ABSTRACT
Sumatra Island is the third largest island with the second largest population in Indonesia which has the following eight provinces: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu and Lampung. The connectivity of these eight provinces in the economic field is very strong. This encourages high mobility between these provinces. During this Covid-19 pandemic, the high mobility between provinces affects the level of spread of Covid-19 on the island of Sumatra. The central government ordered local governments to implement a community activity restriction program called PPKM. In this article, a study is conducted on the impact of the PKKM program on the spread of Covid 19 on the island of Sumatra, Indonesia. The spread of Covid-19 is modeled using the Susceptible-Infected-Recovered-Death (SIRD) model which considers the mobility factor of the population. The model parameters were estimated using Approximate Bayesian Computation (ABC). The results of the study using this model show that the application of PKKM in several provinces in Sumatra can reduce the level of spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The ongoing global Coronavirus 2019 (COVID-19) pandemic poses a major threat. The spread of the COVID-19 virus is likely to occur from one location to another location due to the mobility of people. Many efforts and policies have been made by each country to reduce the spread of the COVID-19 outbreak. The imposition of lockdown and large-scale social restrictions or social distancing has been widely applied to limit the transmission of this virus among the community and provincial levels. Both policies have proven effective in reducing the spread of the COVID-19 virus. To obtain the overview of this case, many researchers were conducted. Here, the Generalized STAR (GSTAR) model was applied to model the increasing number of COVID-19 positive cases per day in six provinces in Java Island. The data was recorded simultaneously in six locations, namely in the Provinces of Banten, Jakarta, West Java, Central Java, Yogyakarta Special Region, and East Java. This paper proposes a new approach in constructing the weight matrix required to build the GSTAR model, namely Minimum Spanning Tree (MST). The weight matrix represents the relationship among observed locations. By using the MST, a topological (undirected graph) network model could be created to show the correlation, centrality, and relationship on the increase of COVID-19 positive cases among the provinces in Java Island. The GSTAR(1;1) with the inverse distance weight matrix using MST presents a good ability to predict the COVID-19 increasing cases of Java island. This is indicated by the final MAPE average score of 19.55. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
ABSTRACT
One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as Rt1∗, t1∗ = 1, 2, 3, …, 40. The main idea of this study is adding datum one by one as much as 10% - 15% of the total data Rt1∗, starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in Rt1∗, so t1∗ = −4. −3, −2, …, 40 and form five other data groups, Rt2∗, …, Rt6∗. Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, Rt6∗ is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
ABSTRACT
The current outbreak of coronavirus disease (COVID-19) has become a global issue to its quick and widespread over the world, including in Indonesia. More than 60% of positive cases came from Java island, therefore the proposed model focused on six provinces in this area. We developed a discrete-time stochastic epidemic model, such as Spatial-SIRD model, associated with the mobility of people by public transportation (air and land). Model parameters were estimated by fitting the data of October 22nd – 28th, 2020 and November 8th-November 14th, 2020 with the model. At the beginning of the estimation process, we used the coefficient of regression from the observation to estimate the range of parameters. Afterward, the order statistics method was carried out to determine the best parameters so we could forecast the number of infectious of each province. The SIR model was created by applying the regression rate of infection parameters before and after the long holiday from October 28 to November 1, 2020. The effect of this long holiday was that it could increase the number of cases so that there was a difference in the rate of infection. © IEOM Society International.
ABSTRACT
The movement of positive people Coronavirus Disease that was discovered in 2019 (Covid-19), written 2019-nCoV, from one location to another has a great opportunity to transmit the virus to more people. High-risk locations for transmission of the virus are public transportations, one of which is the train, because many people take turns in or together inside. One of the policies of the government is physical distancing, then followed by large-scale social restrictions. The keys to the policy are distance and movement. The most famous transportation used for the movement of people among provinces on Java is train. Here a Generalized Space Time Autoregressive (GSTAR) model is applied to forecast infected case of 2019-nCoV for 6 provinces in Java. The specialty of this model is the weight matrix as a tool to see spatial dependence. Here, the modified Inverse Distance Weight matrix is proposed as a combination of the population ratio factor with the average distance of an inter-provincial train on the island of Java. The GSTAR model (1; 1) can capture the pattern of daily cases increase in 2019-nCoV, evidenced by representative results, especially in East Java, where the increase in cases is strongly influenced by other provinces on the island of Java. Based on the Mean Squares of Residuals, it is obtained that the modified matrix gives better result in both estimating (in-sample) and forecasting (out-sample) compare with the ordinary matrix.