A New System for Surveillance and Digital Contact Tracing for COVID-19: Spatiotemporal Reporting Over Network and GPS

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Community-Based Digital Contact Tracing of Emerging Infectious Diseases: Design and Implementation Study With Empirical COVID-19 Cases

BACKGROUND

Contact tracing for containing emerging infectious diseases such as COVID-19 is resource intensive and requires digital transformation to enable timely decision-making.

OBJECTIVE

This study demonstrates the design and implementation of digital contact tracing using multimodal health informatics to efficiently collect personal information and contain community outbreaks. The implementation of digital contact tracing was further illustrated by 3 empirical SARS-CoV-2 infection clusters.

METHODS

The implementation in Changhua, Taiwan, served as a demonstration of the multisectoral informatics and connectivity between electronic health systems needed for digital contact tracing. The framework incorporates traditional travel, occupation, contact, and cluster approaches and a dynamic contact process enabled by digital technology. A centralized registry system, accessible only to authorized health personnel, ensures privacy and data security. The efficiency of the digital contact tracing system was evaluated through a field study in Changhua.

RESULTS

The digital contact tracing system integrates the immigration registry, communicable disease report system, and national health records to provide real-time information about travel, occupation, contact, and clusters for potential contacts and to facilitate a timely assessment of the risk of COVID-19 transmission. The digitalized system allows for informed decision-making regarding quarantine, isolation, and treatment, with a focus on personal privacy. In the first cluster infection, the system monitored 665 contacts and isolated 4 (0.6%) cases; none of the contacts (0/665, 0%) were infected during quarantine. The estimated reproduction number of 0.92 suggests an effective containment strategy for preventing community-acquired outbreak. The system was also used in a cluster investigation involving foreign workers, where none of the 462 contacts (0/462, 0%) tested positive for SARS-CoV-2.

CONCLUSIONS

By integrating the multisectoral database, the contact tracing process can be digitalized to provide the information required for risk assessment and decision-making in a timely manner to contain a community-acquired outbreak when facing the outbreak of emerging infectious disease.

A Digital Mask-Voiceprint System for Postpandemic Surveillance and Tracing Based on the STRONG Strategy

Lockdowns and border closures due to COVID-19 imposed mental, social, and financial hardships in many societies. Living with the virus and resuming normal life are increasingly being advocated due to decreasing virus severity and widespread vaccine coverage. However, current trends indicate a continued absence of effective contingency plans to stop the next more virulent variant of the pandemic. The COVID-19-related mask waste crisis has also caused serious environmental problems and virus spreads. It is timely and important to consider how to precisely implement surveillance for the dynamic clearance of COVID-19 and how to efficiently manage discarded masks to minimize disease transmission and environmental hazards. In this viewpoint, we sought to address this issue by proposing an appropriate strategy for intelligent surveillance of infected cases and centralized management of mask waste. Such an intelligent strategy against COVID-19, consisting of wearable mask sample collectors (masklect) and voiceprints and based on the STRONG (Spatiotemporal Reporting Over Network and GPS) strategy, could enable the resumption of social activities and economic recovery and ensure a safe public health environment sustainably.

Forecasting virus outbreaks with social media data via neural ordinary differential equations

During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.

Blockchain and COVID-19 pandemic: applications and challenges

The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today's healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.

A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.

Randomized Controlled Trial Evaluating the Benefit of a Novel Clinical Decision Support System for the Management of COVID-19 Patients in Home Quarantine: A Study Protocol

(1) Background: We present the protocol of a randomized controlled trial designed to evaluate the benefit of a novel clinical decision support system for the management of patients with COVID-19. (2) Methods: The study will recruit up to 500 participants (250 cases and 250 controls). Both groups will receive the conventional telephone follow-up protocol by primary care and will also be provided with access to a mobile application, in which they will be able to report their symptoms three times a day. In addition, patients in the active group will receive a wearable smartwatch and a pulse oximeter at home for real-time monitoring. The measured data will be visualized by primary care and emergency health service professionals, allowing them to detect in real time the progression and complications of the disease in order to promote early therapeutic interventions based on their clinical judgement. (3) Results: Ethical approval for this study was obtained from the Drug Research Ethics Committee of the Valladolid East Health Area (CASVE-NM-21-516). The results obtained from this study will form part of the thesis of two PhD students and will be disseminated through publication in a peer-reviewed journal. (4) Conclusions: The implementation of this telemonitoring system can be extrapolated to patients with other similar diseases, such as chronic diseases, with a high prevalence and need for close monitoring.

Comparing Efficiency and Performance of IoT BLE and RFID-Based Systems for Achieving Contract Tracing to Monitor Infection Spread among Hospital and Office Staff

COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, such as hospitals and offices, in order to measure personnel proximity while placing as little load on them as possible. Contact tracing is a vital step in controlling and restricting pandemic spread; however, traditional contact tracing is time-consuming, exhausting, and ineffective. As a result, more research and application of smart digital contact tracing is necessary. As the Internet of Things (IoT) and wearable sensor device studies have grown in popularity, this work has been based on the practicality and successful implementation of Bluetooth low energy (BLE) and radio frequency identification (RFID) IoT based wireless systems for achieving contact tracing. Our study presents autonomous, low-cost, long-battery-life wireless sensing systems for contact tracing applications in hospital/office environments; these systems are developed with off-the-shelf components and do not rely on end user participation in order to prevent any inconvenience. Performance evaluation of the two implemented systems is carried out under various real practical settings and scenarios; these two implemented centralised IoT contact tracing devices were tested and compared demonstrating their efficiency results.

