…”