Machine learning approach in inferring main population-level COVID-19 risk factors
Autori
Marković, SofijaRodić, Anđela
Milićević, Ognjen
Salom, Igor
Đorđević, Magdalena
Đorđević, Marko
Ostala autorstva
Morić, IvanaĐorđević, Valentina
Konferencijski prilog (Objavljena verzija)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Machine-learning methods have become indispensable in scientific research as the
amount of available data has grown exponentially in recent years. It is, thus, necessary
to employ various unsupervised and supervised machine learning methods to uncover
the main determinants of COVID-19 transmissibility and severity in the population.
Upon introducing appropriate disease transmissibility and severity measures and
gathering relevant socio-demographic, environmental, and health-related data for the
countries with obtained said measures, we implement several machine-learning-based
approaches to select the most prominent drivers of disease transmissibility and severity.
These approaches include regularization-based linear regression models and more
advanced Random Forest and Gradient Boost methods, which are not limited to the linear
relationships between the features and the response. Principal component analysis was
used for preselection to avoid overfitting, where numerous feat...ures were considered for a
relatively small number of observations (i.e., countries/states). As a result, a broad range
of potential COVID-19 risk factors was reduced to several prominent features, selected
robustly by different methods - we further untangle how they, directly or indirectly,
contribute to the transmissibility and severity of the disease. Our results underscore the
evolving nature of COVID-19, from the severity experienced during the first wave to the
emergence of new, highly transmissible variants like Omicron. These insights can guide
public health interventions, vaccine strategies, and policies aimed at reducing the burden
of COVID-19 and effectively managing future waves and emerging variants.
Ključne reči:
OVID-19 / machine learning / ecological regression analysis / epidemiological modeling / outburst risk factorsIzvor:
4th Belgrade Bioinformatics Conference, 2023, 4, 58-58Izdavač:
- Belgrade : Institute of molecular genetics and genetic engineering
Finansiranje / projekti:
- This work is supported by the Ministry of Science, Technological Development, and Innovation of the Republic of Serbia.
Napomena:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Kolekcije
Institucija/grupa
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Marković, Sofija AU - Rodić, Anđela AU - Milićević, Ognjen AU - Salom, Igor AU - Đorđević, Magdalena AU - Đorđević, Marko PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1998 AB - Machine-learning methods have become indispensable in scientific research as the amount of available data has grown exponentially in recent years. It is, thus, necessary to employ various unsupervised and supervised machine learning methods to uncover the main determinants of COVID-19 transmissibility and severity in the population. Upon introducing appropriate disease transmissibility and severity measures and gathering relevant socio-demographic, environmental, and health-related data for the countries with obtained said measures, we implement several machine-learning-based approaches to select the most prominent drivers of disease transmissibility and severity. These approaches include regularization-based linear regression models and more advanced Random Forest and Gradient Boost methods, which are not limited to the linear relationships between the features and the response. Principal component analysis was used for preselection to avoid overfitting, where numerous features were considered for a relatively small number of observations (i.e., countries/states). As a result, a broad range of potential COVID-19 risk factors was reduced to several prominent features, selected robustly by different methods - we further untangle how they, directly or indirectly, contribute to the transmissibility and severity of the disease. Our results underscore the evolving nature of COVID-19, from the severity experienced during the first wave to the emergence of new, highly transmissible variants like Omicron. These insights can guide public health interventions, vaccine strategies, and policies aimed at reducing the burden of COVID-19 and effectively managing future waves and emerging variants. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Machine learning approach in inferring main population-level COVID-19 risk factors EP - 58 SP - 58 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1998 ER -
@conference{ author = "Marković, Sofija and Rodić, Anđela and Milićević, Ognjen and Salom, Igor and Đorđević, Magdalena and Đorđević, Marko", year = "2023", abstract = "Machine-learning methods have become indispensable in scientific research as the amount of available data has grown exponentially in recent years. It is, thus, necessary to employ various unsupervised and supervised machine learning methods to uncover the main determinants of COVID-19 transmissibility and severity in the population. Upon introducing appropriate disease transmissibility and severity measures and gathering relevant socio-demographic, environmental, and health-related data for the countries with obtained said measures, we implement several machine-learning-based approaches to select the most prominent drivers of disease transmissibility and severity. These approaches include regularization-based linear regression models and more advanced Random Forest and Gradient Boost methods, which are not limited to the linear relationships between the features and the response. Principal component analysis was used for preselection to avoid overfitting, where numerous features were considered for a relatively small number of observations (i.e., countries/states). As a result, a broad range of potential COVID-19 risk factors was reduced to several prominent features, selected robustly by different methods - we further untangle how they, directly or indirectly, contribute to the transmissibility and severity of the disease. Our results underscore the evolving nature of COVID-19, from the severity experienced during the first wave to the emergence of new, highly transmissible variants like Omicron. These insights can guide public health interventions, vaccine strategies, and policies aimed at reducing the burden of COVID-19 and effectively managing future waves and emerging variants.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Machine learning approach in inferring main population-level COVID-19 risk factors", pages = "58-58", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1998" }
Marković, S., Rodić, A., Milićević, O., Salom, I., Đorđević, M.,& Đorđević, M.. (2023). Machine learning approach in inferring main population-level COVID-19 risk factors. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 58-58. https://hdl.handle.net/21.15107/rcub_imagine_1998
Marković S, Rodić A, Milićević O, Salom I, Đorđević M, Đorđević M. Machine learning approach in inferring main population-level COVID-19 risk factors. in 4th Belgrade Bioinformatics Conference. 2023;4:58-58. https://hdl.handle.net/21.15107/rcub_imagine_1998 .
Marković, Sofija, Rodić, Anđela, Milićević, Ognjen, Salom, Igor, Đorđević, Magdalena, Đorđević, Marko, "Machine learning approach in inferring main population-level COVID-19 risk factors" in 4th Belgrade Bioinformatics Conference, 4 (2023):58-58, https://hdl.handle.net/21.15107/rcub_imagine_1998 .