Effect of Tourism and Air Travel Restrictions on the Initial International Spread of the Covid-19 Pandemic

Sharma, Gajanand; Dhulipala, Sowjanya; Patil, Gopal R.

doi:10.3727/108354222x16675992447706

Cited by 2 publications

(1 citation statement)

References 35 publications

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“… Study Methods used Findings Oh et al 14 Google mobility data and regression models Mild to moderate mobility restrictions reduced COVID-19 case counts Bonaccorsi et al 15 Graph network approach Mobility restrictions hugely reduced revenue and increased poverty Bharati and Fakir 16 Heterogeneity analysis using least squares estimation Stricker rules contained the contagion. Restrictions reduced mobility more in relatively less-developed countries Sharma et al 18 Generalised linear model Countries with high tourism activity are affected early by COVID-19 restrictions Li et al 19 Network analysis and Statistical models Road density, population density, and land value positively impact the spread of COVID-19 Uddin et al 22 Network analysis and regression The structure of the suburban road network affects COVID-19 vulnerability and severity This study Network analysis and panel regression Road network density and prior infection rate significantly impact the future COVID-19 infection rate …”

Section: Discussionmentioning

confidence: 99%

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves

Uddin,

Khan,

et al. 2024

Sci Rep

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The COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although lockdowns, social distancing and business closures generally slowed the case growth, there is a growing concern about these restrictions' social, economic and psychological impact, especially on the disadvantaged and poorer segments of society. While we are all in this together, these segments often take the heavier toll of the pandemic and face harsher restrictions or get blamed for community transmission. This study proposes a road-network-based networked approach to model mobility patterns between localities during lockdown stages. It utilises a panel regression method to analyse the effects of mobility in transmitting COVID-19 in an Australian context, together with a close look at a suburban population’s characteristics like their age, income and education. Firstly, we attempt to model how the local road networks between the neighbouring suburbs (i.e., neighbourhood measure) and current infection count affect the case growth and how they differ between delta and omicron variants. We use a geographic information system, population and infection data to measure road connections, mobility and transmission probability across the suburbs. We then looked at three socio-demographic variables: age, education and income and explored how they moderate independent and dependent variables (infection rates and neighbourhood measures). The result shows strong model performance to predict infection rate based on neighbourhood road connection. However, apart from age in the delta variant context, the other variables (income and education level) do not seem to moderate the relationship between infection rate and neighbourhood measure. The results indicate that suburbs with a more socio-economically disadvantaged population do not necessarily contribute to more community transmission. The study findings could be potentially helpful for stakeholders in tailoring any health decision for future pandemics.

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Section: Discussionmentioning

confidence: 99%

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves

Uddin,

Khan,

et al. 2024

Sci Rep

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Stock Prices and COVID-19 Stimulus Policies: Evidence From Tourism and Hospitality Industry

Elrazaz¹,

Khalid²,

Okafor³

2024

Tourism Analysis

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This study examines the impact of economic stimulus policies on tourism-related firms’ stock prices, after movement restriction announcements, and differences in the relationships between economic policy responses and stock prices for large firms vis-à-vis small firms. Using a cross-section data of 888 firms from 56 countries listed on several stock exchanges, we find a positive and significant association between the COVID-19 economic stimulus index and 1- and 2-week average changes in tourism firms’ stock prices after movement restriction announcements. Tourism firms’ stock prices responded favorably to the introduction of macro-financial packages and monetary policies. This study complements the literature on stock market reactions during the pandemic and contributes to the growing body of literature examining its overall effect.

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Effect of Tourism and Air Travel Restrictions on the Initial International Spread of the Covid-19 Pandemic

Cited by 2 publications

References 35 publications

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves

Stock Prices and COVID-19 Stimulus Policies: Evidence From Tourism and Hospitality Industry

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