scholarly journals COVID-19 case fatality rates across Southeast Asian countries (SEA): a preliminary estimate using a simple linear regression model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
George R Puno ◽  
Rena Christina C Puno ◽  
Ida V Maghuyop
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Case Fatality ◽  
The Philippines ◽  
Southeast Asian ◽  
World Health ◽  
Simple Linear Regression ◽  
Content Type ◽  
Simple Linear Regression Model

PurposeThe purpose of this study was to determine COVID-19 preliminary case fatality rates (CFR) across Southeast Asian (SEA) countries.Design/methodology/approachThe study accessed the data on COVID-19 accumulated cases of fatalities and infections across SEA countries from the World Health Organization (WHO) website, covering the early days of March to May 21, 2020. The approach involved the computation of the CFR using the simple linear regression model. The slope of the regression line was the estimate of the CFR at a 95% confidence interval. The study also reviewed the different approaches of the SEA countries in dealing with the pandemic.FindingsAs of May 21, 2020, Singapore, Indonesia and the Philippines were the top three SEA countries with the highest record of COVID-19 infections. Brunei had one fatality, while Cambodia, Laos, Timor-Leste and Viet Nam had nil fatalities. Indonesia and the Philippines had the highest CFR with 6.66 and 6.59%, with R2 of 97.95 and 99.43%, respectively. Singapore had the lowest CFR (0.068%) despite high infections.Originality/valueIncreased CFR in Indonesia and the Philippines suggests that COVID-19 in the two countries is rising at an alarming rate. Strict implementation of shared management approaches to control the pandemic is seen to be closely associated with the decrease of CFR.

scholarly journals PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2423 ◽  
Author(s):  
Jiun-Jian Liaw ◽  
Yung-Fa Huang ◽  
Cheng-Hsiung Hsieh ◽  
Dung-Ching Lin ◽  
Chin-Hsiang Luo
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Concentration Estimation ◽  
Single Feature ◽  
The Difference ◽  
Pm2.5 Concentration

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

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