Your browser doesn't support javascript.
loading
Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic
Heliang Yin; Tong Sun; Lan Yao; Yan Jiao; Ma Li; Lin Lin; J Carolyn Graff; Lotfi Aleya; Arnold Postlethwaite; Weikuan Gu; Hong Chen.
Affiliation
  • Heliang Yin; Center of Integrative Research, The First Hospital of Qiqihar City, Qiqihar, Heilongjiang 161005, P.R. China.
  • Tong Sun; Department of Administration, The First Hospital of Qiqihar, Qiqihar, Heilongjiang, 161005, P.R. China.
  • Lan Yao; Health Outcomes and Policy Research, College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis, TN 38103, USA.
  • Yan Jiao; Department of Orthopedic Surgery and BME-Campbell Clinic, University of Tennessee Health Science Center, Memphis, Tennessee, 38163, USA.
  • Ma Li; Center of Integrative Research, The First Hospital of Qiqihar City, Qiqihar, Heilongjiang 161005, P.R. China.
  • Lin Lin; Center of Integrative Research, The First Hospital of Qiqihar City, Qiqihar, Heilongjiang 161005, P.R. China.
  • J Carolyn Graff; College of Nursing, University of Tennessee Health Science Center, Memphis, TN 38105, USA.
  • Lotfi Aleya; Chrono-Environnement Laboratory, UMR CNRS 6249, Bourgogne Franche-Comte University, F-25030 Besancon Cedex, France
  • Arnold Postlethwaite; Department of Medicine and Division of Connective Tissue Diseases, University of Tennessee Health Science Center, Memphis, Tennessee, 38163, USA
  • Weikuan Gu; Research Service, Memphis VA Medical Center, 1030 Jefferson Avenue, Memphis, TN, 38104, USA.
  • Hong Chen; Center of Integrative Research, The First Hospital of Qiqihar City, Qiqihar, Heilongjiang 161005, P.R. China.
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20187849
Journal article
A scientific journal published article is available and is probably based on this preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See journal article
ABSTRACT
BackgroundCurrently, the 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. MethodsWe collected data from official webpages of cities in China and in the US. The data were uploaded on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between the morbidity and other factors. ResultsOur analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the US, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the US has no association, with an r value of -0.1288. ConclusionsPopulation density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread.
License
cc_no
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Experimental_studies / Observational_studies Language: En Year: 2020 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Experimental_studies / Observational_studies Language: En Year: 2020 Document type: Preprint