The COVID-19 has been pandemic in the world and has resulted in so many deaths due to being infected by the virus, therefore this study aims to determine a suitable model in estimating the number of deaths due to being infected by the COVID-19. This study focus in Lampung, Indonesia. The analysis of the death number was using three methods, there waspoisson regression, negative binomial regression (NBR), and generalized poisson regression (GPR). From the results, three predictor variables have significant effect to the model, there was positive cases of COVID-19, number of poor people, and life expectancy, while population density per km2 has no significant effect. The best estimation model has smallest AIC and BIC values, and the poisson regression method is the best among other methods. Keywords: Poisson Regression, NBR, GPR, COVID-19

The spatial panel data model is the construction of a regression model that is used to explain the spatial dependence on panel data. Space dependence may apply between adjacent areas, as in the economic field. This should not be ignored because if the freedom between regions is not fulfilled. The spatial panel data model may be in the form of a SAR, SEM or GSM model. In this study, the spatial panel data model is used to model regional income in districts/cities in West Java, the results of the analysis obtained are that the SEM model with random effect is the best model because value of R^2-adj is 97.64%.

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Statistical modeling often involves data which has a distribution of the exponential family. Generalized Linear Model (GLM) was developed to model these data by using a link function between the mean of the response variable and the linear form of the predictor variable. If the data of the response variable comes from several census blocks that are taken randomly, then the diversity between census blocks should not be ignored because it can increase bias. The Generalized Linear Mixed Model (GLMM) is a method that can capture a variety of random effects. However, it does not rule out if there are many predictor variables involved in the model and we use GLMMLasso as a combination method of GLMM and Lasso to shrink the parameter coefficients to zero, it is used to reduce the variance. In this study, a simulation was conducted to GLMMLasso use different numbers of predictor variables and different values of shrinkage coefficients to determine which shrinkage coefficient values have a minimum bias on parameter prediction. Acute Respiratory Infection (API) data on children in Jakarta is used to know the factors that could cause increased cases. The simulation result is the shrinkage coefficient which produces a minimum bias is 30, and the R2 value of data analysis on the model is 99.24%