The impact of large-scale social restriction and odd-even policies on transportation mobility and COVID-19 positivity rates in Jakarta

SARS-Cov-2 spreads quickly and continues to do so to this day. One way to limit the spread is by limiting people's mobility through transportation. The Provincial Government of Jakarta has implemented the Large-Scale Social Restriction Transitional Phase-1 since June 5th, 2020, to limit people mobility and the odd-even policies August 3rd, 2020, to limit private vehicles. To assess the effectiveness of these policies against the positivity rate of COVID-19 cases in Jakarta, we used data exploration and significant tests (pre-and post-condition). The result shows that the odd-even policy significantly impacts private transportation mobility, mobility in public transportation, and the COVID-19 positivity rate in Jakarta. The relationship between those three parameters is linearly significant. The odd-even policy stimulates people to switch from private to public transportations and increases the positivity rate of COVID-19. The odd-even policy effectively reduces the mobility of private transportation but insignificantly reduces the positivity rate of COVID-19 in Jakarta. The results can be used as insights for policy decision-makers to manage the COVID-19 pandemic. © 2023 Author(s).

Lessons Learned from Delta and Omicron Variants Transmissions Leveraging Clustering Approach by Sub-Districts in Jakarta

Various variants of COVID-19 have entered Indonesia, such as the delta and the omicron variants. The delta variant has a higher severity than the omicron variant, but the transmission rate for the omicron variant is much faster. The government encourages citizens to get booster vaccines to reduce the effect of the delta and omicron variants. The booster vaccine produced a better effect on citizens than on people who received only the two doses. Therefore, in this study, we observe the transmission of COVID-19 and the vaccine locations on the sub-districts level using the clustering approach. The data we use are COVID-19 positive cases, died, treated, and self-isolated cases. Meanwhile, the vaccination data are 1st dose, 2nd doses, stage 3 of 1st dose, and stage 3 of 2nd doses. The Dunn Index and Hubert Index methods determined the best number of clusters before the clustering process. Silhouette and Davies Bouldin are used to find better clustering between Fuzzy C-Means, K-Means, and Partition Around Medoids (PAM). The results obtained from this study showed that the K-Means method was the best with the best number of clusters, namely 3. Jagakarsa and Kebon Jeruk entered high levels at the time of the delta variant due to the COVID-19 case and vaccination spread. However, Jagakarsa and Kebon Jeruk dropped to the intermediate level during the omicron variant. The benefit of this study is to help the government pay more attention to high COVID-19 cases and low vaccine distribution. © 2022 IEEE.

Data Analysis and Synthesis of COVID-19 Patients using Deep Generative Models: A Case Study of Jakarta, Indonesia

Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset. © 2022 IEEE.

The Effects of COVID-19 and Workplace Mobility to Stock Price and Exchange Rate in Indonesia: An Econometric Approach

The COVID-19 pandemic has reached its 20th month in Indonesia and still damaged various sectors, particularly economy. The policies imposed by the government impacted mainly the stock price. exchange rate, and people mobility in Indonesia. However, there are limited studies that incorporate these variables in Indonesia context. Thus, this study investigates the relationship between the COVID-19 pandemic, stock price, exchange rate, and workplace mobility simultaneously. This study employs Vector Autoregressive (VAR) as the analysis considering its advantages in finding the causal relationship between variables and periodic interpretation using Impulse Response Function (IRF). The VAR results show that from the Granger Causality Test, it turns out that the shocks from COVID-19 positivity rate and mobility in workplaces caused the changes in stock price and exchange rate. On the other hand, the IRF results exhibit the depreciating responses of stock price and exchange rate due to the shocks of COVID-19 positivity rate and mobility are enormous in the short term. In the longer term, the stock price response needs a longer time to return to the initial condition than the exchange rate. Therefore, further policy evaluation and formulation become essential to maintain the stock price and exchange rate, mainly due to the effect of COVID-19 and workplace mobility. © 2022 IEEE.

