Human mobility and coronavirus disease 2019 (COVID-19): a negative binomial regression analysis

Oztig L.I.,
Askin O.E.
Document Type
Source Title
Public Health
Elsevier B.V.


Objectives: This study aimed to examine the link between human mobility and the number of coronavirus disease 2019 (COVID-19)–infected people in countries. Study design: Our data set covers 144 countries for which complete data are available. To analyze the link between human mobility and COVID-19–infected people, our study focused on the volume of air travel, the number of airports, and the Schengen system. Methods: To analyze the variation in COVID-19–infected people in countries, we used negative binomial regression analysis. Results: Our findings suggest a positive relationship between higher volume of airline passenger traffic carried in a country and higher numbers of patients with COVID-19. We further found that countries which have a higher number of airports are associated with higher number of COVID-19 cases. Schengen countries, countries which have higher population density, and higher percentage of elderly population are also found to be more likely to have more COVID-19 cases than other countries. Conclusions: The article brings a novel insight into the COVID-19 pandemic from a human mobility perspective. Future research should assess the impacts of the scale of sea/bus/car travel on the epidemic. The findings of this article are relevant for public health authorities, community and health service providers, as well as policy-makers. © 2020 The Royal Society for Public Health

Migration angle
Index Keywords

COVID-19; elderly population; epidemic; health services; mobility; population density; public health; regression analysis; viral disease; air transportation; aircraft; airport; Article; aviation; China; coronavirus disease 2019; data analysis; disease transmission; human; pandemic; physical mobility; population density; World Health Organization; binomial distribution; Coronavirus infection; global health; regression analysis; travel; virus pneumonia; Coronavirus; Airports; Binomial Distribution; Coronavirus Infections; Global Health; Humans; Pandemics; Pneumonia, Viral; Regression Analysis; Travel