Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data
Аутори
Rodić, AnđelaMarković, Sofija
Salom, Igor
Đorđević, Marko
Остала ауторства
Morić, IvanaĐorđević, Valentina
Конференцијски прилог (Објављена верзија)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Метаподаци
Приказ свих података о документуАпстракт
The Global Health Security Index (GHSI) is designed to assess the preparedness of
countries to deal with infectious disease outbreaks. However, the COVID-19 pandemic
has revealed a paradoxical relationship between the GHSI and the COVID-19 mortality,
with higher GHSI scores being associated with higher death rates. We aimed to explain
this puzzle. To rely on an accurate and robust measure of COVID-19 severity across
countries, we used our model-derived measure instead of the standard Case Fatality
Rate. We employed a range of statistical learning techniques, including non-parametric
machine learning methods, to identify the factors that influence COVID-19 severity in 85
countries. Also, we searched for the predictors of the largely unexplored excess mortality
counts. Our results suggest that the association of higher preparedness, measured by
the GHSI, with higher COVID-19 mortality may be an artifact of oversimplified statistical
analyses used in published studies. In addit...ion, it could be a consequence of misclassified
COVID‑19 deaths, combined with the higher median age of the population and earlier
epidemics onset in countries with high GHSI scores.
Кључне речи:
bioinformatics / modeling epidemics / machine learning / COVID-19 severity / excess deathsИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 64, -64Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Rodić, Anđela AU - Marković, Sofija AU - Salom, Igor AU - Đorđević, Marko PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/2004 AB - The Global Health Security Index (GHSI) is designed to assess the preparedness of countries to deal with infectious disease outbreaks. However, the COVID-19 pandemic has revealed a paradoxical relationship between the GHSI and the COVID-19 mortality, with higher GHSI scores being associated with higher death rates. We aimed to explain this puzzle. To rely on an accurate and robust measure of COVID-19 severity across countries, we used our model-derived measure instead of the standard Case Fatality Rate. We employed a range of statistical learning techniques, including non-parametric machine learning methods, to identify the factors that influence COVID-19 severity in 85 countries. Also, we searched for the predictors of the largely unexplored excess mortality counts. Our results suggest that the association of higher preparedness, measured by the GHSI, with higher COVID-19 mortality may be an artifact of oversimplified statistical analyses used in published studies. In addition, it could be a consequence of misclassified COVID‑19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data EP - 64 IS - 64 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_2004 ER -
@conference{ author = "Rodić, Anđela and Marković, Sofija and Salom, Igor and Đorđević, Marko", year = "2023", abstract = "The Global Health Security Index (GHSI) is designed to assess the preparedness of countries to deal with infectious disease outbreaks. However, the COVID-19 pandemic has revealed a paradoxical relationship between the GHSI and the COVID-19 mortality, with higher GHSI scores being associated with higher death rates. We aimed to explain this puzzle. To rely on an accurate and robust measure of COVID-19 severity across countries, we used our model-derived measure instead of the standard Case Fatality Rate. We employed a range of statistical learning techniques, including non-parametric machine learning methods, to identify the factors that influence COVID-19 severity in 85 countries. Also, we searched for the predictors of the largely unexplored excess mortality counts. Our results suggest that the association of higher preparedness, measured by the GHSI, with higher COVID-19 mortality may be an artifact of oversimplified statistical analyses used in published studies. In addition, it could be a consequence of misclassified COVID‑19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data", pages = "64", number = "64", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_2004" }
Rodić, A., Marković, S., Salom, I.,& Đorđević, M.. (2023). Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4(64). https://hdl.handle.net/21.15107/rcub_imagine_2004
Rodić A, Marković S, Salom I, Đorđević M. Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data. in 4th Belgrade Bioinformatics Conference. 2023;4(64):null-64. https://hdl.handle.net/21.15107/rcub_imagine_2004 .
Rodić, Anđela, Marković, Sofija, Salom, Igor, Đorđević, Marko, "Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data" in 4th Belgrade Bioinformatics Conference, 4, no. 64 (2023), https://hdl.handle.net/21.15107/rcub_imagine_2004 .