Piece-wise linear regression: A new approach to predict COVID-19 spreading

Senapati, Apurbalal; Maji, Soumen; Mondal, Arunendu

doi:10.1088/1757-899x/1020/1/012017

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…In order to overcome the above (non-linearity) problems, other methods like Polynomial Regression [11], and Piecewise Linear Regression has been used [7]. The idea behind Piecewise Linear Regression is akin to that of Linear Regression, with the key distinction being that instead of using a single line to fit the data, multiple lines are employed.…”

Section: Piecewise Linear Regressionmentioning

confidence: 99%

“…To address the aforementioned issues, alternative methods such as Polynomial Regression [11] and Piecewise Linear Regression has been employed [7]. Polynomial regression shares similarities with linear regression in that it seeks to model the relationship between an independent variable and a dependent variable.…”

Section: Related Workmentioning

confidence: 99%

“…It does not perform well in the case of non-linear data points but most of the real-life data are not linear in nature. To address this issue, researchers have developed a modified version of linear regression known as the piecewise linear regression model [6,7]. The primary concern of the model is to find the breakpoints and partitioned the data based on these breakpoints.…”

Section: Introductionmentioning

confidence: 99%

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Correlation Coefficient-based Breakpoint detection @Piecewise Linear Regression

Senapati

2023

Preprint

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Linear Regression is a traditional supervised Machine Learning technique commonly used for prediction. The approach is frequently used in many applications involving sequencing or forecasts for the time-series domain. The efficiency of the model depends on the characteristics of the data and some other external factors. One notable limitation of this model is its reliance on linear data. When presented with non-linear data, its performance can be severely impacted. To address this issue, researchers have suggested an alternative approach known as piecewise linear regression. Piecewise linear regression is a variant of linear regression that involves partitioning the data points based on their linear characteristics, followed by the application of linear regression within each partition. The primary difficulty associated with piecewise linear regression is identifying the optimal approach for partitioning the data, which can be thought of as locating the breakpoints. The main objective of this paper is to introduce a strategy for partitioning and identifying breakpoints based on the correlation properties of the data points. The effectiveness of this approach was evaluated using open-access COVID-19 data in the context of a prediction model. The findings indicate that the proposed partitioning and breakpoint identification method outperforms other existing systems, suggesting that it is a viable option that is relatively close to the optimal solution.

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Section: Piecewise Linear Regressionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correlation Coefficient-based Breakpoint detection @Piecewise Linear Regression

Senapati

2023

Preprint

Self Cite

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“…Regression model such as Linear and Multiple Linear Regression techniques were applied to the dataset to visualise the trend of the affected cases [26]. Similarly, a piece-wise linear regression model was developed by [27] to predict new cases of COVID-19. The goal of such predictions was to take strategic control of the disease.…”

Section: Related Workmentioning

confidence: 99%

Modeling the Impact of Climate Factors on COVID-19 Transmission in Nigeria

Mabu¹,

Modu²,

Bukar³

et al. 2022

IJDSA

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Due to spatial and temporal changes in climate, the incidences of COVID-19 is much more higher in some parts of America, Europe and Asia by comparing with Saharan and sub-Saharan Africa. Several studies show the link between climate factors (e.g., temperature rainfall and humidity) and COVID-19 occurrence will be used to aid intervention planning, prevention and control policies. Nigeria is a country that is sensitive to spatial and temporal variability in the occurrence of climate factors, and fully knowing it link with COVID-19 is crucial towards mitigation. In this study, we examined the link by firstly deployed convenience sampling to select three cities (Abuja, Kano and Lagos) where the international airports of Nigeria are situated and also the index case of the country came through Lagos. Secondly, we used the reported cases of COVID-19 from its onset in the country (22/02/2020) up to 21/05/2021. Thirdly, lagged regression was used to explore the link between weekly counts of COVID-19 cases and weekly recorded average of the climate data; including the google trend index as a measure of the populace health seeking behaviour. We found a significant influence of temperature, humidity and heath seeking trend, with a very negligible contributions of precipitation to the occurrence of the COVID-19 in the states investigated. This result will assist policy makers with a prior knowledge to plan for non-pharmaceutical interventions in anticipation of possible outbreak.

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“…In addition, in Piece-wise linear regression: A new approach to predict COVID-19 spreading, scientists describe a piecewise linear regression, which outperforms the other current models. [6]. This study discusses the predictive model, polynomial regression and support vector machine regressors, for COVID-19 in Mainland China.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 data prediction and visualization in mainland China

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19.

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Piece-wise linear regression: A new approach to predict COVID-19 spreading

Cited by 5 publications

References 17 publications

Correlation Coefficient-based Breakpoint detection @Piecewise Linear Regression

Correlation Coefficient-based Breakpoint detection @Piecewise Linear Regression

Modeling the Impact of Climate Factors on COVID-19 Transmission in Nigeria

COVID-19 data prediction and visualization in mainland China

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