Regional Economy Condition in Indonesia during COVID-19 Pandemic: An Analysis using Teaching Learning-Based Fuzzy Geodemographic Clustering

COVID-19 has impacted Indonesia and caused an economic recession during 2020. The economic condition in Indonesia should be evaluated through the regional economic condition. One well-known approach to do a regional analysis is a geodemographic analysis using Fuzzy Geographically Weighted Clustering (FGWC). However, FGWC is still weak against the local optima, so it is necessary to use an optimisation algorithm to enhance it. This study proposes a new approach of FGWC enhancement using Elicit Teaching-Learning Based Optimisation (ETLBO) to analyse the regional economic condition in Indonesia. We compare ETLBO with previously implemented optimisation algorithms in FGWC, such as Particle Swarm Optimisation (PSO) and Intelligent Firefly Algorithm (IFA). This study found that ETLBO performs well in identifying Indonesia's regional economic condition. Moreover, the clustering results showed the difference of problematic sectors. We also found that the provinces in Java Island joined into a cluster and have problems in many sectors. This study can be used as the basis for the evaluation of regional economic conditions in Indonesia. © 2022 IEEE.

COVID-19 Mortality Risk Factors Using Survival Analysis: A Case Study of Jakarta, Indonesia

At the end of 2021 Q2, coronavirus disease 2019 (COVID-19) in Indonesia experienced a continuous increase in positivity and mortality rates. There are limited studies regarding the factors of COVID-19 mortality in Indonesia with a more balanced dataset. The previous studies only implemented logistic regression, sensitive to the imbalanced dataset. Meanwhile, other countries implemented survival analysis to overcome the problem. Most survival analyses using Cox proportional hazard (CPH) model require the variables to be time-independent. To this end, this study aims to identify the risk factors for COVID-19 mortality in Indonesia using a survival analysis approach using Jakarta as a case study. We use the Piecewise Exponential Model (PEM) to overcome the time-dependent problem in CPH. The findings show that the COVID-19 mortality does not differ the gender. In contrast, it differs the elderly with 3.5 times higher to be deceased. Dyspnea, malaise, and pneumonia are the primary symptoms associated with COVID-19 mortality. From the comorbidities, diabetes and chronic disease are related to COVID-19, while hypertension and heart attack are still considerable in clustered contexts. The advanced treatment using intubation and extra corporeal membrane oxygenation (ECMO) produces a relatively large hazard risk of COVID-19 mortality. Based on the findings, we suggest that collaboration among the government, society, and hospitals is vital in overcoming the COVID-19 pandemic and minimizing the COVID-19 death.

The Impact of Large-Scale Social Restriction and Odd-Even Policies during COVID-19 Pandemic to Traffic Congestion and Air Pollution in Jakarta

The COVID-19 pandemic has had a global impact on transportation mobility and air pollution, including Jakarta as the capital and busiest city in Indonesia. This paper reports the impact of two policies imposed by the Governor of Jakarta, namely the odd-even and the large-scale social restriction (PSBB) transitional phase-1, against the traffic congestion and air pollution quality in Jakarta during the COVID-19 pandemic. This paper investigates the odd-even and PSBB policy impact using paired T-Test. Moreover, this study assesses the relationship between traffic congestion and air pollution using the Pearson correlation. The result shows that the odd-even policy does affect significant only on MH Thamrin Street. Furthermore, the odd-even policy does not significantly affect air pollution reduction in Jakarta. This study also found that there is no meaningful relationship between traffic congestion and air pollution. These results can be used to reference future data-driven policy improvement on traffic congestion and air pollution management in Jakarta and other cities. © 2021 IEEE.

An evidence-based culture: COVID-19 positivity factors during the asymptomatic occurrence in Jakarta, lndonesia

Coronavirus disease 2019 (COVID-19) has been a global disaster, with over 746,312 confirmed cases and still counting in Indonesia, especially Jakarta, which has about 50 per cent asymptomatic confirmed cases. This paper aims to investigate the persistent factors of COVID-19 diagnosis using four scenarios of asymptomatic inclusion. We use Bayesian Logistic Regression to identify the factors of COVID-19 positivity, which can address issues in the traditional approach such as overfitting and uncertainty. This study discovers three main findings: (1) COVID-19 can infect people regardless of age;(2) Among twelve symptoms of coronavirus (COVID-19), five symptoms increase the COVID-19 likelihood, and two symptoms decrease the possibility of COVID-19 infection;and (3) From an epidemiological perspective, the contact history rises the probability of COVID-19, while healthcare workers and people who did travel are less likely to become infected from COVID-19. Therefore given this study, it is essential to be attentive to the people who have the symptoms and contact history. Surprisingly, health care workers and travelers who apply health protocols strictly according to the rules have a low risk of COVID19 infection. © 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Impact of COVID-19 large scale restriction on environment and economy in Indonesia. (Special Issue: Covid-19.)

