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BACKGROUND In efforts to control the COVID-19 pandemic, the Government of Indonesia issued policies of micro-scale activity restrictions (PPKM Mikro/Micro PPKM) to limit community mobility by building COVID-19 command posts (Posko) at micro-scale (RT/RW) level and implementing Micro PPKM through community participation as a bottom-up approach. OBJECTIVE This study aims to describe the monitoring results of Micro PPKM implementation through the Bersatu Lawan COVID-19 digital mobile application, and the impact such granular information might have in curbing the COVID-19 pandemic. METHODS Data was collected from front-line public order forces (police and military) and Behavioral Change Ambassadors reports submitted using the BLC apps. The data was reported in real-time and analyzed by the integrated system of the Indonesia National Task Force for the Acceleration of COVID-19 Mitigation. RESULTS As of February 28, 2022, the total of COVID-19 command posts established were 29,913. A total of 151 million reports of micro PPKM activities were recorded using the BLC application in 44,770 villages/wards, 5,502 sub-districts, and 474 regencies/cities throughout the province. The top 5 most reported activities were 3M education and outreach, distribution of masks, enforcement of discipline, support for vaccination programs, and supervision of entering and exiting areas. CONCLUSIONS Indonesia adopted a bottom-up approach strategy to control the COVID-19 pandemic by utilizing a reporting system linked to community-driven activities at the lowest national administrative level. The reports were made available to different institutional users enabling uniformity in understanding of public health information. The system can potentially be repurposed for future healthcare emergency events. CLINICALTRIAL N/A

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.

BACKGROUND The COVID-19 cases are soaring in Asia. Indonesia, Southeast Asia’s most populous country is now on the second rank in Asia, just after devastating India. The compliance towards mask wearing, keeping a social distance and hand washing needs to be monitored to assess public behavioral changes that can reduce transmission. OBJECTIVE This study aims to evaluate this compliance in Indonesia between October 2020 and May 2021; and to demonstrate the use of Bersatu Lawan COVID (BLC) mobile application in in monitoring the compliance. METHODS Data was collected in real-time by the BLC app from reports submitted by personnel of the military services, policies, and behavioral change ambassadors. Then, the data was analyzed automatically by the system managed by Indonesia National Task Force for the Acceleration of COVID-19 Mitigation. RESULTS Between 1 October 2020 and 2 May 2021, the BLC app has generated more than 165 million reports, with 469 million people were monitored, and 124,315,568 locations were under observation in 514 districts/cities in 34 provinces in Indonesia. This paper groups them into four colored zones, based on the degree of compliance and analyses variations among regions and locations. CONCLUSIONS Compliance rates vary among the 34 provinces, and among districts and cities of those provinces. However, compliance to mask wearing seems slightly higher than social distancing. This suggest policy makers need to promote higher compliance in other measures, including social distancing and hand washing, whose efficacies have been proven to break the chain of transmission, when combined with masks wearing.

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.

Background COVID-19 cases are soaring in Asia. Indonesia, Southeast Asia’s most populous country, is now ranked second in the number of cases and deaths in Asia, after India. The compliance toward mask wearing, social distancing, and hand washing needs to be monitored to assess public behavioral changes that can reduce transmission. Objective This study aimed to evaluate this compliance in Indonesia between October 2020 and May 2021 and demonstrate the use of the Bersatu Lawan COVID-19 (BLC) mobile app in monitoring this compliance. Methods Data were collected in real time by the BLC app from reports submitted by personnel of military services, police officers, and behavioral change ambassadors. Subsequently, the data were analyzed automatically by the system managed by the Indonesia National Task Force for the Acceleration of COVID-19 Mitigation. Results Between October 1, 2020, and May 2, 2021, the BLC app generated more than 165 million reports, with 469 million people monitored and 124,315,568 locations under observation in 514 districts/cities in 34 provinces in Indonesia. This paper grouped them into 4 colored zones, based on the degree of compliance, and analyzed variations among regions and locations. Conclusions Compliance rates vary among the 34 provinces and among the districts and cities of those provinces. However, compliance to mask wearing seems slightly higher than social distancing. This finding suggests that policy makers need to promote higher compliance in other measures, including social distancing and hand washing, whose efficacies have been proven to break the chain of transmission when combined with masks wearing.

UNSTRUCTURED With an increasing number of cases and global spread, coronavirus disease 2019 (COVID-19) was declared a pandemic on the 11th March by the World Health Organisation (WHO). Mass gatherings (MGs) are particularly concerning as they’ve the potential to spread the disease both within and between countries, with transmission occurring; (i) during the event, (ii) whilst travelling to and from it and (iii) when attendees return to their home communities. In Malaysia, a ‘Tabligh’ religious gathering was held in late February. Approximately 16,000 people attended, including 1,500 foreigners from over 20 countries, at Seri Petaling Mosque near to Kuala Lumpur. This gathering was linked to a surge in cases in Malaysia and across Singapore, Indonesia, India, Brunei, Cambodia, and Thailand. It remains Malaysia’s largest COVID-19 cluster and as of 13th April was responsible for 38% of cases and 18 deaths in the country. It is important to risk assess MGs consistently with the COVID-19 risk assessment tool. To ensure the acceptability and success of MG cancellation decisions they should be: (1) Implemented early to reduce event travel; (2) Clearly communicated so that attendees understand why such decisions were taken including reinforcing good hygiene practices & (3) Make use of alternative arrangements such as postponement or technologies to hold the event remotely if safe to do so.

UNSTRUCTURED The current outbreak of coronavirus disease 2019 (COVID-19) caused by the novel coronavirus named SARS-CoV-2 has been a major global public health problem threatening many countries and territories. Mathematical modelling is one of the non-pharmaceutical public health measures that plays a crucial role for mitigating the risk and impact of the pandemic. A group of researchers and epidemiologists have developed a machine learning-powered inherent risk of contagion (IRC) analytical framework to georeference the COVID-19 with an operational platform to plan response & execute mitigation activities. This framework dataset provides a coherent picture to track and predict the COVID-19 epidemic post lockdown by piecing together preliminary data on publicly available health statistic metrics alongside the area of reported cases, drivers, vulnerable population, and number of premises that are suspected to become a transmission area between drivers and vulnerable population. The main aim of this new analytical framework is to measure the IRC and provide georeferenced data to protect the health system, aid contact tracing, and prioritise the vulnerable.