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Spatial panel‐data models are estimated to identify the factors of the prevalence of the coronavirus outbreak in North Africa. Using daily data on the number of cases collected between March 2020 and December 2021, three types of general models are investigated, and they include spatial spillovers between the neighboring countries of the region. In...
Citations
... Moreover, Khedhiri (2022) further elucidated that the spatial repercussions and alterations in travel policies constitute pivotal factors in explaining the mechanisms underpinning the spread of the coronavirus pandemic across North Africa. Consequently, this study postulates the hypothesis that a substantial spatial interdependence exists between the progression of COVID-19 within a European nation and the performance metrics of the tourism industry, encompassing variables such as hotel revenue and aviation departures, within its adjacent nations. ...
The tourism industry suffered the worst impact due to the spread of the COVID-19 pandemic. In this case, the Europe is the region whose tourism industry has been hardest hit globally. This study aims to analyze the relationship between the COVID-19 pandemic and the tourism industry in the Europe involving 19 countries with a period of about 514 days (early March 2020 to late July 2021) using spatial econometric analysis techniques. The data used was obtained from the European Center for Diseases Prevention and Control (ECDC) and several data providers that can be accessed online. The Spatial Durbin Model (SDM) with fixed effects was selected as the best spatial panel model that shows the spatial dependence among European countries on the tourism industry in relation to the COVID-19 pandemic. There is significant evidence of indirect effects indicating spatial spillovers in almost all independent variables in the model.
... In a recent study, spatial panel-data models were used to identify the factors for the spike of COVID-19 infections in North Africa. 2 In another study, a statistical analysis was performed based on zero-inflation models and autoregressive conditional count models to forecast death counts with evidence from Tunisian data. 3 Furthermore, quantitative analyses including statistical modeling and deep learning methods have also been performed to forecast the pandemic outbreak in different parts of the world. ...
