Modeling weekly COVID-19 new cases in Jakarta with growth curve time series models

Abstract

Growth curves are widely used in modeling the growth process. These curves can capture the pattern of the growth process to explain the characteristic of the growth process. In this research, growth curves are modelled to time series form models in the growth rate form by allowing lagged in the model. These growth time series models are applied in weekly new cases of positive COVID-19 in.Jakarta to forecast the number of new cases in the five weeks ahead in the testing data. Three schemes are made to be modelled. The growth curves that employed in this research are Logistic and Richard growth curves. Evaluations value of the Richard and Logistic models are evaluated by RMSEP of the forecasting result in testing data and RMSE of the prediction in training data to determine which model is better to fit in the weekly new cases of positive COVID-19 in.Jakarta. Time series model based on Richard growth curve seems to have better performance than Logistic growth curve in forecasting weekly new cases of COVID-19 in.Jakarta, because Richard time series models have smaller RMSE and RMSEP in almost all scheme than Logistic models. Other than that more testing data fall into the 95% confidence interval of weekly new cases in Richard time series model than Logistic time series model in all scheme.