Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm

Yeşilkanat, Cafer Mert

doi:10.1016/j.chaos.2020.110210

Cited by 138 publications

(79 citation statements)

References 47 publications

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“…Fanelli and Piazza [6] analyzed the temporal dynamics of the coronavirus disease 2019 outbreak in China, Italy and France. Yesilkanat [20] estimated the near future case numbers for 190 countries in the world using random forest algorithm. Sahin and Sahin [12] estimated the cumulative cases of COVID-19 using fractional nonlinear grey Bernoulli model.…”

Section: Introductionmentioning

confidence: 99%

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

Ballı

2021

Chaos, Solitons & Fractals

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Section: Introductionmentioning

confidence: 99%

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

Ballı

2021

Chaos, Solitons & Fractals

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“…A search, on the same date, on for studies targeting the COVID condition showed more than 3100 registered studies [ 2 , 3 ]. This disease has affected the whole world with a large number of studies and review articles appearing about different aspects of this disease: possible symptoms and treatments [ 4 , 5 , 6 , 7 , 8 , 9 ], technological tools to combat the virus [ 10 , 11 , 12 , 13 ], epidemiological models of virus transmission [ 14 , 15 , 16 ], the detection of fake news related to COVID-19 [ 17 , 18 ], etc. This huge, ever-growing amount of work reflects the need to gather knowledge about this virus in all possible ways.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Mortality Analysis in Patients with COVID-19

Sánchez-Montañés

Rodríguez-Belenguer

Serrano-López

et al. 2020

IJERPH

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This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.

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“…In addition, a machine learning based random forest model has been used to forecast the number of COVID-19 cases with a mean correlation coefficient R 2 of 0.914. 22…”

Section: Resultsmentioning

confidence: 99%

Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

Jayatilaka

Hassan²,

Marikkar

et al. 2020

Preprint

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The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

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Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm

Abstract: Highlights Daily number of COVID-19 cases was estimated by the random forest method. Case estimates by random forest were mapped and compared to actual data. Random forest performed well in estimating the number of cases in the near future.

Cited by 138 publications

References 47 publications

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods

Machine Learning for Mortality Analysis in Patients with COVID-19

Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

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