Analysis of travel restrictions for COVID-19 control in Latin America through network connectedness

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

doi:10.1093/jtm/taaa176

Cited by 21 publications

(27 citation statements)

References 2 publications

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“…Spatial-temporal maps of Europe at 14-day intervals between May and June 2020 were generated. As countries serving as air transportation hubs were particularly prone to the spread of COVID-19 [16], spatial-temporal maps at the country level for these hubs have been plotted to investigate network connectedness between the countries harboring these hubs during the growth of the pandemic. Each bubble represents a country with airports defined as among the top 10 air transportation hubs in Europe by the Official Airline Guide MegaHub Index of 2019 [25].…”

Section: Data Visualizationmentioning

confidence: 99%

“…A study on COVID-19 control in Latin America suggested that countries serving as air transportation hubs are more prone to disease transmission. The practicable use of travel data for the prediction of the risk of the COVID-19 pandemic has been previously reported [16].…”

Section: Introductionmentioning

confidence: 99%

“…Time-series plots show changes in network density and reciprocity, which are likely to be early signs of alterations in the risk of a pandemic [21]. Spatial-temporal maps during network analysis illustrate the connectedness among places and reflect changes in the risk of a pandemic before unprocessed data on the number of confirmed cases are obtained [16]. Moreover, diverse tools can be used to analyze pandemic connectedness.…”

Section: Introductionmentioning

confidence: 99%

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

Chu¹,

Chan²,

Tsang³

et al. 2021

JMIR Public Health Surveill

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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.

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Section: Data Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chu¹,

Chan²,

Tsang³

et al. 2021

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

show abstract

“…The findings of these studies have shown that lockdown measures, in particular, appear to be one of the most effective approaches to limiting the spread of infections [ 15 ]. Studies have also shown that the imposition of travel restrictions has been effective in reducing correlations in the numbers of infected people across different countries [ 16 , 17 ]. In this paper, we develop a dynamic pandemic network model to evaluate the performances of several major policies in terms of controlling the spread of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model

Chu

Chan

et al. 2021

IJERPH

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In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.

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“…Travel restriction measures have varied from country to country and cases of imported lineages have been confirmed since the early phase of COVID-19 pandemic. 5 , 6 Despite SARS-CoV-2 genetic diversity across states seems to be geographically compartmentalized, 7 the relaxing distancing control measures and the increasing intercity population mobility have contributed to the spread of multiple lineages across different geographical regions in the country.…”

mentioning

confidence: 99%