Emergency preparedness during the COVID-19 pandemic: Modelling the roles of social media with fuzzy DEMATEL and analytic network process

While the utility of social media has been widely recognized in the current literature, minimal effort has been made to further the analysis of their roles on disruptive events, such as the COVID-19 pandemic. To address this gap, this work comprehensively identifies the 16 prevalent social media roles in disaster preparedness during the COVID-19 pandemic. Furthermore, an integrated fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and analytic network process (ANP), hereby termed the FDANP methodology, is used to perform the causal analysis of social media roles and to systemically measure the priority of these roles in emergency preparedness. Among the identified roles, those considered top priority are social media roles concerned with the facilitation of public health policy development, prevention of misinformation, and management of public behavior and response. These results were found to be robust, as evidenced by the sensitivity analysis. The implications of these findings were also detailed in this work in the context of a developing country.

Public Attitudes Regarding Trade-offs Between the Functional Aspects of a Contact-Confirming App for COVID-19 Infection Control and the Benefits to Individuals and Public Health: Cross-sectional Survey

BACKGROUND

It is expected that personal health information collected through mobile information terminals will be used to develop health strategies that benefit the public. Against this background, several countries have actively attempted to use mobile phones to control infectious diseases. These collected data, such as activity logs and contact history, are countermeasures against diseases such as COVID-19. In Japan, the Ministry of Health, Labor, and Welfare has developed and disseminated a contact-confirming app (COVID-19 Contact-Confirming Application [COCOA]) to the public, which detects and notifies individuals whether they have been near someone who had subsequently tested positive for COVID-19. However, there are concerns about leakage and misuse of the personal information collected by such information terminals.

OBJECTIVE

This study aimed to investigate the possible trade-off between effectiveness in preventing infectious diseases and infringement of personal privacy in COCOA. In addition, we analyzed whether resistance to COCOA would reduce if the app contributed to public health or if a discount was provided on mobile phone charges.

METHODS

A cross-sectional, quantitative survey of Japanese citizens was conducted using Survey Monkey, a general-purpose web-based survey platform. When developing the questions for the questionnaire, we included the installation status of COCOA and recorded the anxiety stemming from the potential leakage or misuse of personal information collected for COVID-19 infection control. The respondents were asked to rate various factors to determine their perceptions on a 5-point scale.

RESULTS

In total, 1058 participants were included in the final analysis. In response to the question of whether the spread of the disease was being controlled by the infection control measures taken by the government, 25.71% (272/1058) of the respondents answered that they strongly agreed or agreed. One-quarter of the respondents indicated that they had already installed COCOA. This study found that the sense of resistance to government intervention was not alleviated by the benefits provided to individuals when using the app. The only factors that were positively associated with the response absolutely opposed to use of the app, even with a discount on mobile phone use charges, were those regarding leaks and misuse of personal information, which was true for all functions (function A: odds ratio [OR] 1.8, 95% CI 1.3-2.4; function B: OR 1.9, 95% CI 1.5-2.6; function C: OR 1.8, 95% CI 1.4-2.4).

CONCLUSIONS

Public organizations need to emphasize the general benefits of allowing them to manage personal information and assure users that this information is being managed safely rather than offering incentives to individuals to provide such personal information. When collecting and using citizens' health information, it is essential that governments and other entities focus on contributing to the public good and ensuring safety rather than returning benefits to individual citizens.

Accounting Transparency, Fear Sentiment and the COVID-19 Epidemic: For Public Health Security and the Construction of an Early Warning System

A central issue of public health security and the construction of an early warning system is to establish a set of responsibility-oriented incentives and restraint mechanisms. This is closely related to the accounting transparency of the institutional environment and the fear sentiment of the individual's predicament. This study analyses the relationship between accounting transparency, fear sentiment, and COVID-19 through a VAR model analysis. The results show a significant and negative relationship between accounting transparency and daily new COVID-19 patients. In particular, accounting transparency has a negative impact on the increase in the number of people infected with a two-period lag, while the three-period lag in the number of new epidemics has a negative impact on accounting information. Second, accounting transparency has a positive impact on the increase in the search volume on COVID-19 within a three-period lag. After the three-period lag, the number of new epidemics has a positive impact on accounting information. Third, an increase in fear sentiment can be driven by the fear of COVID-19. Fourth, in the public health early warning system, according to the abovementioned time characteristics, the system arranges the emotional counseling, early warning incentives, and institutional constraints to be dealt with in the first 4 days. In addition, in the early warning target-oriented system setting, the parallel system helps to improve the early warning efficiency.

Assessing personal exposure to COVID-19 transmission in public indoor spaces based on fine-grained trajectory data: A simulation study

The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to worldwide health systems in quick response to epidemics. The assessment of personal exposure to COVID-19 in enclosed spaces is critical to identifying potential infectees and preventing outbreaks. However, traditional contact tracing methods rely heavily on a manual interview, which is costly and time consuming given the large population involved. With advanced indoor localisation techniques, it is possible to collect people's footprints accurately by locating their smartphones. This study presents a new framework for the assessment of personal exposure to COVID-19 carriers using their fine-grained trajectory data. An integral model was established to quantify the exposure risk, in which the spatial and temporal decay effects are simultaneously considered when modelling the airborne transmission of COVID-19. Regarding the obstacle effect of the indoor layout on airborne transmission, a weight graph based on the space syntax technique was further introduced to constrain the transmission strength between subspaces that are less inter-visible. The proposed framework was demonstrated by a simulation study, in which external comparison and internal analysis were conducted to justify its validity and robustness in different scenarios. Our method is expected to promote the efficient identification of potential infectees and provide an extensible spatial-temporal model to simulate different control measures and examine their effectiveness in a built environment.

