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On data that are not normally distributed, classical linear regression is developed into the Generalized Linear Model (GLM). One of them is logistic regression which is used for binary discrete data. A different strategy is to consider all variables (response and predictors) to be random observations from the joint probability distribution. A model with dependencies should be used because some factors are dependent. The copula model, one of which is the Gaussian copula, is a popular technique for creating multivariate models with dependencies. The Gaussian copula regression model (GCMR) is a general structure that can be used to model any form of dependent response. The Gaussian copula blends the ease of interpretation of marginal modeling with the flexibility of the dependency structure specification. In this case, the estimated parameter is the regression coefficient and the error is expressed in the dependency structure. The model estimation uses the maximum likelihood method. This paper aims to apply the GCMR model to time series data where the dependent variable is discrete with a binomial distribution. A case study was conducted to see the effect of weather factors on Covid cases in DKI Jakarta. The regression model used is binomial data logistic regression with the dependency structure expressed in the correlation matrix with the assumption that the error is obtained from the ARMA(p,q) process. The dependent variable is the number of Covid-19 cases and the independent variable is the maximum temperature, average temperature and humidity. The data are a daily time series with a range of March 1, 2020 to April 30, 2022. Based on the GCMR model, Maximum temperature, average temperature and Humidity have a significant influence on the number of Covid-19 cases in Jakarta. For this case study, the results show that the GCMR model is better than logistic regression analysis. [ABSTRACT FROM AUTHOR]

The COVID-19's rapidly spread in Indonesia is a serious concern for government. Various government policies, such as large-scale social restrictions, which is called as PSBB, and the imposing restrictions on community activities, be called as PPKM, have been applied to slow the spread of COVID-19. These policies were supposed to control people activities in many areas, especially that will invite the crowd happening. In this paper, the control charts be explored to obtain the overview of the daily new COVID-19 cases number, which is considered as a production process. The proportion of positive COVID-19 cases be observed and monitored using P-control chart. The data used is from the specimen COVID-19 test in DKI Jakarta Province, since April 2020 to January 2021. It is obtained that the process of daily new COVID-19 cases number has not been statistically controlled. It is detected some mean shifts of proportion in some point of times. Several findings indicate an upward trend, suggesting that the proportion of positive cases COVID-19 is rising. The public's conduct in Jakarta has served as a model for the government in designing policies to tackle the spread of Covid-19. However, due to the high mobility of Jakarta residents and its lack of consistency in the region, the COVID-19 process still remained out of control. [ABSTRACT FROM AUTHOR]