Prediction of Covid-19 pandemic based on Regression

Mandayam, Ashish U; A.C, Rakshith; Siddesha, S; Niranjan, S. K.

doi:10.1109/icrcicn50933.2020.9296175

Cited by 18 publications

(7 citation statements)

References 2 publications

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“…Summed up direct relapse models utilized for the investigation of illness impacting factors, like straight relapse, Poisson relapse, and Logistic relapse, have restricted capacity to manage complex nonlinear connections among trademark factors [3][4][5]. Simultaneously, because of the restricted avoidance and control assets in the flare-up period, contrasted and the informative investigation of impacting factors, quick distinguishing proof of high-risk bunches is of more commonsense importance for working on the nature of logical navigation.…”

Section: Related Workmentioning

confidence: 99%

Computational tracking and intelligent forecasting of COVID-19 dynamic propagation

Prasanth¹,

Saran²,

Sathish³

et al. 2022

ijhs

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Coronavirus is a quickly spreading viral sickness that contaminates people and the day to day existence of individuals, their wellbeing, and the economy of a nation are impacted because of this lethal viral illness. Many endeavors have been directed to track down a reasonable and quick method for recognizing contaminated patients in a beginning phase. The spread of COVID-19 in the entire world has seriously jeopardized the humankind. The assets of the absolute biggest economies are worried because of the huge infectivity and contagiousness of this sickness. The ability of Machine Learning models and Deep Learning models can be actually used to estimate the quantity of impending cases impacted by COVID-19 which is by and by viewed as a likely danger to humankind. Specifically, seven standard determining models, in particular LR, LASSO, SVM, NN, XGB Regressor, Random Forest Regressor have been utilized in this review to estimate the compromising elements of COVID-19. Three kinds of assumptions are made by all of the models, similar to the amount of as of late spoiled cases, the amount of passings, and the amount of recoveries.

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Section: Related Workmentioning

confidence: 99%

Computational tracking and intelligent forecasting of COVID-19 dynamic propagation

Prasanth¹,

Saran²,

Sathish³

et al. 2022

ijhs

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show abstract

“…It is a variant of the multiple linear regression model, except that the best fit line is curved rather than straight. [1] have proposed carrying out contrast and comparing, Linear regression and Support Vector Regression by considering the data for different countries. The project was working of the setup of a comparison between different regression models and determine the best model for the implementation of the predictive analysis of the covid-19 cases.…”

Section: IImentioning

confidence: 99%

Covid19 Information Management and Services App

Agarkhed¹,

Kulkarni²,

Rani³

2022

IJRASET

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The Corona Virus Disease popularized as COVID-19 is a highly transmissible viral infection and has severe impact on global health. It has impacted the global economy also very badly. To overcome it many vaccines were introduced but the distribution and handling of such information has become necessary to provide a user-friendly interface for the layman in India. The project attempts to address such a problem. Furthermore, the application uses a machine learning model to detect the next number of cases based on the previous data time series data. Although the prediction is based on the time series data it provides a real-world hint into the number of cases. The algorithm is based on a hybrid regression model on top of support vector machines, Polynomial regression, Bayesian Ridge regression. The final predicted cases are presented on the dashboard in a linear graph.

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“…After processing data, we perform missing values by eliminating inaccurate data to become relevant. After completing the missing value, we separated several features to see the data type of each variable in the dataset and see if there is NA data or empty data [30]. Then delete the noise data so that the data becomes effective [31].…”

Section: Data Preprocessingmentioning

confidence: 99%

DeepCov: Effective Prediction Model of Covid-19 using CNN algorithm

Pradila

2022

ijicom

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Prediksi wabah Covid-19 adalah tugas yang menantang dan masalah yang rumit dalam kumpulan data yang sangat besar. Beberapa komunitas telah mengusulkan berbagai metode untuk memprediksi kasus positif Covid-19. Namun, teknik konvensional tetap menjadi kelemahan untuk memprediksi kasus tren yang sebenarnya. Dalam eksperimen ini, kami mengadopsi CNN untuk membangun model kami dengan menganalisis fitur dari kumpulan data besar Covid-19 untuk memprediksi wabah jangka panjang hingga menghadirkan pencegahan dini. Berdasarkan hasil percobaan, model kami dapat mencapai akurasi yang memadai dengan kerugian kecil. Dalam penelitian ini, kami menghitung fungsi yang menghasilkan RMSE 0,00070 dan MAPE 0,02440 untuk memprediksi kasus baru dan mendapatkan RMSE 0,00468 dan MAPE 0,06446 untuk memprediksi kematian baru. Oleh karena itu, metode yang kami usulkan dapat mencapai akurasi yang baik dalam memprediksi tren kasus positif wabah Covid-19 di Indonesia.

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Prediction of Covid-19 pandemic based on Regression

Cited by 18 publications

References 2 publications

Computational tracking and intelligent forecasting of COVID-19 dynamic propagation

Computational tracking and intelligent forecasting of COVID-19 dynamic propagation

Covid19 Information Management and Services App

DeepCov: Effective Prediction Model of Covid-19 using CNN algorithm

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