Volume 5, Issue 2, June 2020, Page: 47-51
Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends
Uri Eliyahu, Department of Nutrition, Faculty of Health Sciences, Ariel University, Ariel, Israel
Mona Boaz, Department of Nutrition, Faculty of Health Sciences, Ariel University, Ariel, Israel
Received: Apr. 19, 2020;       Accepted: Jun. 23, 2020;       Published: Aug. 4, 2020
DOI: 10.11648/j.bsi.20200502.12      View  80      Downloads  70
Abstract
In December 2019, an infectious pandemic outbreak occurred in the city of Wuhan in the Province of Hubei, China. The pathogen was identified as a novel coronavirus - COVID-19. This virus belongs to a family of viruses that cause Severe Acute Respiratory Syndrome, known as SARS-COV. The disease is characterized by a high mortality rate among adults aged 60 years or above, particularly those with chronic comorbidities. Databases available on the web provide updated, real-time data on the incidence and mortality rates ascribed to the COVID-19 pandemic in various countries. However, to draw accurate epidemiologic conclusions, demographic data (population density, age distribution, and urbanization level), as well as clinical data (number of screening tests and number of days since the first detected disease case in the country) must be taken into consideration. Informed use of these data affords reliable epidemiologic analysis. For example, a comparison of COVID-19 case fatality rates between Germany and Iran – two countries similar in population size and urbanization level – reveals that the mortality rate in Iran is significantly higher than that of Germany, while the active morbidity burden is much higher in Germany. This may seem surprising, given that Germany’s population is considerably older than that of Iran and four times as dense. It may be surmised that the quality and availability of health services in Germany are superior to those in Iran, offering a higher number of screening tests and more effective clinical treatment. Another important factor affecting morbidity spread is the timing of a lockdown policy implementation. For example, a comparison between China and the USA – two countries with similar land area and median age – reveals that in spite of the fact that in China population density is about 4.25 times higher than in the USA, morbidity rate is considerably lower than in the USA. Two factors can be considered responsible for this lower rate: lower urbanization and an earlier lockdown policy compared with the USA.
Keywords
Pandemic, Prevalence, Incidence, Case Fatality, Recovery
To cite this article
Uri Eliyahu, Mona Boaz, Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends, Biomedical Statistics and Informatics. Vol. 5, No. 2, 2020, pp. 47-51. doi: 10.11648/j.bsi.20200502.12
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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