Ten GIS-Based Solutions for Managing and Controlling COVID-19 Pandemic Outbreak

The coronavirus (COVID-19) pandemic has caused disastrous results in most countries of the world. It has rapidly spread across the globe with over 156 million cumulative confirmed cases and 3.264 million deaths to date, according to World Health Organization (WHO) Coronavirus Disease (COVID-19) Dashboard. With these huge amounts of causalities in the world, Geographic Information Systems (GIS) as a computer-based analyzer could help governments, experts, medical staff, and citizens to prevent and respond to the incidence. On the other hand, the COVID-19 pandemic involves many unknown parameters where most of them have a spatial dimension. Thus, spatial analysis and GIS could provide appropriate decision-making tools, predictive models, statistical methods, and new technologies for COVID-19 outbreak control, also help the people for avoiding direct contact and preserving social distance. This article aims to review the most promising categories of GIS-based solutions in this domain. We divided the solutions into ten classes including spatio-temporal analysis, SDSS approaches, geo-business, context-aware recommendation systems, participatory GIS and volunteered geographic information (VGI), internet of things (IoT), location-based service (LBS), web mapping, satellite imagery-based analysis, and waste management. The main contribution of this paper is proposing different geospatial guidelines that could provide reliable and useful protocols for COVID-19 outbreak control to minimize causalities, restrict incidence, establish effective urban communication, provide new approaches for business in lockdown situations, telehealth treatment, patient monitoring, adaptive decision making, and visualize trend analysis.

Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies & digital technologies

As the COVID-19 pandemic expanded over the globe, governments implemented a series of technological measures to prevent the disease's spread. The development of the COVID Tracing Application (CTA) was one of these measures. In this study, we employed bibliometric and topic-based content analysis to determine the most significant entities and research topics. Additionally, we identified significant privacy concerns posed by CTAs, which gather, store, and analyze data in partnership with large technology corporations using proximity measurement technologies, artificial intelligence, and blockchain. We examined a series of key privacy threats identified in our study. These privacy risks include anti-democratic and discriminatory behaviors, politicization of care, derogation of human rights, techno governance, citizen distrust and refusal to adopt, citizen surveillance, and mandatory legislation of the apps' installation. Finally, sixteen research gaps were identified. Then, based on the identified theoretical gaps, we recommended fourteen prospective study strands. Theoretically, this study contributes to the growing body of knowledge about the privacy of mobile health applications that are embedded with cutting-edge technologies and are employed during global pandemics.

CN, F.V. J, P. V, K. J. Mobile technologies for contact tracing and prevention of COVID-19 positive cases: a cross- sectional study

Purpose

The purpose of this paper is to review the techniques for versatile advancements in contact tracing for the coronavirus disease 2019 (COVID-19) positive cases in this pandemic and to introduce the way of using the mobile location information collected within the country India. As the method, an exploratory review of current measures was conducted for confirmed COVID-19 contact tracing after understanding the current situation of the world. This paper has examined the way of using free locational information in an innovative way to reduce the spread of COVID-19 spread.

Design/methodology/approach

COVID-19 pandemic is the utmost global economic and health challenge of the century. One powerful and consistent procedure to slow down the spread and decrease the effect of COVID-19 is to track the essential and auxiliary contacts of confirmed COVID-19 positive cases by using contact-tracing innovation.

Findings

Although it takes the information from various clients, there are numerous odds in the information. The sincere measures were taken by the authors to avoid the abuse of information by any kind. A portion of the tips for keeping information from getting abused is on the whole, the information ought to be with just higher specialists, and they ought not to have the authorization to impart information to anybody.

Originality/value

This paper helps to track the COVID-19 positive cases as of now by using the field information assortment and outbreak examination stages. At the same time, mobile location information used inside the current guideline, rules for information handlers must incorporate measures to reduce the abusing of information.

Mining user reviews of COVID contact-tracing apps: An exploratory analysis of nine European apps

CONTEXT

More than 78 countries have developed COVID contact-tracing apps to limit the spread of coronavirus. However, many experts and scientists cast doubt on the effectiveness of those apps. For each app, a large number of reviews have been entered by end-users in app stores.

OBJECTIVE

Our goal is to gain insights into the user reviews of those apps, and to find out the main problems that users have reported. Our focus is to assess the "software in society" aspects of the apps, based on user reviews.

METHOD

We selected nine European national apps for our analysis and used a commercial app-review analytics tool to extract and mine the user reviews. For all the apps combined, our dataset includes 39,425 user reviews.

RESULTS

Results show that users are generally dissatisfied with the nine apps under study, except the Scottish ("Protect Scotland") app. Some of the major issues that users have complained about are high battery drainage and doubts on whether apps are really working.

CONCLUSION

Our results show that more work is needed by the stakeholders behind the apps (e.g., app developers, decision-makers, public health experts) to improve the public adoption, software quality and public perception of these apps.

COVID-19 pandemic as a trigger for the acceleration of the cybernetic revolution, transition from e-government to e-state, and change in social relations

Among many influences that the pandemic has and will have on society and the World System as a whole, one of the most important is the acceleration of the start of a new technological wave and a new technological paradigm in the near future. This impact is determined by the growing need for the development of a number of areas in medicine, bio- and nanotechnology, artificial intelligence and others, which we denote as "MANBRIC convergence". It is shown that the experience of dealing with the COVID-19 pandemic has confirmed that the final phase of the Cybernetic Revolution will begin in the 2030s at the intersection of a number of medical, bio, digital and several other technologies, with medical needs as an integrating link. Among the multitude of self-regulating systems in the economy and life (which, in our opinion, will flourish during the Cybernetic Revolution) socio-technical self-regulating systems (SSSs) will play a special role. Thus, COVID-19 becomes a powerful impetus not only in terms of accelerating technological development and approaching the final phase of the Cybernetic Revolution, but also in changing sociopolitical (and socio-administrative) relations in the forthcoming decades.