COVID-19 has a severe and widespread impact, especially in Indonesia. COVID-19 was first reported in Indonesia on March 03, 2020 then rapidly spread to all 34 provinces by April 09, 2020. Since then, COVID-19 is declared a state of national disaster and health emergency. This research analyzes the difference of CO, HCHO, NO2, and SO2 density in Jakarta, West Java, Central Java, and South Sulawesi before and during the pandemic. Also, this study assesses the effect of large scale restrictions on the economic growth during COVID-19 pandemic in Indonesia. In a nutshell, the results on Wilcoxon and Fisher test by significance level a=5% as well as odds ratio showed that there are significant differences of CO density in all regions with highest odds ratio in East Java (OR=9.07), significant differences of HCHO density in DKI Jakarta, East Java, and South Sulawesi. There are significant differences of NO2 density before and during public activities limitation in DKI Jakarta, West Java, East Java, and South Sulawesi. However, the results show that there are no significant differences of SO2 density in all regions. In addition, this research shows that there are significant differences of retail, grocery and pharmacy, and residental mobility before and during the COVID-19 pandemic in Indonesia. This research also shows that during the COVID-19 pandemic there are severe economic losses, industry, companies, and real disruptions are severe for all levels of life due to large scale restrictions.

Forecasting of COVID-19 Cases in Jakarta using Poisson Autoregression

COVID-19 is currently become a global problem, including in Jakarta, Indonesia. There have been many approaches to predict COVID-19 occurrence, including the forecasting approach. However, the traditional forecasting method, particularly machine learning, often does not consider the condition of the data, although it has forms of the count, such as the number of cases. This study employs an autoregression model using Poisson distribution in predicting the COVID-19 future cases, namely the positive and recovery number. We compare the Poisson Autoregression with several well-known forecasting methods, namely ARIMA, Exponential Smoothing, BATS, and Prophet. This study found that Poisson Autoregression could create an accurate prediction with MAPE below 20% and tend to follows the actual data for the next 8 to 14 days to the future. Thus, this approach can forecast the future cases of COVID-19 and other cases that use count data in Jakarta, like the number of citizen complaints or transportation context. © 2021 IEEE.

Contributing Clinical Attributes to COVID-19 Mortality in Jakarta: Machine Learning Study

Since December 2019, we have lived in a pandemic era of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Medical records of COVID-19 patients have been reported and analyzed worldwide. The Health Agency of Jakarta, Indonesia, collected clinical symptoms, demographics, travel history, and mortality information from March 2020 up to now. Despite massive research on COVID-19 patients' data, the significant clinical symptoms that lead to COVID-19 mortality in Jakarta have not been well described to the best of the authors' knowledge. We extracted the COVID-19 records in Jakarta and compared them between patients who were discharged and deceased. This paper examines each clinical symptom's importance to mortality using machine learning techniques, namely weighted Artificial Neural Network, Decision Tree, and Random Forest. We observed that Pneumonia, Shortness of Breath, Malaise, Hypertension, Fever, and Runny Nose are the top six significant clinical symptoms that lead to deaths in Jakarta. We suggest medical experts become more cautious with these symptoms. Also, in medical facilities, these symptoms can be used as prescreening before entering the facilities. © 2021 IEEE.

Micro, Small, and Medium Enterprises' Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering

The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to cluster the business vulnerability in Indonesia. We performed the data analysis with the dataset from Indonesia's national socioeconomic and labor force survey (SUSENAS and SAKERNAS). We first compared the performance of FPA with traditional FGWC, as well as several known optimization algorithms in FGWC such as Artificial Bee Colony, Intelligent Firefly Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm. Our results showed that FPAFGWC has the best performance in optimizing the FGWC clustering result in the business vulnerability context. We found that almost all of the regions in Indonesia outside Java Island have vulnerable businesses. Meanwhile, in most of Java Island, particularly the JABODETABEK area that is the national economic backbone, businesses are not vulnerable. Based on the results of the study, we provide the recommendation to handle the gap between the number of micro and small enterprises (MSMEs) in Indonesia.