Machine Learning Model of COVID-19 Forecasting in Ukraine Based on the Linear Regression

Mohammadi, Alireza; Chumachenko, Dmytro; Chumachenko, Tetyana

doi:10.1109/elit53502.2021.9501122

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Separately, parametric equations can be evaluated taking into account all data. These parameters include morbidity and weather [49], the level of population heterogeneity [50], the mortality rate [51], vaccination [52], restrictive measures [53], etc. Regression analysis models are used to show or anticipate the relationship between a process and what the process might trigger.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

Chumachenko

2022

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The paper is devoted to an overview of the current state of research on the modeling of epidemic processes. The classification of mathematical and simulation models of epidemic processes is carried out. The disadvantages of classical models are revealed. Specific characteristics inherent in epidemic processes have been determined, which must be taken into account when constructing mathematical and simulation models. A review of deterministic compartment models is carried out. Various methods and approaches to the construction of statistical models of epidemic processes are considered. The types of problems that are solved using machine learning are analyzed.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

Chumachenko

2022

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“…Experimental results show that the SOMFTS technique is advantageous for future forecasting of COVID19 cases. A linear regression model is implemented on the clinical dataset of COVID19 patients in Ukraine that is capable of making accurate forecasts of COVID-19 future cases [13]. It provides the time-series prediction of confirmed, deaths, and recovered cases in Ukraine.…”

Section: Related Workmentioning

confidence: 99%

Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

Gam

Cao

Seow

2022

2022 IEEE 21st International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS)

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There are questions about how to accurately prepare with the correct number of resources for distribution in order to properly manage the healthcare resources (e.g., healthcare workers, Masks, ART-19 TestKit) required to tighten the grip on the COVID-19 pandemic. Mathematical and computational forecasting models have well served the means to address these questions, as well as the resulting advisories to governments. A workflow is proposed in this research, aiming to develop a forecasting simulation that makes accurate predictions on COVID-19 confirmed cases in Singapore. According to the analysis of the prior works, six candidate forecasting models are evaluated and compared in the workflow: polynomial regression, linear regression, SVM, Prophet, Holt's linear, and LSTM models. The study's goal is to determine the most suitable forecasting model for COVID-19 cases in Singapore. Two algorithms are also proposed to better compute the performance of two models: the order algorithm to determine optimal degree order for the polynomial regression model , and the optimizing algorithm for the Holt's linear model to calculate the optimal smoothing parameters. Observed from the experiment results with the COVID-19 dataset, the Prophet method model achieves the best performance with the lowest Root Mean Square Error (RMSE) score of 1557.744836 and Mean Absolute Percentage Error (MAPE) score of 0.468827, compared to the other five models. The Prophet method model achieving average accuracy range of 90% when forecasting the number of confirmed COVID-19 cases in Singapore for the next 87 days ahead. is chosen and recommended to be used as a system model for forecast the COVID-19 confirm cases in Singapore. The developed workflow will greatly assist the authorities in taking timely actions and making decisions to contain the COVID-19 pandemic.

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Generative Adversarial Networks for Synthetic Meteorological Data Generation

Viana,

Teixeira,

Soares

et al. 2024

Lecture Notes in Computer Science

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Machine Learning Model of COVID-19 Forecasting in Ukraine Based on the Linear Regression

Cited by 5 publications

References 18 publications

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

Generative Adversarial Networks for Synthetic Meteorological Data Generation

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