Please enter verification code
Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends
Biomedical Statistics and Informatics
Volume 5, Issue 2, June 2020, Pages: 47-51
Received: Apr. 19, 2020; Accepted: Jun. 23, 2020; Published: Aug. 4, 2020
Views 293      Downloads 136
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
Article Tools
Follow on us
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.
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 © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Helmy YA, Fawzy M, et al. (2020). The COVID-19 Pandemic: A Comprehensive Review of Taxonomy, Genetics, Epidemiology, Diagnosis, Treatment, and Control. Journal of Clinical Medicine.; 1225.
Oksanen A, Kaakinen M, et al. (2020). Regulation and Trust: 3-Month Follow-up Study on COVID-19 Mortality in 25 European Countries. JMIR Public Health Surveill. 2020; 6 (2): e19218. Apr 24. doi: 10.2196/19218.
Petrosillo N, Viceconte G, et al. (2020). COVID-19, SARS and MERS: are they closely related?. Clin Microbiol Infect.; 26 (6): 729-734. doi: 10.1016/j.cmi.2020.03.026.
U.S. Department of Health and Human Services Centers for Disease Control and Prevention (CDC),
Liu L. (2020). Emerging study on the transmission of the Novel Coronavirus (COVID-19) from urban perspective: Evidence from China. Cities. 103: 102759. doi: 10.1016/j.cities.2020.102759.
Corburn J, Vlahov D, et al. (2020). Slum Health: Arresting COVID-19 and Improving Well-Being in Urban Informal Settlements [published online ahead of print, 2020 Apr 24]. J Urban Health. 1-10. doi: 10.1007/s11524-020-00438-6.
Index Mundi,
Rocklöv J, Sjödin (2020). H. High population densities catalyze the spread of COVID-19. J Travel Med. 27 (3): taaa038. doi: 10.1093/jtm/taaa038.
Ahmadi M, Sharifi A, et al. (2020). Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Sci Total Environ. 729: 138705. doi: 10.1016/j.scitotenv.2020.138705.
Ramirez Aldana R, Gomez Varjan JC, et al. (2020) _Spatial_analysis_of_COVID-19_spread_in_Iran_Insights_into_geographical_and_structural_transmission_determinants_at_a_province_level, Research Gate
Smith AW, Freedman DO. (2020). Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak, Journal of Travel Medicine, Volume 27, Issue 2, March 2020, taaa020,
Wu Y, Jing W, Liu J, et al. (2020) Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Sci Total Environ. 729: 139051. doi: 10.1016/j.scitotenv.2020.139051.
Muurlink OT, Taylor-Robinson AW. (2020). COVID-19: Cultural Predictors of Gender Differences in Global Prevalence Patterns. Front. Public Health, 30 April 2020.
Ceylan Z. (2020) Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment. Volume 729.
Neiderud CJ. (2015). How urbanization affects the epidemiology of emerging infectious diseases. Infect Ecol Epidemiol. 2015; 5: 27060. Published 2015 Jun 24. doi: 10.3402/iee.v5.27060.
Centers for Disease Control and Prevention (CDC),
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186