Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends

Uri Eliyahu; Mona Boaz

doi:doi:10.11648/j.bsi.20200502.12

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Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends

Uri Eliyahu, Mona Boaz

Published in Biomedical Statistics and Informatics (Volume 5, Issue 2)

Received: 19 April 2020 Accepted: 23 June 2020 Published: 4 August 2020

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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.

Published in	Biomedical Statistics and Informatics (Volume 5, Issue 2)
DOI	10.11648/j.bsi.20200502.12
Page(s)	47-51
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Pandemic, Prevalence, Incidence, Case Fatality, Recovery

References

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[3]	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.
[4]	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.
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[6]	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.
[7]	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.
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[15]	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.
[16]	https://www.trip.com/travel-restrictions-covid-19/.
[17]	https://www.worldnomads.com/travel-safety/worldwide/worldwide-travel-alerts.
[18]	Muurlink OT, Taylor-Robinson AW. (2020). COVID-19: Cultural Predictors of Gender Differences in Global Prevalence Patterns. Front. Public Health, 30 April 2020. https://doi.org/10.3389/fpubh.2020.00174.
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APA Style

Uri Eliyahu, Mona Boaz. (2020). Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends. Biomedical Statistics and Informatics, 5(2), 47-51. https://doi.org/10.11648/j.bsi.20200502.12

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ACS Style

Uri Eliyahu; Mona Boaz. Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends. Biomed. Stat. Inform. 2020, 5(2), 47-51. doi: 10.11648/j.bsi.20200502.12

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AMA Style

Uri Eliyahu, Mona Boaz. Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends. Biomed Stat Inform. 2020;5(2):47-51. doi: 10.11648/j.bsi.20200502.12

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@article{10.11648/j.bsi.20200502.12,
  author = {Uri Eliyahu and Mona Boaz},
  title = {Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends},
  journal = {Biomedical Statistics and Informatics},
  volume = {5},
  number = {2},
  pages = {47-51},
  doi = {10.11648/j.bsi.20200502.12},
  url = {https://doi.org/10.11648/j.bsi.20200502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20200502.12},
  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.},
 year = {2020}
}

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TY  - JOUR
T1  - Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends
AU  - Uri Eliyahu
AU  - Mona Boaz
Y1  - 2020/08/04
PY  - 2020
N1  - https://doi.org/10.11648/j.bsi.20200502.12
DO  - 10.11648/j.bsi.20200502.12
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JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 47
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PB  - Science Publishing Group
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UR  - https://doi.org/10.11648/j.bsi.20200502.12
AB  - 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.
VL  - 5
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Author Information

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

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APA Style

Uri Eliyahu, Mona Boaz. (2020). Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends. Biomedical Statistics and Informatics, 5(2), 47-51. https://doi.org/10.11648/j.bsi.20200502.12

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ACS Style

Uri Eliyahu; Mona Boaz. Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends. Biomed. Stat. Inform. 2020, 5(2), 47-51. doi: 10.11648/j.bsi.20200502.12

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AMA Style

Uri Eliyahu, Mona Boaz. Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends. Biomed Stat Inform. 2020;5(2):47-51. doi: 10.11648/j.bsi.20200502.12

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@article{10.11648/j.bsi.20200502.12,
  author = {Uri Eliyahu and Mona Boaz},
  title = {Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends},
  journal = {Biomedical Statistics and Informatics},
  volume = {5},
  number = {2},
  pages = {47-51},
  doi = {10.11648/j.bsi.20200502.12},
  url = {https://doi.org/10.11648/j.bsi.20200502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20200502.12},
  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.},
 year = {2020}
}

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TY  - JOUR
T1  - Use of Databases Available on the Web to Describe COVID-19 Morbidity and Mortality Trends
AU  - Uri Eliyahu
AU  - Mona Boaz
Y1  - 2020/08/04
PY  - 2020
N1  - https://doi.org/10.11648/j.bsi.20200502.12
DO  - 10.11648/j.bsi.20200502.12
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 47
EP  - 51
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20200502.12
AB  - 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.
VL  - 5
IS  - 2
ER  -

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