COVID-19 Contact Tracing Research Articles

Development of Proximity-Based COVID-19 Contact Tracing System Devices for Locally Virus Spread Prevention

COVID-19 contact tracing is a preventive solution to slow the spread of the virus. Several countries have implemented manual contact tracing as well as digital tracking using smartphone applications. A proximity-based COVID-19 contact tracing system device using BLE (Bluetooth Low Energy) technology focuses on tracking and controlling the spread of the virus in local communities. The devices consist of a signal sending device (tag) and a signal receiving device (scanner). Suppose a system device is implemented in a factory. The tag will be used by employees by placing it in the front pocket of the factory employee's clothes or hooked on the shirt. The tag will continuously send a signal that will be read by the scanner. This received signal with the received signal strength indicator (RSSI) format will be used to calculate the distance between the scanner and the tag. Then the distance will be used to determine the coordinate point of the tag, with calculations using the trilateration algorithm. Therefore, the distance between tags can be obtained, while with signal fluctuation, the actual coordinate point cannot be obtained, yet proximity information can still be obtained by filtering distance data at a specified time interval that is less than the threshold value of the distance, 2 meters, then comparing the data with the overall data, resulting in a percentage value. A high percentage, above 80%, indicates the closeness between tags.

Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing.

BackgroundContact tracing has been globally adopted in the fight to control the infection rate of COVID-19. To this aim, several mobile apps have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community’s response to the applications by analyzing information from different sources, such as news and users’ reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users’ reviews and extracts the evoked sentiments. We believe such solutions combined with a user-friendly interface can be used as a rapid surveillance tool to monitor how effective an application is and to make immediate changes without going through an intense participatory design method.ObjectiveIn this paper, we aim to analyze the efficacy of AI and NLP techniques for automatically extracting and classifying the polarity of users’ sentiments by proposing a sentiment analysis framework to automatically analyze users’ reviews on COVID-19 contact tracing mobile apps. We also aim to provide a large-scale annotated benchmark data set to facilitate future research in the domain. As a proof of concept, we also developed a web application based on the proposed solutions, which is expected to help the community quickly analyze the potential of an application in the domain.MethodsWe propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding with the development and training of artificial intelligence (AI) models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large-scale data set of user reviews on COVID-19 contact tracing applications. We used both classical and deep learning methods for classification experiments.ResultsWe used 8 different methods on 3 different tasks, achieving up to an average F1 score of 94.8%, indicating the feasibility of the proposed solution. The crowd-sourcing activity resulted in a large-scale benchmark data set composed of 34,534 manually annotated reviews.ConclusionsThe existing literature mostly relies on the manual or exploratory analysis of users’ reviews on applications, which is tedious and time-consuming. In existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and natural language processing techniques provide good results for analyzing and classifying users’ sentiments’ polarity and that automatic sentiment analysis can help to analyze users’ responses more accurately and quickly. We also provided a large-scale benchmark data set. We believe the presented analysis, data set, and proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile apps deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.

Sociodemographic characteristics and health access associated with COVID-19 infection and death: a cross-sectional study in Malang District, Indonesia

ObjectivesThis study aims to examine sociodemographic characteristics and health access associated with COVID-19 infection and death in Malang District, Indonesia.DesignA non-random cross-sectional study.SettingPopulation in 390 villages in Malang District, East...

Interval-valued fuzzy sets aggregation and evaluation approaches

We review interval-valued fuzzy sets (IVFS) and develop their application for several areas. We show how to aggregate IVFSs and provide information measures to guide the aggregation. An example of the use of IVFS to represent the uncertainties in Covid-19 contact tracing is illustrated. Then we consider the application of IVFS in spatial data. By determining distance and area of IVFS data we can use aggregation to apply this to minimum bounding rectangles for GIS. Finally, IVFS can be applied in computing diversity in the process of selection of groups exhibiting diversity.

