Document Type : Short Communication
Author
University of Porto, INESC TEC & ISPGAYA, Porto, Portugal
Abstract
Background: The estimation of the real number of COVID-19 infected people is one of the concerns of the governments around the world. In this sense, this study seeks to assess the incidence and fatality of COVID-19 in Europe considering the expected number of the infected cases.
Methods: A quantitative exploratory study was performed on the top 10 countries most affected by COVID-19 by 9th June in Europe. Furthermore, this study presents three propagation estimation models of the COVID-19 that help us to understand the real incidence of the pandemic in each country. Each model is briefly explained and applied.
Results: The findings revealed a great heterogeneity of COVID-19 cases and deaths among the countries. The indicator of the number of deaths reveals the greatest disparity between other countries with the United Kingdom, recording about 6 or 7 times more deaths than Russia or Germany. Infection fatality rate (IFR) tends to be a more reliable indicator when analyzing data because it is less dependent on the number of tests performed.
Conclusion: Several estimation models can be used to determine the incidence of COVID-19. However, their results in European countries are still quite asymmetrical although they are more reliable than just looking at the perspective of the number of cases or deaths recorded. The infection fatality rate (IFR) emerges as a more accurate indicator by estimating the expected number of registered cases, which includes asymptomatic cases and patients with mild symptoms that are not known and reported by health authorities.
Keywords
- 1. Meagher KM, Cummins NW, et al. COVID-19 Ethics and Research. Mayo Clinic Proceedings 2020;95(6):1119-1123.
- 2. Rohman M [Internet]. Sustaining Community Research Partnerships During COVID-19. Available from: https://news.feinberg.northwestern.edu/2020/04/sustaining-community-research-partnerships-during-covid-19/
- 3. Akyürek CE, Sawalha R, et al. Factors Affecting the Decision Making Process in Healthcare Institutions. Academy of Strategic Management Journal 2015;14:1-14.
- 4. OECD [Internet]. Beyond containment: Health systems responses to COVID-19 in the OECD. Available from: http://www.oecd.org/coronavirus/policy-responses/beyond-containment-health-systems-responses-to-covid-19-in-the-oecd-6ab740c0/
- 5. Winter AK, Hedge ST. The importance role of serology for COVID-19 control. The Lancet Infectious Diseases 2020;20(7):758-759.
- 6. Nishiura H, Klinkenberg D, et al. Early Epidemiological Assessment of the Virulence of Emerging Infectious Diseases: A Case Study of an Influenza Pandemic. Plos One 2009;4(8):1-8.
- 7. IHME [Internet]. COVID-19 resources. Available from: http://www.healthdata.org/covid
- 8. CoronaSurveys [Internet]. CoronaSurveys Project Summary. Available from: https://coronasurveys.org
- 9. Bernard HR, Hallett T, et al. Counting hard-to-count populations: the network scale-up method for public health. Sexually Transmitted Infections 2010;86(S2):11-15.
- 10. Worldometer [Internet]. Coronavirus Updates. Available from: https://www.worldometers.info
- 11. Verity R, Okell LC, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet Infectious Diseases 2020;20(6):669-677.
- 12. Cheng MP, Papenburg J, et al. Diagnostic Testing for Severe Acute Respiratory Syndrome–Related Coronavirus 2. Annals of Internal Medicine 2020;172(11):726-734.
- 13. Schultz T [Internet]. Why Belgium's Death Rate Is So High: It Counts Lots of Suspected COVID-19 Cases. Available from: https://www.npr.org/sections/coronavirus-live-updates/2020/04/22/841005901/why-belgiums-death-rate-is-so-high-it-counts-lots-of-suspected-covid-19-cases
)