Developing a Case Investigation and Contact-Tracing System in Puerto Rico, 2020

We present a record of events that led to the creation of the Puerto Rico Case Investigation and Contact-Tracing System (CICTS) to monitor and control the spread of severe acute respiratory syndrome coronavirus 2 in Puerto Rico. The development of the CICTS is a significant step toward establishing a comprehensive infectious disease surveillance system in Puerto Rico. Furthering the development of a CICTS infrastructure is critical in the response against future emerging infectious diseases in the region. (Am J Public Health. 2022;112(2):223-226. https://doi.org/10.2105/AJPH.2021.306584).

Use of a smartphone app to inform healthcare workers of hospital policy during a pandemic such as COVID-19: A mixed methods observational study

Objective

To evaluate the use of a COVID-19 app containing relevant information for healthcare workers (HCWs) in hospitals and to determine user experience.

Methods

A smartphone app (Firstline) was adapted to exclusively contain local COVID-19 policy documents and treatment protocols. This COVID-19 app was offered to all HCWs of a 900-bed tertiary care hospital. App use was evaluated with user analytics and user experience in an online questionnaire.

Results

A total number of 1168 HCWs subscribed to the COVID-19 app which was used 3903 times with an average of 1 minute and 20 seconds per session during a three-month period. The number of active users peaked in April 2020 with 1017 users. Users included medical specialists (22.3%), residents (16.5%), nurses (22.2%), management (6.2%) and other (26.5%). Information for HCWs such as when to test for SARS-CoV-2 (1214), latest updates (1181), the COVID-19 telephone list (418) and the SARS-CoV-2 / COVID-19 guideline (280) were the most frequently accessed advice. Seventy-one users with a mean age of 46.1 years from 19 different departments completed the questionnaire. Respondents considered the COVID-19 app clear (54/59; 92%), easy-to-use (46/55; 84%), fast (46/52; 88%), useful (52/56; 93%), and had faith in the information (58/70; 83%). The COVID-19 app was used to quickly look up something (43/68; 63%), when no computer was available (15/68; 22%), look up / dial COVID-related phone numbers (15/68; 22%) or when walking from A to B (11/68; 16%). Few respondents felt app use cost time (5/68; 7%).

Conclusions

Our COVID-19 app proved to be a relatively simple yet innovative tool that was used by HCWs from all disciplines involved in taking care of COVID-19 patients. The up-to-date app was used for different topics and had high user satisfaction amongst questionnaire respondents. An app with local hospital policy could be an invaluable tool during a pandemic.

Using artificial intelligence technology to fight COVID-19: a review

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.

A pilot study using financial transactions' spatial information to define high-risk neighborhoods and distribution pattern of COVID-19

BACKGROUND

Development of surveillance systems based on big data sources with spatial information is necessitated more than ever during this pandemic. Here, we present our pilot results of a new technique for the incorporation of spatial information of transactions and a vital registry of COVID-19 to evaluate the disease spread.

METHODS

We merged two databases of laboratory-confirmed national COVID-19 registry of Iran and financial transactions of point-of-sale devices from February to March 2020 as our training data sources. Spatial information was used for the visualization of maps and movements of sick individuals. We used the point-of-sale devices-related guild to check for the dynamics of financial transactions and effectiveness of quarantines.

FINDINGS

In the study period, 174,428 confirmed cases were in the COVID-19 registry with accompanying transactions information. In total, 13,924,982 financial transactions were performed by them, with a mean of 1.2 per day for each person. All guilds had a decreasing pattern of "risky" transactions except for grocery stores and pharmacies. The latter showed a decreasing pattern by impose of lockdowns. Different cities were the hotspot of disease transmission as many "high-risk" transactions were performed in them, among which Tehran (mainly its central neighborhoods) and southern cities of Lake Urmia predominated. Lockdowns indicated that the disease gradually became less transmissible.

INTERPRETATION

Financial transactions can be readily used for epidemics surveillance. Semi real-time results of such iterations can be informative for policy makers, guild owners, and general population to prepare safer commuting and merchandise spaces.

Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors

As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below a given threshold. Moreover, the results significantly ensure the accuracy of the gathered information while minimizing the cost of deployment and maximizing the lifetime of the WSN. Furthermore, the proposed solution can be readily applied to sensor distribution problems in various fields.

“Through their eyes, I can work” – rural physicians' perceptions about mobile phone use among community health workers – a qualitative analysis

Purpose

Physicians who are primary care providers in rural communities form an essential stakeholder group in rural mobile health (mHealth) delivery. This study was exploratory in nature and was conducted in Udupi district of Karnataka, India. The purpose of this study is to examine the perceptions of rural medical officers (MOs) (rural physicians) regarding the benefits and challenges of mobile phone use by community health workers (CHWs).

Design/methodology/approach

In-depth interviews were conducted among 15 MOs belonging to different primary health centers of the district. Only MOs with a minimum five years of experience were recruited in the study using purposive and snowball sampling. This was followed by thematic analysis of the data collected.

