Mining The Basic Reproduction Number (R0) Forecast For The Covid Outbreak

• Main Author: Rajogoval, Illayakantthan
• Format: Monograph
• Language: English
• Published: Universiti Sains Malaysia 2022
• Subjects: T Technology; TJ Mechanical engineering and machinery
• Online Access: http://eprints.usm.my/55903/1/Mining%20The%20Basic%20Reproduction%20Number%20%28R0%29%20Forecast%20For%20The%20Covid%20Outbreak_Illayakantthan%20Rajogoval.pdf; http://eprints.usm.my/55903/

The popular method used to measure the spread of COVID-19 is by calculating the Basic Reproduction Number, R0. This number is a globally used metric to describe the COVID-19 outbreak globally. Existing studies applied different models including SIR, SIRD, SEIR, SPIR, MCMC, Statistical exponential growth, statistical likelihood estimation and dynamic transmission models to evaluate COVID-19 R0. However, there is no exact best model to ensure accurate Basic Reproduction Number, R0. Besides, the essential attributes to return the best prediction model for COVID-19 R0 remains unclear. Therefore, this study aims to identify and evaluate the attributes and parameters associated with the development of the Basic Reproduction Number, R0 models, classify the data used in existing Basic Reproduction Number R0 models, develop a predictive classification model for the Basic Reproduction Number, R0 and to assess and enhance the accuracy of the Basic Reproduction Number, R0 prediction. Two case studies related to the COVID-19 outbreak in Malaysia and Malta were used. The study attributes are mainly about daily cases, deaths, hospitalization, ICU admission and Isolation. Waikato Environment for Knowledge Analysis (WEKA) version 3.8 was adopted for data mining analysis at two levels of classification stages. Data pre-processing procedures ensure outlier and extreme values and irrelevant attributes are discarded. For classification analysis, J48, Naïve Bayes, Random Forest and SMO were used on three different testing options: full training set, 10-fold cross validation and 66% split to yield the percentage of classification accuracy. Accuracies obtained for first-level classification ranged from 70.6349% to 100% for the Malaysia dataset and 89.6266% to 100% for the Malta dataset. viii Meanwhile, for second-level classification, the accuracies ranged from 67.8751% to 100% for the Malaysia dataset while 58.9212% to 100% for the Malta dataset. All the classification accuracies obtained were above the baseline accuracy. The COVID-19 Basic Reproduction Number, R0 a predictive model is developed using a linear regression classification algorithm to predict the COVID-19 Basic Reproduction Number, Robased on the actual COVID-19 Basic Reproduction Number, R0. The study identified new cases, deaths, hospitalization, and ICU admission as important attributes to derive accurate COVID-19 Basic Reproduction Number, R0