COVID-19 and the fear of other unknowns: challenges and lessons learned from a digital contact tracing activity in the Rohingya camps in Cox’s Bazar, Bangladesh

Contact tracing can play an important role in controlling infectious disease outbreaks such as the COVID-19 pandemic. Containing the spread of COVID-19 is crucial in humanitarian settings such as in the Rohingya camps in Cox’s Bazar, Bangladesh. This manuscript describes the COVID-19 contact tracing activities undertaken by a group of researchers and implementers in Cox’s Bazar, Bangladesh. The paper details the design and development of the Commcare ‘Contact tracing and case monitoring app’, subsequent implementation of the contact tracing activity, challenges faced during the implementation process, and the strategies adopted by the research team to overcome these challenges. The research team leveraged the suite of template applications for COVID-19 response developed by Dimagi in response to the COVID-19 pandemic. Research partners organized a series of brainstorming meetings and workshops with relevant stakeholders to finalize the ‘COVID- 19 contact tracing and case monitoring app’ for final implementation. This app was implemented in 10 Rohingya camps from Ukhiya and Teknaf sub-districts of Cox’s Bazar for 4.5 months from 1st January 2021 to 15th May 2021. Due to a restriction on internet availability in the Rohingya camps by the government of the host country, the research team had to adopt a manual approach to implement the contact tracing activity. During these 4.5 months, 249,452 individuals from 10 Rohingya camps were screened for COVID-19 case registration. Of all the screened individuals, 431 were identified as COVID suspected cases, and 77 were identified as confirmed cases. The research team experienced several implementation challenges such as inexperience of contact tracers with the nature of the work, convincing the community to register in a digital system, obtaining information around COVID-19 symptoms, and many cultural, linguistic, gender, and other social barriers. The team adopted challenge-specific mitigation strategies for the effective implementation of the activity. The modalities of operation adopted by the team engaged with this present intervention to overcome the difficulties experienced in its conduction can hopefully provide some guidance to future parties attempting to conduct similar activities in complex humanitarian settings.

Resistance to contact tracing applications: The implementation process in a social context

The success of new technologies such as contact tracing mobile applications depends on large-scale end-user adoption. However, the implementation may encounter resistance, since the uncertainty surrounding novel technology may raise anxiety, and persuasion efforts to promote use can evoke reactance. Thereby, anxiety and reactance are two forms of resistance to new technology. Little is known about the role of resistance over the course of the innovation implementation process, in a social environment where technology functionality depends on adoption by others. Therefore, this four-wave longitudinal study followed adoption of the Dutch COVID-19 contact tracing app during four months (N = 1120), and explored the time dynamics and interplay of reactance to freedom threat, anxiety, and perceived social norms on app use. Mixed-effect analyses showed that anxiety and, subtly, reactance decreased with time; initial freedom threat predicted later reactance. App use related negatively to reactance and anxiety; and positively to positive social norms. Over time, the norm effect was mediated by lower reactance and anxiety. The results imply that resistance is pervasive, suggest that self-perceived app use norms may be key to overcoming resistance to new applications, and demonstrate that theories predicting innovation or technology acceptance benefit from studying predictors over time.

Testing Communication Concepts on COVID-19 Contact Tracing Among Black and Latinx/Hispanic People in the United States

ObjectiveBlack and Latinx/Hispanic people were more than twice as likely to die from COVID-19 than White people, but because of legacies of discrimination and maltreatment in health care, were less likely to participate in some public health responses to COVID-19, including contact tracing. This study aimed to test three communication campaign concepts to engage Black and Latinx/Hispanic people in contact tracing efforts.MethodsTwelve focus group discussions with 5 to 10 participants each were conducted online among participants from Black and Latinx/Hispanic urban populations in Philadelphia and New York state. Participants provided sociodemographic information and were presented with potential campaign concepts and prompted to rate the concepts and engage in open-ended discussion. For rating and sociodemographic data, chi-square tests were performed. For open-ended discussion data, a thematic analysis approach was used.ResultsAcross groups, the campaign concept that was rated most likely to encourage cooperation with contact tracing efforts was “Be the One,” with 45% of total first-place votes. Participants expressed that the campaign caught their attention (79%), motivated them to engage with contact tracers (71%) and to talk to others about contact tracing (77%). Discussions also elucidated: the importance of community engagement; the need for clearer explanations of contact tracing; the preference for already trusted, community-based contact tracers; the need to reassure people about confidentiality; and for contact tracing to be culturally competent and empathetic.ConclusionsThis study highlights how strategic, culturally sensitive communication can buttress current and future contact tracing efforts, especially among Black and Latinx/Hispanic people.

