Analytics of the COVID-19 Death According to the Vaccine Dose: Malaysia Case Study

Abstract

Vaccination is essential to minimize the transmission of the Covid-19 virus and its possible influence on morbidity and mortality rates around the world. In this paper, we first performed exploratory data analysis (EDA) on Covid-19 deaths in Malaysia depending on vaccine dose and next we used this vaccine dataset to predict the death cases using a machine learning algorithm. In EDA, we evaluated the vaccination dose impact according to each type of vaccines on the deaths count in Malaysia. The analysed data is compared to the number of dosages, comorbidity status and age variation. Aside from that, we observed the number of deceased people who were tested positive for Covid-19 after vaccination and the death count days after getting vaccinated. Our finding shows that the highest deaths number is mostly occurred to the person who received first dose vaccine, have more than one disease and lastly having the age range of 50 to 60 years old. In the second part of the paper, we used the death cases, daily cases, and daily vaccination to predict the death cases in which both the daily cases and the daily vaccination is used as the input factor. PSO-SVR with three kernel function (linear, polynomial, and radial basis function) is used to predict 30 days of death cases. From the prediction, the input factor of daily vaccination (RMSE=107.98) gives twice better accuracy compared to using the daily cases (RMSE=48.71). However, when using both input factor, the error reduces to (RMSE=16.77). The best kernel function for prediction is RBF in which for both input factors, RBF gives results of (RMSE=16.77) compared to linear (RMSE=17.43) and polynomial (RMSE=17.24).