Findings

The perceptions of MOs regarding the CHWs' use of mobile phones were largely positive. However, they reported the existence of some challenges that limits the potential of its full use. The findings were categorized under four themes namely, benefits of mobile phone use to CHWs, benefits of mobile phone-equipped CHWs, current mobile phone use by CHWs and barriers to CHWs' mobile phone use. The significant barriers reported in the CHWs' mobile phone use were poor mobile network coverage, technical illiteracy, lack of consistent technical training and call and data expense of the CHWs. The participants recommend an increased number of mobile towers, frequent training in mobile phone use and basic English language for the CHWs as possible solutions to the barriers.

Originality/value

Studies examining the perceptions of doctors who are a primary stakeholder group in mHealth as well as in the public health system scenario are limited. To the authors’ knowledge, this is one of the first studies to examine the perception of rural doctors regarding CHWs' mobile phone use for work in India.

A survey of COVID-19 contact-tracing apps

Recently, the sudden outbreak of the COVID-19 virus caused a major health crisis by affecting masses around the world. The virus, which is known to be highly contagious, has forced the research community and governments to fight the disease and take prompt actions by applying various strategies to keep the numbers under control. These strategies range from imposing strict social distancing measures, isolating infected cases, and enforcing either a partial or a full lockdown, to mathematical modeling and contact-tracing applications. In this work, we survey the current contact-tracing apps and organize them based on underlying technologies such as Bluetooth, Wi-Fi, GPS, geofencing, and Quick Response (QR) codes. We compare the main features of 22 existing applications and highlight each of the pros and cons associated with these different technologies.

Appraisal of a Contact Tracing Training Program for COVID-19 in Greece Focusing on Vulnerable Populations

BACKGROUND

Contact tracing as an epidemiological strategy has repeatedly contributed to the containment of various past epidemics and succeeded in controlling the spread of disease in the community. Systematic training of contact tracers is crucial in ensuring the effectiveness of epidemic containment.

METHODS

An intensive training course was offered to 216 health and other professionals who work with vulnerable population groups, such as Roma, refugees, and migrants in Greece, by the scientific team of the postgraduate programme "Global Health-Disaster Medicine" of the Medical School, National and Kapodistrian University of Athens, with the support of the Swiss embassy in Greece. The course was delivered online due to the pandemic restriction measures and was comprised of 16 h over 2 days. The course curriculum was adapted in Greek using, upon agreement, a similar training course to what was developed by the Johns Hopkins University Bloomberg School of Public Health. Evaluation of the course was conducted in order to determine the short term satisfaction from participating in this training course.

RESULTS

A total of 70% of the course participants completed the evaluation questionnaires and all trainers gave feedback on the course. The training modules were ranked as extremely useful by the majority of the participants and over 50% of the participants specifically stated that the course content was directly related to their work with vulnerable groups. Content about the ethics of contact tracing and the effective communication skills presented were deemed most useful.

CONCLUSION

The course was well organised and provided the required skills for effective contact tracing. Many course participants intend to use some components in their work with vulnerable populations groups. Contact tracing efforts work best in a systematic and coordinated way and the provision of systematic and organised training can greatly increase its effectiveness.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Mapping the Role of Digital Health Technologies in Prevention and Control of COVID-19 Pandemic: Review of the Literature

BACKGROUND

Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic.

METHODS

We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis.

RESULTS

A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers.

CONCLUSION

Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.

Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review

OBJECTIVES

Using participatory health informatics (PHI) to detect disease outbreaks or learn about pandemics has gained interest in recent years. However, the role of PHI in understanding and managing pandemics, citizens' role in this context, and which methods are relevant for collecting and processing data are still unclear, as is which types of data are relevant. This paper aims to clarify these issues and explore the role of PHI in managing and detecting pandemics.

METHODS

Through a literature review we identified studies that explore the role of PHI in detecting and managing pandemics. Studies from five databases were screened: PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), IEEE Xplore, ACM (Association for Computing Machinery) Digital Library, and Cochrane Library. Data from studies fulfilling the eligibility criteria were extracted and synthesized narratively.

RESULTS

Out of 417 citations retrieved, 53 studies were included in this review. Most research focused on influenza-like illnesses or COVID-19 with at least three papers on other epidemics (Ebola, Zika or measles). The geographic scope ranged from global to concentrating on specific countries. Multiple processing and analysis methods were reported, although often missing relevant information. The majority of outcomes are reported for two application areas: crisis communication and detection of disease outbreaks.

CONCLUSIONS

For most diseases, the small number of studies prevented reaching firm conclusions about the utility of PHI in detecting and monitoring these disease outbreaks. For others, e.g., COVID-19, social media and online search patterns corresponded to disease patterns, and detected disease outbreak earlier than conventional public health methods, thereby suggesting that PHI can contribute to disease and pandemic monitoring.

Development, Acceptance, and Concerns Surrounding App-Based Services to Overcome the COVID-19 Outbreak in South Korea: Web-Based Survey Study

Background

Since the COVID-19 outbreak, South Korea has been engaged in various efforts to overcome the pandemic. One of them is to provide app-based COVID-19–related services to the public. As the pandemic continues, a need for various apps has emerged, including COVID-19 apps that can support activities aimed at overcoming the COVID-19 pandemic.

Objective

We aimed to determine which apps were considered the most necessary according to users and evaluate the current status of the development of COVID-19–related apps in South Korea. We also aimed to determine users’ acceptance and concerns related to using apps to support activities to combat COVID-19.

Methods

We collected data from 1148 users from a web-based survey conducted between November 11 and December 6, 2020. Basic statistical analysis, multiple response analysis, and the Wilcoxon rank sum test were performed using R software. We then manually classified the current status of the development of COVID-19–related apps.