Privacy-preserving COVID-19 contact tracing solution based on blockchain

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users.By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost.

In times of crisis: Public perceptions toward COVID-19 contact tracing apps in China, Germany, and the United States

The adoption of COVID-19 contact tracing apps (CTAs) has been proposed as an important measure to contain the spread of COVID-19. Based on a cross-national dataset, this article analyzes public perceptions toward CTAs and the factors that drive CTA acceptance in China, Germany, and the United States. We find that public acceptance of CTAs is significantly higher in China as compared with Germany and the United States. Despite very different sociopolitical contexts, there are striking similarities in the factors that drive CTA acceptance in all three countries. Citizens are willing to accept digital contact tracing despite concerns about privacy infringement and government surveillance, as long as the apps are perceived as effective in lowering infection rates and providing health information. This creates a chicken-and-egg problem for CTAs in Germany and the United States where CTAs are voluntary: a high citizen adoption rate is necessary for CTAs to be effective, but CTAs are only effective if adoption rates are high.

172 Factors affecting rural residents willingness to share personal data for COVID-19 contact tracing

OBJECTIVES/GOALS: The goal of this study was to examine the attitudes of central Pennsylvania rural residents toward data sharing in the setting of the COVID-19 pandemic. We determined their willingness to provide cell phone data and personal information to public health staff investigating COVID-19 cases, as well as their concerns about sharing this information. METHODS/STUDY POPULATION: We used the electronic medical records of an academic healthcare institution in central Pennsylvania to obtain names and addresses of patients who had visited an outpatient clinic or been an inpatient within the prior three years, were 18 years or older, and who resided in a community defined as rural by the Commonwealth of Pennsylvania. The survey included four statements about contact tracing, three statements about intent to receive the COVID-19 vaccine, and validated scales for general trust and for distrust in healthcare organizations. All study variables were summarized to determine their distributions, and bivariate binomial logistic regression was conducted. A multivariable model including all of the independent variables was then fit for each outcome variable. RESULTS/ANTICIPATED RESULTS: The response rate was 19.5%. 95 % of respondents were white, 56% were female, and nearly two-thirds were older than 60 years. Binomial logistic regression showed that both distrust in healthcare organizations and political values influenced respondents willingness to share information with contact tracers as well as their concerns about sharing. In multivariate analysis, political values were a consistent predicator of willingness to share and concerns about sharing. Respondents who indicated that they wanted to get vaccinated as soon as possible were significantly more likely than those who did not want to be vaccinated to be willing to share their cell phone location data and personal data. Conversely, they were less likely to be concerned with these data being shared without their permission. DISCUSSION/SIGNIFICANCE: Understanding rural residents concerns about sharing personal information is critical if we are to develop successful strategies for lessening the impact of COVID-19 and managing future pandemics. This study is a first step in eliciting such concerns in the context of COVID-19and has implications for directing a successful pandemic response.

COVID-19 case findings and contact tracing in South German nursing homes

BackgroundThe air borne SARS-CoV-2 poses a high threat to the elderly and people with underlying diseases. COVID-19 spread quickly in South German nursing homes and for this reason called for preventive measures by the German government. The aim of this paper is to showcase the testing strategies implemented by the Public Health Department Reutlingen to control the spread of COVID-19 in local nursing homes and to report the results thereof.MethodsThis study reports COVID-19 outbreaks in nursing homes in Reutlingen County and how they were dealt with through extensive testing, contact tracing, isolation and hygiene inspections. The testing strategy consisted of three phases: In phase 1 only suspected cases, in phase 2 all staff and residents, and in phase 3 all suspected cases and their contacts were tested.ResultsNearly all residents (98%) and staff members (92%) of all nursing homes in Reutlingen County were tested for SARS-COV-2. 25 of 37 nursing homes had COVID-19 cases, 5 had 30–81 cases/home. 62% of the 395 nursing homes cases were residents, but less than half of them exhibited symptoms (41%). The cases uncovered in nursing homes represented 26% of all 1529 cases in Reutlingen County during the time of this study.ConclusionsMany COVID-19 cases were discovered through extensive testing, allowing for early interventions. The results shed light on the COVID-19 situation in nursing homes and allowed for individually designed preventive measures. The results also lead to a change in the German legislation. The outbreak management methods of the Public Health Department Reutlingen may also be applicable in other countries.

