Modeling with generalized linear model on covid-19: Cases in Indonesia

Abstract

The ongoing Covid-19 outbreak has made scientists continue to research this Covid-19 case. Most of the research carried out is on the prediction and modeling of Covid-19 data. This study will also discuss Covid-19 data modeling. The model that is widely used is the linear model. However, if the classical assumption of normality is not met, a special method is needed. The method that can overcome this is the generalized linear model (GLM), with the assumption that the data is distributed in an exponential family. The distribution used in this study is the Gaussian, Poisson, and Gamma distribution. Where the three distributions will be compared to get the best model. The variables used in this study were the number of confirmed Covid-19 cases per day and the number of deaths due to Covid-19 per day. This study also aims to see how much influence the confirmation of Covid-19 has on the number of deaths due to Covid-19 per day. By using 3 types of exponential family distribution, the best result is the Gaussian distribution GLM. Selection of the best model using Akaike Information Criterion (AIC).

Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19

“…In recent times, epidemiology and medical research have witnessed an accelerated utilization of data-driven approaches to comprehend and predict disease transmission dynamics [9], [10]. Leveraging the power of advanced statistical techniques and machine learning, researchers have made significant strides in modeling the spread of COVID-19 at a population level [11], [12]. However, there remains a compelling need to focus on predicting individual-level manifestations of the disease, especially the likelihood of experiencing particular symptoms associated with COVID-19.…”