Analysis of the Changes in 2019-nCov Before and After the Implementation of the "Required Swab PCR-Test for Entry into West Kalimantan via Air Transport" Policy

Abstract

Implementing policies during the 2019-nCov pandemic are expected to reduce the number of cases added every day. West Kalimantan is one of the provinces that implements a policy of obliging to include negative results on the PCR-test swab every time they use air transportation to West Kalimantan. In this study, we wanted to know whether there were differences in data behavior before and after implementing the policy. These differences can be analyzed simply by looking at the descriptive statistics of the data. Furthermore, in this study, a time series analysis was also carried out, and the data patterns and the suitable models representing the data. Time series analysis is also needed to predict the next 5 days related to the addition of 2019-nCov cases in West Kalimantan. In modeling, modifications have been made by partitioning the data into two data, namely data before the policy is implemented and the rest is data after the policy is implemented. The result shows that the suitable model for before and after the policy is applied is ARIMA (1,0,0) and ARIMA (7,0,0)(1,0,0)7, respectively. This model shows a better performance in translating problems than using the entire data as input in modeling. The smaller MSE value indicates this than using the ARIMA model (1,0,0) for the entire data (without partition). Therefore, in the prediction stage, a model with partitioned data is used. The results showed that there was a decrease in daily cases in the next five days.