Pandemic risk of COVID-19 outbreak in the United States: An analysis of network connectedness with air travel data

Tiwari, Afy; So, Mike K. P.; Chong, Andy Chun Yin; Chan, Jacky N.L.; Chu, Amanda M. Y.

doi:10.1016/j.ijid.2020.11.143

Cited by 25 publications

(25 citation statements)

References 18 publications

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“…Moreover, using the parameters obtained from our estimation, we examined the highest probability of two countries being linked, the country-specific effect, the initial average distance, and the speed to build up the pandemic risk. As in other papers [ 28 , 51 , 52 , 53 ], using this pandemic space analysis, we concluded that both lockdown and travel restrictions are effective at reducing the pandemic risk across countries. Nevertheless, these two measures, and probably also other control measures, are not sufficient to wipe out the risk of pandemic across countries.…”

Section: Discussionsupporting

confidence: 83%

“…One possible approach is to make use of the network statistics in pandemic network data [ 24 ]. There have been recent research papers published that propose the use of COVID-19 pandemic network data to predict and estimate the pandemic risk across countries [ 25 , 26 , 27 , 28 ]. The authors of those papers based their conclusions on the study of pandemic risk scores and network connectedness, using network density, the clustering coefficient, and the assortativity coefficient.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 83%

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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“…Although the direction of the relationship between daily flights and the number of cases varies between countries in the countries examined within the scope of the study, a statistically significant but quite weak relationship was found in all countries in general. However, although the results of the studies on the subject in the literature differ, the results of our study coincide with the results of Tiwari et al (2020).…”

Section: Demet Dağlı -Ednan Ayvazsupporting

confidence: 84%

The Relation Between Flight Mobility and Spread of Covid-19: A Study in the Balkan Countries

Dağli¹,

Ayvaz²

2021

TurkishStudies

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Covid-19 virus first appeared in Wuhan, China, and then spread rapidly across the world. While the epidemic has created an unprecedented crisis effect worldwide, the aviation industry is one of the industries most affected by the crisis. Human mobility provided by the aviation industry was perceived as a threat to the spread of the Covid-19 virus, and the first measures taken by governments to prevent this spread were in the direction of imposing national and international travel restrictions as well as quarantine practices. Due to the bans and restrictions imposed by governments, the aviation industry has faced a serious decline in demand. This situation has led to a significant loss of income for businesses operating in the aviation industry and aviation businesses have experienced difficult situations. The purpose of this study is to examine the relationship between flight mobility and the spread of the Covid-19 virus. Within the scope of the study, the relationship between daily flight traffic and the number of Covid-19 cases from the date of the first case to December 31 in each of the nine Balkan countries was examined using the correlation analysis method. For the correlation analysis performed in the study, the daily number of cases published by the World Health Organization (WHO) and the daily flight numbers published by EUROCONTROL were used. Serbia, Montenegro and Kosovo were excluded from the scope of this study as daily data on the number of flights and cases were not available. As a result of the analysis, although it was determined that the relationship between flight mobility and Covid-19 spread differs between countries, it was found that there was a statistically significant but very weak relationship in general.

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“…( 2021b ), and Tiwari et al. ( 2021 ), we obtain the network densities for the pandemic networks according to the steps below. Let be the number of confirmed cases of country i in day t .…”

Section: Methodsmentioning

confidence: 99%

“…Using the methods in Chu et al ( 2020a , 2020b ), Tiwari et al. ( 2021 ), and So et al. ( 2021b ) who proposed the use of network density to detect early signals of the COVID-19 pandemic, we test any lead-lag relationships between the pandemic network and the financial network by testing the Granger causality between the pandemic network density and the financial network density.…”

Section: Introductionmentioning

confidence: 99%

Financial Network Connectedness and Systemic Risk During the COVID-19 Pandemic

Chan

Chu

2021

Asia-Pac Financ Markets

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The COVID-19 pandemic causes a huge number of infections. The outbreak of COVID-19 has not only caused substantial healthcare impacts, but also affected the world economy and financial markets. In this paper, we study the effect of the COVID-19 pandemic on financial market connectedness and systemic risk. Specifically, we test dynamically whether the network density of pandemic networks constructed by the number of COVID-19 confirmed cases is a leading indicator of the financial network density and portfolio risk. Using rolling-window Granger-causality tests, we find strong evidence that the pandemic network density leads the financial network density and portfolio risk from February to April 2020. The findings suggest that the COVID-19 pandemic may exert significant impact on the systemic risk in financial markets.

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Pandemic risk of COVID-19 outbreak in the United States: An analysis of network connectedness with air travel data

Cited by 25 publications

References 18 publications

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

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

The Relation Between Flight Mobility and Spread of Covid-19: A Study in the Balkan Countries

Financial Network Connectedness and Systemic Risk During the COVID-19 Pandemic

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