Results

In total, 68.4% (785/1148) of the respondents showed high willingness to protect themselves from COVID-19 by using related apps. Users considered the epidemiological investigation app to be the most necessary app (709/1148, 61.8%) overall, followed by the self-management app for self-isolation (613/1148, 53.4%), self-route management app (605/1148, 52.7%), COVID-19 symptom management app (483/1148, 42.1%), COVID-19–related information provision app (339/1148, 29.5%), and mental health management app (270/1148, 23.5%). Despite the high intention to use these apps, users were also concerned about privacy issues and media exposure. Those who had an underlying disease and had experience using COVID-19–related apps showed significantly higher intentions to use those apps (P=.05 and P=.01, respectively).

Conclusions

Targeting users is very important in order to design and develop the most necessary apps. Furthermore, to gain the public’s trust and make the apps available to as many people as possible, it is vital to develop diverse apps in which privacy protection is maximized.

Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation

The global and national response to the COVID-19 pandemic has been inadequate due to a collective lack of preparation and a shortage of available tools for responding to a large-scale pandemic. By applying lessons learned to create better preventative methods and speedier interventions, the harm of a future pandemic may be dramatically reduced. One potential measure is the widespread use of contact tracing apps. While such apps were designed to combat the COVID-19 pandemic, the time scale in which these apps were deployed proved a significant barrier to efficacy. Many companies and governments sprinted to deploy contact tracing apps that were not properly vetted for performance, privacy, or security issues. The hasty development of incomplete contact tracing apps undermined public trust and negatively influenced perceptions of app efficacy. As a result, many of these apps had poor voluntary public uptake, which greatly decreased the apps' efficacy. Now, with lessons learned from this pandemic, groups can better design and test apps in preparation for the future. In this viewpoint, we outline common strategies employed for contact tracing apps, detail the successes and shortcomings of several prominent apps, and describe lessons learned that may be used to shape effective contact tracing apps for the present and future. Future app designers can keep these lessons in mind to create a version that is suitable for their local culture, especially with regard to local attitudes toward privacy-utility tradeoffs during public health crises.

Big Data Technology Applications and the Right to Health in China during the COVID-19 Pandemic

Individuals have the right to health according to the Constitution and other laws in China. Significant barriers have prevented the full realisation of the right to health in the COVID-19 era. Big data technology, which is a vital tool for COVID-19 containment, has been a central topic of discussion, as it has been used to protect the right to health through public health surveillance, contact tracing, real-time epidemic outbreak monitoring, trend forecasting, online consultations, and the allocation of medical and health resources in China. Big data technology has enabled precise and efficient epidemic prevention and control and has improved the efficiency and accuracy of the diagnosis and treatment of this new form of coronavirus pneumonia due to Chinese institutional factors. Although big data technology has successfully supported the containment of the virus and protected the right to health in the COVID-19 era, it also risks infringing on individual privacy rights. Chinese policymakers should understand the positive and negative impacts of big data technology and should prioritise the Personal Information Protection Law and other laws that are meant to protect and strengthen the right to privacy.

Lessons from countries implementing find, test, trace, isolation and support policies in the rapid response of the COVID-19 pandemic: a systematic review

OBJECTIVE

To systematically learn lessons from the experiences of countries implementing find, test, trace, isolate, support (FTTIS) in the first wave of the COVID-19 pandemic.

DESIGN, DATA SOURCES AND ELIGIBILITY CRITERIA

We searched MEDLINE (PubMed), Cochrane Library, SCOPUS and JSTOR, initially between 31 May 2019 and 21 January 2021. Research articles and reviews on the use of contact tracing, testing, self-isolation and quarantine for COVID-19 management were included in the review.

DATA EXTRACTION AND SYNTHESIS

We extracted information including study objective, design, methods, main findings and implications. These were tabulated and a narrative synthesis was undertaken given the diverse research designs, methods and implications.

RESULTS

We identified and included 118 eligible studies. We identified the core elements of an effective find, test, trace, isolate, support (FTTIS) system needed to interrupt the spread of a novel infectious disease, where treatment or vaccination was not yet available, as pertained in the initial stages of the COVID-19 pandemic. We report methods used to shorten case finding time, improve accuracy and efficiency of tests, coordinate stakeholders and actors involved in an FTTIS system, support individuals isolating and make appropriate use of digital tools.

CONCLUSIONS

We identified in our systematic review the key components of an FTTIS system. These include border controls, restricted entry, inbound traveller quarantine and comprehensive case finding; repeated testing to minimise false diagnoses and pooled testing in resource-limited circumstances; extended quarantine period and the use of digital tools for contact tracing and self-isolation. Support for mental or physical health and livelihoods is needed for individuals undergoing self-isolation/quarantine. An integrated system with rolling-wave planning can best use effective FTTIS tools to respond to the fast-changing COVID-19 pandemic. Results of the review may inform countries considering implementing these measures.

Mass Tracking in Cellular Networks for the COVID-19 Pandemic Monitoring

The year 2020 was marked by the emergence of the COVID-19 pandemic. After months of uncontrolled spread worldwide, a clear conclusion is that controlling the mobility of the general population can slow down the propagation of the pandemic. Tracking the location of the population enables better use of mobility limitation policies and the prediction of potential hotspots, as well as improved alert services to individuals that may have been exposed to the virus. With mobility in their core functionality and a high degree of penetration of mobile devices within the general population, cellular networks are an invaluable asset for this purpose. This paper shows an overview of the possibilities offered by cellular networks for the massive tacking of the population at different levels. The major privacy concerns are also reviewed and a specific use case is shown, correlating mobility and number of cases in the province of Málaga (Spain).

