Background Since the first cases reported in Wuhan, China, in December 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread worldwide. In Indonesia, the first case was reported in early March 2020, and the numbers of confirmed infections have been increasing until now. Efforts to contain the virus globally and in Indonesia are ongoing. This is the very first manuscript using a spatial-temporal model to describe the SARS-CoV-2 transmission in Indonesia, as well as providing a patient profile for all confirmed COVID-19 cases. Method Data was collected from the official website of the Indonesia National Task Force for the Acceleration of COVID-19, from the period of 02 March 2020–02 August 2020. The data from RT-PCR confirmed, SARS-CoV-2 positive patients was categorized according to demographics, symptoms and comorbidities based on case categorization (confirmed, recovered, dead). The data collected provides granular and thorough information on time and geographical location for all 34 Provinces across Indonesia. Results A cumulative total of 111,450 confirmed cases of were reported in Indonesia during the study period. Of those confirmed cases 67.79% (75,551/111,450) were shown as recovered and 4.83% (5,382/111,450) of them as died. Patients were mostly male (50.52%; 56,300/111,450) and adults aged 31 to 45 years old (29.73%; 33,132/111,450). Overall patient presentation symptoms of cough and fever, as well as chronic disease comorbidities were in line with previously published data from elsewhere in South-East Asia. The data reported here, shows that from the detection of the first confirmed case and within a short time period of 40 days, all the provinces of Indonesia were affected by COVID-19. Conclusions This study is the first to provide detailed characteristics of the confirmed SARS-CoV-2 patients in Indonesia, including their demographic profile and COVID-19 presentation history. It used a spatial-temporal analysis to present the epidemic spread from the very beginning of the outbreak throughout all provinces in the country. The increase of new confirmed cases has been consistent during this time period for all provinces, with some demonstrating a sharp increase, in part due to the surge in national diagnostic capacity. This information delivers a ready resource that can be used for prediction modelling, and is utilized continuously by the current Indonesian Task Force in order to advise on potential implementation or removal of public distancing measures, and on potential availability of healthcare capacity in their efforts to ultimately manage the outbreak.

Tuberculosis (TB) infections remain a global health burden with a high incidence rate in South-East Asia, including Indonesia. TB control strategy is founded on early case detection and complete treatment to minimize transmission and prevent the emergence of drug resistance. However, many patients face challenges to comply with daily medication, causing many to adhere inconsistently or stop prematurely. Technological solutions could enhance adherence to treatment and support national screening and follow-up policies. These include telephone video communication, enabling health professionals to watch patients take their medication, address patients' concerns, and provide advice and support. This manuscript describes the outcome of a qualitative pilot study, based on a series of focus group discussions to assess the knowledge, attitudes, and behaviors, on the potential utilization of mobile technology for health purposes with a particular focus on TB treatment follow-up. The findings illustrate that general knowledge of mobile health technologies, of their legal framework of operations, and of their exact potential within the healthcare system is incomplete or poor. The novel findings are as follows: (a) the willingness of participants to learn about these technologies, (b) the open and welcoming attitude toward receiving such information even within frontline community settings, and (c) the willingness to back a government-supported, healthcare-driven set of such initiatives. Potential implementation barriers have also been highlighted. This study is an important first step toward understanding the attitudes and behaviors on utilizing mobile health technology for TB in Indonesia.

Background The COVID-19 pandemic has triggered a greater use of digital technologies as part of the health care response in many countries, including Indonesia. It is the world’s fourth-most populous nation and Southeast Asia’s most populous country, with considerable public health pressures. Objective The aim of our study is to identify and review the use of digital health technologies in COVID-19 detection and response management in Indonesia. Methods We conducted a literature review of publicly accessible information in technical and scientific journals, as well as news articles from September 2020 to August 2022 to identify the use case examples of digital technologies in COVID-19 detection and response management in Indonesia. Results The results are presented in 3 groups, namely (1) big data, artificial intelligence, and machine learning (technologies for the collection or processing of data); (2) health care system technologies (acting at the public health level); and (3) COVID-19 screening, population treatment, and prevention population treatment (acting at the individual patient level). Some of these technologies are the result of government-academia-private sector collaborations during the pandemic, which represent a novel, multisectoral practice in Indonesia within the public health care ecosystem. A small number of the identified technologies pre-existed the pandemic but were upgraded and adapted for current needs. Conclusions Digital technologies were developed in Indonesia during the pandemic, with a direct impact on supporting COVID-19 management, detection, response, and treatment. They addressed different areas of the technological spectrum and with different levels of adoption, ranging from local to regional to national. The indirect impact of this wave of technological creation and use is a strong foundation for fostering future multisectoral collaboration within the national health care system of Indonesia.

Lymphatic filariasis (LF) is a vector-borne disease caused by parasitic helminths and constitutes a serious public health issue in tropical regions. According to the World Health Organization (WHO), infected cases in Southeast Asia constitute 50% of the estimated 120 million infections globally. In Indonesia, LF is caused by all filarial species, and in 2018, 236 districts of a total of 514 districts in the entire country were declared as endemic areas. The global program to eliminate filariasis has been running for the last 19 years and has been conducted as a full national initiative for the last eight years in Indonesia. The study describes the surveillance of LF cases and prevalence in Indonesia for the past 17 years (2001–2017)—during the global and national LF elimination programs—using national registry-based data. The data demonstrate that the national program has been largely effective in the areas it has been active the longest, while there are provinces lagging behind in the successful suppression of LF. The high geographical fragmentation of the country, with the associated ecological parameters relating to LF incidence, likely play an important role in maintaining the highly varied incidence rate across Indonesia.

UNSTRUCTURED The COVID-19 pandemic has triggered the use of digital technologies in its handling in many countries, including Indonesia. This paper runs a literature review of publicly accessible information and news articles between September 2020 to July 2021 to discover the use of digital technologies in COVID-19 detection and response management in Indonesia. It is the world’s fourth most populous nation, and Southeast Asia’s most populous country which is battling surging cases and deaths. The results are presented into three groups, namely (i) Big Data, Artificial Intelligence and Machine Learning; (ii) Healthcare System Technologies; and (iii) Population Treatment. Some of them are innovated by government-academia-private sector collaboration during the pandemic, while the rest have pre-existed but gets promoted and intensified by the same multi-sectoral collaboration. Those digital technologies are developed to support three areas of COVID-19 management, detect, respond, and treatment. Further, this paper argues that the use of digital technologies is one of the results of the country’s effort to foster multi-sectoral collaboration.