The spatial econometrics of the coronavirus pandemic

Krisztin, Tamás; Piribauer, Philipp; Wögerer, Michael

doi:10.1007/s12076-020-00254-1

Cited by 49 publications

(53 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another way that infection rates have been modelled is through the use of spatial econometrics. For example, Krisztin et al (2020) use Bayesian spatial econometric techniques to examine the impact of spatial spillovers on infection rates (number of flight connections, trade patterns, common borders etc.). Their main finding was that international flight linkages were the main transmission channel.…”

Section: Empirical Challengesmentioning

confidence: 99%

Suggestions for a Covid-19 Post-Pandemic Research Agenda in Environmental Economics

Elliott

Schumacher

Withagen

2020

Environ Resource Econ

View full text Add to dashboard Cite

In this article we draw upon early lessons from the 2020 Covid-19 crisis and discuss how these may relate to a future research agenda in environmental economics. In particular, we describe how the events surrounding the Covid-19 crisis may inform environmental research related to globalization and cooperation, the green transition, pricing carbon externalities, as well as the role of uncertainty and timing of policy inventions. We also discuss the implications for future empirical research in this area.

show abstract

Section: Empirical Challengesmentioning

confidence: 99%

Suggestions for a Covid-19 Post-Pandemic Research Agenda in Environmental Economics

Elliott

Schumacher

Withagen

2020

Environ Resource Econ

View full text Add to dashboard Cite

show abstract

“…More connected countries registered first cases of infection earlier than the less connected countries with the effect being reinforced by direct flight connections to China. Krisztin et al (2020) used spatial econometric specification to model daily infection rates of Covid-19 across countries focusing on the number of flight connections, relationships in international trade and common borders. In early stages of the infection international flight linkages have shown to be the main transmission channel.…”

Section: Literature Reviewmentioning

confidence: 99%

The socio-economic catalysers of COVID-19 pandemic

Jošić¹

2020

Croatian Review of Economic, Business and Social Statistics

View full text Add to dashboard Cite

The COVID-19 pandemic was triggered on December 2019 in the city of Wuhan, China, spreading across the world causing global economic crisis and public health emergency. One could ask: what are the socio-economic factors that catalyse the spread of the disease and why are some countries more affected by the COVID-19 pandemic. Therefore, the goal of this paper is to investigate these socio-economic catalysers of the COVID-19 spread. For that purpose, a cross-country regression analysis was conducted at three time points (April 1st, 2020, April 15th 2020 and April 29th, 2020) using OLS, Tobit and PPML estimators. The results of the analysis have shown that countries with higher gross domestic product per capita, population, HDI and HFI indices have been hardely hit with the global COVID-19 pandemic. When some variables were transformed with by dividing it with the population variable, POPDEN and TOUR variables appeared to be significant. The AGE variable was important in the model taking into account total deaths due to the COVID-19 infection. The limitations of the paper are related to data unavailability for some variables in the most recent year. The results obtained from this analysis should be repeated, taking into account other time points and additional COVID-19 socioeconomic catalysers.

show abstract

“…Some works, including ours, focus on the dynamics of people moving from one place to another, while other researchers have focused their efforts on studying the way the virus can be transmitted in certain environments pertaining to travel, such as trains and planes [ 11 , 12 ]. For example, Krisztin et al used econometric models to study how cross-country air travel played an important role in the early spread of the virus between European countries [ 13 ]. However, we shall not delve any deeper into this subject as this would take us too far afield into the background behind our approach.…”

Section: Introductionmentioning

confidence: 99%

“…Instead, in the same vein as [ 13 ], Farzanegan et al studied the role played by tourism in the spread of the virus between countries across the world by using regression analysis to verify the relevance of tourist flows on the total number of infected people in conjunction with other factors, such as, for example, population density and aging, healthcare expenditures, and others [ 15 ]. Similarly, Falk and Hangsten have studied how alpine tourism during the winter holidays resulted in a surge of COVID-19 cases in the Scandinavian countries, as many citizens brought back the virus after their return from their holidays in Italy and Austria [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared

Casini

Roccetti

2020

Sensors

View full text Add to dashboard Cite

On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease.

show abstract

The spatial econometrics of the coronavirus pandemic

Cited by 49 publications

References 28 publications

Suggestions for a Covid-19 Post-Pandemic Research Agenda in Environmental Economics

Suggestions for a Covid-19 Post-Pandemic Research Agenda in Environmental Economics

The socio-economic catalysers of COVID-19 pandemic

A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared

Contact Info

Product

Resources

About