Application of machine learning for predicting the spread of COVID-19

Zhong, Xiaoxu; Ye, Yukun

doi:10.48550/arxiv.2204.04364

Cited by 2 publications

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“…ML has been successful in many areas, such as computer vision, detecting fraud, online advertising, automatic driving, and robotics [ 1 ]. The success of ML applications in fields, such as treatment, disease diagnosis, patient monitoring, epidemiology, and drug discovery, among others, makes it possible to predict the potential and influence of ML tools in designing and implementing new and better solutions in these areas [ 2 , 3 ]. For instance, Ref.…”

Section: Introductionmentioning

confidence: 99%

On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events

et al. 2023

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The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan’s daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model.

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Section: Introductionmentioning

confidence: 99%

On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events

et al. 2023

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“…The success of applying ML in the areas of disease diagnosis, treatment, patient monitoring, drug discovery, and epidemiology, among others, allows for foreseeing the potential and impact of ML in the design and implementation of new and better solutions in the mentioned areas [4,5]. For example, in [6], a review of the importance of using drones, AI, the internet of things (IoT), and block chain, among other emerging technologies, was prepared to address the pandemic; for example, [7] uses blockchain to propose a method that prevents manipulation of information, such as COVID-19 test results.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions

et al. 2022

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Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants.

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Application of machine learning for predicting the spread of COVID-19

Cited by 2 publications

References 0 publications

On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events

On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events

Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions

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