Social Media Use for Health Purposes: Systematic Review

Background

Social media has been widely used for health-related purposes, especially during the COVID-19 pandemic. Previous reviews have summarized social media uses for a specific health purpose such as health interventions, health campaigns, medical education, and disease outbreak surveillance. The most recent comprehensive review of social media uses for health purposes, however, was conducted in 2013. A systematic review that covers various health purposes is needed to reveal the new usages and research gaps that emerge in recent years.

Objective

This study aimed to provide a systematic review of social media uses for health purposes that have been identified in previous studies.

Methods

The researchers searched for peer-reviewed journal articles published between 2006 and 2020 in 12 databases covering medicine, public health, and social science. After coding the articles in terms of publication year, journal area, country, method, social media platform, and social media use for health purposes, the researchers provided a review of social media use for health purposes identified in these articles.

Results

This study summarized 10 social media uses for various health purposes by health institutions, health researchers and practitioners, and the public.

Conclusions

Social media can be used for various health purposes. Several new usages have emerged since 2013 including advancing health research and practice, social mobilization, and facilitating offline health-related services and events. Research gaps exist regarding advancing strategic use of social media based on audience segmentation, evaluating the impact of social media in health interventions, understanding the impact of health identity development, and addressing privacy concerns.

Artificial intelligence-based approaches for COVID-19 patient management

During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.

The construction and visualization of the transmission networks for COVID-19: A potential solution for contact tracing and assessments of epidemics

The WHO has described coronavirus disease 2019 (COVID-19) as a pandemic due to the speed and scale of its transmission. Without effective interventions, the rapidly increasing number of COVID-19 cases would greatly increase the burden of clinical treatments. Identifying the transmission sources and pathways is of vital importance to block transmission and allocate limited public health resources. According to the relationships among cases, we constructed disease transmission network graphs for the COVID-19 epidemic through a visualization technique based on individual reports of epidemiological data. We proposed an analysis strategy of the transmission network with the epidemiological data in Tianjin and Chengdu. The transmission networks showed different transmission characteristics. In Tianjin, an imported case of COVID-19 can produce an average of 2.9 secondary infections and ultimately produce as many as 4 generations of infections, with a maximum of 6 cases being generated before the imported case is identified. In Chengdu, 45 noninformative cases and 24 cases with vague exposure information made accurate information about the transmission network difficult to provide. The proposed analysis framework of visualized transmission networks can trace the transmission source and contacts, assess the current situation of transmission and prevention, and provide evidence for the global response and control of the COVID-19 pandemic.

App Use and Usability of a Barcode-Based Digital Platform to Augment COVID-19 Contact Tracing: Postpilot Survey and Paradata Analysis

BACKGROUND

The COVID-19 pandemic has drastically changed life in the United States, as the country has recorded over 23 million cases and 383,000 deaths to date. In the leadup to widespread vaccine deployment, testing and surveillance are critical for detecting and stopping possible routes of transmission. Contact tracing has become an important surveillance measure to control COVID-19 in the United States, and mobile health interventions have found increased prominence in this space.

OBJECTIVE

The aim of this study was to investigate the use and usability of MyCOVIDKey, a mobile-based web app to assist COVID-19 contact tracing efforts, during the 6-week pilot period.

METHODS

A 6-week study was conducted on the Vanderbilt University campus in Nashville, Tennessee. The study participants, consisting primarily of graduate students, postdoctoral researchers, and faculty in the Chemistry Department at Vanderbilt University, were asked to use the MyCOVIDKey web app during the course of the study period. Paradata were collected as users engaged with the MyCOVIDKey web app. At the end of the study, all participants were asked to report on their user experience in a survey, and the results were analyzed in the context of the user paradata.

RESULTS

During the pilot period, 45 users enrolled in MyCOVIDKey. An analysis of their enrollment suggests that initial recruiting efforts were effective; however, participant recruitment and engagement efforts at the midpoint of the study were less effective. App use paralleled the number of users, indicating that incentives were useful for recruiting new users to sign up but did not result in users attempting to artificially inflate their use as a result of prize offers. Times to completion of key tasks were low, indicating that the main features of the app could be used quickly. Of the 45 users, 30 provided feedback through a postpilot survey, with 26 (58%) completing it in its entirety. The MyCOVIDKey app as a whole was rated 70.0 on the System Usability Scale, indicating that it performed above the accepted threshold for usability. When the key-in and self-assessment features were examined on their own, it was found that they individually crossed the same thresholds for acceptable usability but that the key-in feature had a higher margin for improvement.

CONCLUSIONS

The MyCOVIDKey app was found overall to be a useful tool for COVID-19 contact tracing in a university setting. Most users suggested simple-to-implement improvements, such as replacing the web app framework with a native app format or changing the placement of the scanner within the app workflow. After these updates, this tool could be readily deployed and easily adapted to other settings across the country. The need for digital contact tracing tools is becoming increasingly apparent, particularly as COVID-19 case numbers continue to increase while more businesses begin to reopen.

Understanding On-Campus Interactions With a Semiautomated, Barcode-Based Platform to Augment COVID-19 Contact Tracing: App Development and Usage

BACKGROUND

The COVID-19 pandemic has forced drastic changes to daily life, from the implementation of stay-at-home orders to mandating facial coverings and limiting in-person gatherings. While the relaxation of these control measures has varied geographically, it is widely agreed that contact tracing efforts will play a major role in the successful reopening of businesses and schools. As the volume of positive cases has increased in the United States, it has become clear that there is room for digital health interventions to assist in contact tracing.

OBJECTIVE

The goal of this study was to evaluate the use of a mobile-friendly app designed to supplement manual COVID-19 contact tracing efforts on a university campus. Here, we present the results of a development and validation study centered around the use of the MyCOVIDKey app on the Vanderbilt University campus during the summer of 2020.