Estimated COVID-19 Cases and Hospitalizations Averted by Case Investigation and Contact Tracing in the US

Evidence of the impact of COVID-19 case investigation and contact tracing (CICT) programs is lacking, but policy makers need this evidence to assess the value of such programs. To estimate COVID-19 cases and hospitalizations averted nationwide by US states' CICT programs. This decision analytical model study used combined data from US CICT programs (eg, proportion of cases interviewed, contacts notified or monitored, and days to case and contact notification) with incidence data to model outcomes of CICT over a 60-day period (November 25, 2020, to January 23, 2021). The study estimated a range of outcomes by varying assumed compliance with isolation and quarantine recommendations. Fifty-nine state and territorial health departments that received federal funding supporting COVID-19 pandemic response activities were eligible for inclusion. Data analysis was performed from July to September 2021. Public health case investigation and contact tracing. The primary outcomes were numbers of cases and hospitalizations averted and the percentage of cases averted among cases not prevented by vaccination and other nonpharmaceutical interventions. In total, 22 states and 1 territory reported all measures necessary for the analysis. These 23 jurisdictions covered 42.5% of the US population (approximately 140 million persons), spanned all 4 US Census regions, and reported data that reflected all 59 federally funded CICT programs. This study estimated that 1.11 million cases and 27 231 hospitalizations were averted by CICT programs under a scenario where 80% of interviewed cases and monitored contacts and 30% of notified contacts fully complied with isolation and quarantine guidance, eliminating their contributions to future transmission. As many as 1.36 million cases and 33 527 hospitalizations could have been prevented if all interviewed cases and monitored contacts had entered into and fully complied with isolation and quarantine guidelines upon being interviewed or notified. Across both scenarios and all jurisdictions, CICT averted an estimated median of 21.2% (range, 1.3%-65.8%) of the cases not prevented by vaccination and other nonpharmaceutical interventions. These findings suggest that CICT programs likely had a substantial role in curtailing the pandemic in most jurisdictions during the 2020 to 2021 winter peak. Differences in outcomes across jurisdictions indicate an opportunity to further improve CICT effectiveness. These estimates demonstrate the potential benefits from sustaining and improving these programs.

Protecting communities during the COVID-19 global health crisis: health data research and the international use of contact tracing technologies

Seen presently during the global COVID-19 global health crisis are the ways government agencies are enabled by digital data collection through the development of contact tracing technologies (CTT) and mobile-based tracking in their effort to limit the spread of the COVID-19 coronavirus. While recent research has focused on contact tracing and privacy (Simko et al. 2020. Contact tracing and privacy: studying opinion and preferences), contact tracing and data protection (Abeler et al. 2020. JMIR mHealth uHealth 8(4): e19359) contact tracing system and information security considerations (Gvili, 2020. Security analysis of the Covid-19 contact tracing specifications by Apple inc. and Google inc), CTT and the data collected and curated have not been framed to date via their intersections with health-datafication and the research participant. As this article outlines, each is strongly linked to public health, healthcare industries and to modalities of capturing and producing knowledge that is expected to help in addressing public health concerns. Where different countries and regions are implementing a range of social distancing and/or social isolation recommendations, some have introduced contact tracing and quick response (QR) barcodes on mobile device apps. To additionally explore access, uptake and use that accompany such technologies, the International Responses to COVID-19 Contact Tracing: COVID-19 APP Uptake and Use Survey was developed and used with participants living in Singapore, Australia, the United States and the United Kingdom. The article concludes that in the process and counter to the common good or public interest objective that all are kept safe, new forms of risk and exposure are being produced.

