ABSTRACT
The spread of COVID-19 in Indonesia is still classified as a pandemic until October 31, 2022. Even though the endemic has been enforced in several nations worldwide. However, the fact that people's mobility is increasing means that this condition can increase the number of new cases of COVID-19. The Indonesian government remains vigilant about any decisions that will be taken to maintain the stability of the country's health sector, economy, and population mobility. First, The purpose of this our research is to forecast of daily positive confirmed and daily mortality for the next 13 days using COVID-19 epidemiological data in Indonesia, i.e. DKI Jakarta and West Java. Second, the forecasting model uses a deep learning approach, i.e. LSTM and ARIMA. furthermore, The LSTM method and ARIMA modeling results are compared based on their respective to regions. Finally, The LSTM method has good model performance and the ability to forecast COVID-19 cases based on RMSE and MAPE. © 2022 IEEE.
ABSTRACT
The DKI Jakarta government is one of the cities that respond to the needs of urban communities through the innovation of the JAKI application. The JAKI application is designed to facilitate effective public services in the pandemic era, provide comfort in activities, get accessible health services, and provide full access to learning for the people of DKI Jakarta COVID-19 pandemic. This study uses a qualitative descriptive method, and this method explains or describes the conditions and attitudes of the object under study. The results show that using the JAKI application has benefits in providing services to the community during the COVID-19 pandemic. Based on the data obtained, the JAKI application can facilitate access to public services by displaying features that are easily understood by the public, such as Jakarta Siaga (JAKsiaga), Jakarta School (JAKschool), Jakarta Aman (JAKaman), Jakarta Air Pollution Standard Index (JAKISPU), Jakarta Lapor (JAKlapor), and Jakarta Respon (JAKrespon). In the JAKI application, all data related to the handling of COVID-19 has been described in full and is transparent so that it can be accessed by all levels of society and makes it easier for the public to provide access to services in the form of smart living in the era of the COVID-19 pandemic, to reduce the risk of spreading COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Diabetes mellitus (DM) comorbidity is one of the risk factors for coronavirus disease 2019 (COVID-19) mortality. This study aimed to determine the association of comorbid DM and mortality among COVID-19 confirmed cases in DKI Jakarta Province, controlled with confounding variables from March to August 2020. The study design was a retrospective cohort using cox proportional hazard regression, with a total sample of 1,480. The data consisted of 740 COVID-19 cases with and 740 without comorbid DM. The inclusion criteria were COVID-19 confirmed cases with polymerase chain reaction (PCR) laboratory testing reported to the DKI Jakarta Provincial Department of Health, and the exclusion criteria were pregnant women. The study result indicated that the Crude Hazard Ratio (CHR) of DM and mortality among COVID-19 confirmed cases was 7.4 (95% CI = 4.5-12.3, p-value<0.001), while the adjusted Hazard Ratio, controlled by covariates (comorbid hypertension and age groups) was 3.9 (95% CI = 2.2- 6.8, p-value<0.001). This concludes that the risk of death from COVID-19 cases with comorbid DM was 3.9 times compared to those without comorbid DM after controlling for confounding variables comorbid hypertension and age group (50 years and 50 years).