Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models
This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | International Journal of Telemedicine and Applications |
| Online Access: | http://dx.doi.org/10.1155/2023/9962100 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559717983977472 |
|---|---|
| author | Dmytro Chumachenko Tetiana Dudkina Sergiy Yakovlev Tetyana Chumachenko |
| author_facet | Dmytro Chumachenko Tetiana Dudkina Sergiy Yakovlev Tetyana Chumachenko |
| author_sort | Dmytro Chumachenko |
| collection | DOAJ |
| description | This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates. |
| format | Article |
| id | doaj-art-b4090c6e33864dc6aa5d65098ef304cd |
| institution | Kabale University |
| issn | 1687-6423 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj-art-b4090c6e33864dc6aa5d65098ef304cd2025-02-03T01:29:30ZengWileyInternational Journal of Telemedicine and Applications1687-64232023-01-01202310.1155/2023/9962100Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning ModelsDmytro Chumachenko0Tetiana Dudkina1Sergiy Yakovlev2Tetyana Chumachenko3Mathematical Modelling and Artificial Intelligence DepartmentMathematical Modelling and Artificial Intelligence DepartmentMathematical Modelling and Artificial Intelligence DepartmentEpidemiology DepartmentThis study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates.http://dx.doi.org/10.1155/2023/9962100 |
| spellingShingle | Dmytro Chumachenko Tetiana Dudkina Sergiy Yakovlev Tetyana Chumachenko Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models International Journal of Telemedicine and Applications |
| title | Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models |
| title_full | Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models |
| title_fullStr | Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models |
| title_full_unstemmed | Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models |
| title_short | Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models |
| title_sort | effective utilization of data for predicting covid 19 dynamics an exploration through machine learning models |
| url | http://dx.doi.org/10.1155/2023/9962100 |
| work_keys_str_mv | AT dmytrochumachenko effectiveutilizationofdataforpredictingcovid19dynamicsanexplorationthroughmachinelearningmodels AT tetianadudkina effectiveutilizationofdataforpredictingcovid19dynamicsanexplorationthroughmachinelearningmodels AT sergiyyakovlev effectiveutilizationofdataforpredictingcovid19dynamicsanexplorationthroughmachinelearningmodels AT tetyanachumachenko effectiveutilizationofdataforpredictingcovid19dynamicsanexplorationthroughmachinelearningmodels |