An analysis of the COVID-19 contact tracing App in South Africa: Challenges experienced by users

South Africa is one of the countries in Africa that has a high COVID-19 virus infection rate. The government of South Africa introduced the COVID Alert SA App, a contact tracing mobile application (App), to reduce the spread of the COVID-19 virus. This paper reports on the challenges users experienced when using the COVID Alert SA App. Using the design-reality gap model, the study analyzed online user reviews of the App and government reports. The qualitative data were analyzed using thematic analysis to highlight insights on the challenges of using the COVID Alert SA App. The findings indicate that public awareness was a major limitation of the App, amongst others, including a lack of trust relating to privacy and security when using the App, and a lack of technical support. The insights can be useful for developers, researchers, policymakers, and other stakeholders to improve the adoption and use of the contact tracing App to reduce the spread of the COVID-19 virus.

Leveraging Polio Geographic Information System Platforms in the African Region for Mitigating COVID-19 Contact Tracing and Surveillance Challenges: Viewpoint.

BackgroundThe ongoing COVID-19 pandemic in Africa is an urgent public health crisis. Estimated models projected over 150,000 deaths and 4,600,000 hospitalizations in the first year of the disease in the absence of adequate interventions. Therefore, electronic contact tracing and surveillance have critical roles in decreasing COVID-19 transmission; yet, if not conducted properly, these methods can rapidly become a bottleneck for synchronized data collection, case detection, and case management. While the continent is currently reporting relatively low COVID-19 cases, digitized contact tracing mechanisms and surveillance reporting are necessary for standardizing real-time reporting of new chains of infection in order to quickly reverse growing trends and halt the pandemic.ObjectiveThis paper aims to describe a COVID-19 contact tracing smartphone app that includes health facility surveillance with a real-time visualization platform. The app was developed by the AFRO (African Regional Office) GIS (geographic information system) Center, in collaboration with the World Health Organization (WHO) emergency preparedness and response team. The app was developed through the expertise and experience gained from numerous digital apps that had been developed for polio surveillance and immunization via the WHO’s polio program in the African region.MethodsWe repurposed the GIS infrastructures of the polio program and the database structure that relies on mobile data collection that is built on the Open Data Kit. We harnessed the technology for visualization of real-time COVID-19 data using dynamic dashboards built on Power BI, ArcGIS Online, and Tableau. The contact tracing app was developed with the pragmatic considerations of COVID-19 peculiarities. The app underwent testing by field surveillance colleagues to meet the requirements of linking contacts to cases and monitoring chains of transmission. The health facility surveillance app was developed from the knowledge and assessment of models of surveillance at the health facility level for other diseases of public health importance. The Integrated Supportive Supervision app was added as an appendage to the pre-existing paper-based surveillance form. These two mobile apps collected information on cases and contact tracing, alongside alert information on COVID-19 reports at the health facility level; the information was linked to visualization platforms in order to enable actionable insights.ResultsThe contact tracing app and platform were piloted between April and June 2020; they were then put to use in Zimbabwe, Benin, Cameroon, Uganda, Nigeria, and South Sudan, and their use has generated some palpable successes with respect to COVID-19 surveillance. However, the COVID-19 health facility–based surveillance app has been used more extensively, as it has been used in 27 countries in the region.ConclusionsIn light of the above information, this paper was written to give an overview of the app and visualization platform development, app and platform deployment, ease of replicability, and preliminary outcome evaluation of their use in the field. From a regional perspective, integration of contact tracing and surveillance data into one platform provides the AFRO with a more accurate method of monitoring countries’ efforts in their response to COVID-19, while guiding public health decisions and the assessment of risk of COVID-19.

Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing

Summary An academic team at Stanford University worked with the County of Santa Clara Public Health Department to develop a machine-learning system for language matching in Covid-19 contact tracing through a collaborative design process. Although developed for a specific public health activity, the setting is representative of a wide range of health care delivery contexts, and the experience lays out how health care organizations can develop accountable algorithms that improve care, mitigate risk, and enable evaluation by stakeholders. Key elements of the design process involved: (1) a partnership and stakeholder consultation to develop a common understanding of and iteration around algorithmic design; (2) the use of a model understandable to all stakeholders, which exhibited only modest performance degradation relative to more complex models; and (3) a randomized controlled trial and qualitative survey of how the algorithm impacted real-world contact tracing, providing an evaluation that goes beyond narrow technical measures of algorithmic performance.

“Unless One Does the Research, It May Seem as Just a Useless Battery-consuming App” – Field Notes on COVID-19 Contact Tracing Applications

Globally, countries have been developing contact tracing applications to control the spread of the coronavirus (COVID-19) disease. In this work, we present the findings of eight focus groups we conducted with participants living in France and Germany, to explore why they decided to adopt, or not adopt, a contact tracing application as well as understand how they perceived the benefits, drawbacks, and threat model of a contact tracing application.

African Americans views of COVID-19 contact tracing and testing

Increasing COVID-testing and contact tracing is necessary to control the COVID-19 pandemic considering suboptimal vaccine rates. We conducted semi-structured interviews to explore views towards contact tracing and testing among 62 African Americans. Based on our findings, participants identified COVID-19 testing and contact tracing as beneficial, yet medical and governmental mistrust, stigma associated with SARS-CoV-2, lack of access, poor communication, and costs as major barriers. This study also highlights intervention targets to improve COVID-testing and contact tracing.

COVID-19 CONTACT TRACING AND TRACKING

The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems. COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic. Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions. In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals. However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability. The use of Covid-19 contact tracing applications is close to be unheard of in Nigeria. We are far behind in leveraging on mobile app technology to combat the continued spread of this deadly virus. This project proposes a mobile app solution that provides adequate information to the contacted victims and as well warns the non-contacted of the dangers of leaving home without their kits. KEYWORDS: Covid-19, covid-19 contact tracing, contact tracing apps, coronavirus, mobile app, covid-19 alert.

Detection and Contact Tracing of COVID-19 in Indonesia through a Red Cross Community Based Surveillance System

PurposeEpidemics and pandemics begin and end in communities. Communities play a critical role in prevention, detection, and response to infectious disease threats when meaningfully engaged. Surveillance and contact tracing are core components of effective public health preparedness and response efforts. But, surveillance systems that rely on case detection via healthcare facilities may fail or be slow to detect and trace many cases. Communities are often the first to identify unusual disease patterns in humans and animals. Community-Based Surveillance (CBS) gives a voice to communities, enables them to communicate unusual health events to authorities, and complements facility-based surveillance systems.Methods & MaterialsSince 2019, Indonesia Red Cross (PMI) has piloted CBS in 8 villages within four provinces. Initiated for passive surveillance, the volunteers trained to report ‘alerts’ of epidemic-prone diseases to health authorities. The concept was then adapted to an active approach focussing on COVID-19 and scaled to 74 villages across eight provinces. Partnering with local authorities, volunteers supported COVID-19 screening, detection, tracing, and local response, all while implementing volunteer safety standards. The reporting system was created and coordinated by a structure within PMI.ResultsFrom August 2020-June 2021, community volunteers made 1,079 alerts: 99% of the alerts were reported to health authorities, with 71.1% followed up within 24 hours. After alert detection, volunteers sensitized family members on preventive measures while individuals awaited follow-up visits for diagnosis and management by health authorities. 64% of alerts were PCR positive for COVID-19, 12% were negative, 14% did not undergo testing, and test results for 10% of the alerts were unavailable. Of 979 contacts traced by volunteers, authorities followed up 99%, confirming 29% positive and 34% negative for COVID-19; the remaining 37% included those recommended to isolate without testing and those for whom test results were unavailable.ConclusionCBS has supported early detection and contact tracing of COVID-19 in Indonesia. The community case definition used to identify potential alerts was sensitive and easily understood by non-health workers. The system proved readily scalable and adaptable, enabling a shift from passive to active surveillance and making it feasible to be implemented anywhere in the community.