Coronavirus and Migration: Analysis of Human Mobility and the Spread of Covid-19

Sirkeci, İbrahim; Yüceşahin, M. Murat

doi:10.33182/ml.v17i2.935

Cited by 132 publications

(129 citation statements)

References 23 publications

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“…The literature shows a significant effect of population density on the outbreak of infectious diseases (44). While a previous study (29) found no significant relationship between population density and total confirmed COVID-19 cases, there is a broader literature that shows an association between population density and the outbreak of infectious diseases (44).…”

Section: Datamentioning

confidence: 97%

“…We test this using four population characteristics of individual countries: Human Development Index (HDI), population aged over 65, mean household size and national population size. These variables have been selected based on recent studies that found them significant in explaining the COVID-19 outbreak at the early stages of its spread (8,29,46).…”

Section: Different Underlying Processes Characterise Types Of Spatialmentioning

confidence: 99%

“…Globalisation variable 1 is de facto interpersonal globalisation is a KOF sub-index of social globalisation that includes indicators of international traffic, transfers, international tourism, international students and migration (38). An early study of the COVID-19 spatial diffusion (29) shows that the volume of migration flows has been a strong indicator for the international spread of the pandemic. Globalisation variable 2 is de facto trade globalisation, another KOF sub-index of economic globalization that reflects trade in goods and services as well as trade partner diversity (38).…”

Section: Datamentioning

confidence: 99%

“…To understand the role of settlement characteristics in COVID-19 diffusion, we test four variables that measure various national-scale dimensions, including: urbanisation rate, population density, maximum urban population density, and areal accessibility (measures the average drive time of the national population from smaller to larger settlements (63)). These represent human interaction within national boundaries, with recent publications demonstrating that diffusion happens more rapidly in cities that are dense, well-connected, and accessible (11,29,(42)(43)(44). Settlement variable 1 is urbanisation rate, defined as the proportion of a national population located in cities or metropolitan regions (national definitions vary).…”

Section: Datamentioning

confidence: 99%

“…Older populations or populations with higher mortality rates are more likely to get tested than younger populations that may be asymptomatic (46,65). Population variable 1 is HDI (Human Development Index), which captures a holistic picture of individual countries and has been used as an indicator of the macro environment in a previous study (29) written in the early period of the pandemic. The study found that each unit increase in the HDI score is associated with five more confirmed COVID-19 cases.…”

Section: Datamentioning

confidence: 99%

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The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

Sigler

Mahmuda

Kimpton

et al. 2020

Preprint

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Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020. Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

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Section: Datamentioning

confidence: 97%

Section: Different Underlying Processes Characterise Types Of Spatialmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

See 3 more Smart Citations

The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

Sigler

Mahmuda

Kimpton

et al. 2020

Preprint

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Socioeconomic determinants of COVID‐19 in Asian countries: An empirical analysis

Varkey

Joy

Sarmah

et al. 2020

Journal of Public Affairs

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The spread of coronavirus disease, 2019, has affected several countries in the world including Asian countries. The occurrences of COVID infections are uneven across countries and the same is determined by socioeconomic situations prevailing in the countries besides the preparedness and management. The paper is an attempt to empirically examine the socioeconomic determinants of the occurrence of COVID in Asian countries considering the data as of June 18, 2020, for 42 Asian countries. A multiple regression analysis in a cross‐sectional framework is specified and ordinary least square (OLS) technique with heteroscedasticity corrected robust standard error is employed to obtain regression coefficients. Explanatory variables that are highly collinear have been dropped from the analysis. The findings of the study show a positive significant association of per capita gross national income and net migration with the incidence of total COVID‐19 cases and daily new cases. The size of net migration emerged to be a potential factor and positive in determining the total and new cases of COVID. Social capital as measured by voters' turnout ratio (VTR) in order to indicate the people's participation is found to be significant and negative for daily new cases per million population. People's participation has played a very important role in checking the incidence of COVID cases and its spread. In alternate models, countries having high incidence of poverty are also having higher cases of COVID. Though the countries having higher percentage of aged populations are more prone to be affected by the spread of virus, but the sign of the coefficient of this variable for Asian country is not in the expected line. Previous year health expenditure and diabetic prevalence rate are not significant in the analysis. Therefore, people‐centric plan and making people more participatory and responsive in adhering to the social distancing norms in public and workplace and adopting preventive measures need to be focused on COVID management strategies. The countries having larger net migration and poverty ratio need to evolve comprehensive and inclusive strategies for testing, tracing, and massive awareness for sanitary practices, social distancing, and following government regulation for management of COVID‐19, besides appropriate food security measures and free provision of sanitary kits for vulnerable section.

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University Communication in Times of Covid-19: The Ecuadorian Case

Clery

Molina

Linzán

et al. 2021

Comprehensible Science

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The COVID-19 outbreak has become a worldwide concern for everyone, and specifically for Higher Education Institutions, their students, teachers and those responsible for university public policies. Social distancing is now the primary consideration for any student when deciding where and how to study, reducing their willingness to educate themselves in physical settings with others who may carry the virus. In this context, this article analyzes how the Covid-19 pandemic affects Ecuadorian universities in the form of a communication and marketing trial, and its influence on university actors. The Covid-19 crisis has put the processes of social structures to the test. Communication and marketing are examples of these processes that face an unprecedented and unforeseen situation. In this sense, it is intended to reflect on how the pandemic affects and will condition the practice of university communication, both internal communication and its link with society. Finally, emphasis is placed on the importance of the universities assuming that reputation is a risk, and that this risk must be managed with the techniques and tools of communication, since the pandemic demonstrated higher education institutions the importance of managing relationships of trust with their teachers and students.

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Coronavirus and Migration: Analysis of Human Mobility and the Spread of Covid-19

Cited by 132 publications

References 23 publications

The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

Socioeconomic determinants of COVID‐19 in Asian countries: An empirical analysis

University Communication in Times of Covid-19: The Ecuadorian Case

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