METHODS

We performed a 6-week pilot study in the Stevenson Center Science and Engineering Complex on Vanderbilt University's campus in Nashville, TN. Graduate students, postdoctoral fellows, faculty, and staff >18 years who worked in Stevenson Center and had access to a mobile phone were eligible to register for a MyCOVIDKey account. All users were encouraged to complete regular self-assessments of COVID-19 risk and to key in to sites by scanning a location-specific barcode.

RESULTS

Between June 17, 2020, and July 29, 2020, 45 unique participants created MyCOVIDKey accounts. These users performed 227 self-assessments and 1410 key-ins. Self-assessments were performed by 89% (n=40) of users, 71% (n=32) of users keyed in, and 48 unique locations (of 71 possible locations) were visited. Overall, 89% (202/227) of assessments were determined to be low risk (ie, asymptomatic with no known exposures), and these assessments yielded a CLEAR status. The remaining self-assessments received a status of NOT CLEAR, indicating either risk of exposure or symptoms suggestive of COVID-19 (7.5% [n=17] and 3.5% [n=8] of self-assessments indicated moderate and high risk, respectively). These 25 instances came from 8 unique users, and in 19 of these instances, the at-risk user keyed in to a location on campus.

CONCLUSIONS

Digital contact tracing tools may be useful in assisting organizations to identify persons at risk of COVID-19 through contact tracing, or in locating places that may need to be cleaned or disinfected after being visited by an index case. Incentives to continue the use of such tools can improve uptake, and their continued usage increases utility to both organizational and public health efforts. Parameters of digital tools, including MyCOVIDKey, should ideally be optimized to supplement existing contact tracing efforts. These tools represent a critical addition to manual contact tracing efforts during reopening and sustained regular activity.

An analysis of the development of digital health technologies to fight COVID-19 in Brazil and the world

The coronavirus pandemic that struck the world in late 2019 continues to break records of new cases and deaths from the disease. Guidelines for clinical management of infected patients and prevention of new cases focus on measures to control symptoms, hygiene habits, social distancing, and decrease in human crowding. This forced a change in the way health services provide care, generating the incorporation of new health technologies. The Essay thus aims to compile and analyze experiences in the use of digital health technologies to minimize the impacts of COVID-19. The authors identified the development of technological solutions for clinical management of patients, imaging diagnosis, use of artificial intelligence for risk analysis, geolocation apps, data analysis and reports, self-diagnosis, and even orientation for decision-making. The great majority of the initiatives listed here prove effective in minimizing the impacts of COVID-19 on health systems and aim to decrease human crowding and thus facilitate access to services, besides contributing to the incorporation of new health practices and modes of care.

Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review

BACKGROUND

COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has become a global pandemic, affecting most countries worldwide. Digital health information technologies can be applied in three aspects, namely digital patients, digital devices, and digital clinics, and could be useful in fighting the COVID-19 pandemic.

OBJECTIVE

Recent reviews have examined the role of digital health in controlling COVID-19 to identify the potential of digital health interventions to fight the disease. However, this study aims to review and analyze the digital technology that is being applied to control the COVID-19 pandemic in the 10 countries with the highest prevalence of the disease.

METHODS

For this review, the Google Scholar, PubMed, Web of Science, and Scopus databases were searched in August 2020 to retrieve publications from December 2019 to March 15, 2020. Furthermore, the Google search engine was used to identify additional applications of digital health for COVID-19 pandemic control.

RESULTS

We included 32 papers in this review that reported 37 digital health applications for COVID-19 control. The most common digital health projects to address COVID-19 were telemedicine visits (11/37, 30%). Digital learning packages for informing people about the disease, geographic information systems and quick response code applications for real-time case tracking, and cloud- or mobile-based systems for self-care and patient tracking were in the second rank of digital tool applications (all 7/37, 19%). The projects were deployed in various European countries and in the United States, Australia, and China.

CONCLUSIONS

Considering the potential of available information technologies worldwide in the 21st century, particularly in developed countries, it appears that more digital health products with a higher level of intelligence capability remain to be applied for the management of pandemics and health-related crises.

Structural Racism in the COVID-19 Pandemic: Moving Forward

The COVID-19 pandemic has taken a substantial human, social and economic toll globally, but its impact on Black/African Americans, Latinx, and American Indian/Alaska Native communities in the U.S. is unconscionable. As the U.S. continues to combat the current COVID-19 cycle and prepares for future pandemics, it will be critical to learn from and rectify past and contemporary wrongs. Drawing on experiences in genomic research and intersecting areas in medical ethics, health disparities, and human rights, this article considers three key COVID-19-related issues: research to identify remedies; testing, contact tracing and surveillance; and lingering health needs and disability. It provides a pathway for the future: community engagement to develop culturally-sensitive responses to the myriad genomic/bioethical dilemmas that arise, and the establishment of a Truth and Reconciliation Commission to transition the country from its contemporary state of segregation in healthcare and health outcomes into an equitable and prosperous society for all.

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

BACKGROUND

The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely.

OBJECTIVE

Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province.

METHODS

A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses.

RESULTS

An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government's big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts.

CONCLUSIONS

During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers

Conventional testing and diagnostic methods for infections like SARS-CoV-2 have limitations for population health management and public policy. We hypothesize that daily changes in autonomic activity, measured through off-the-shelf technologies together with app-based cognitive assessments, may be used to forecast the onset of symptoms consistent with a viral illness. We describe our strategy using an AI model that can predict, with 82% accuracy (negative predictive value 97%, specificity 83%, sensitivity 79%, precision 34%), the likelihood of developing symptoms consistent with a viral infection three days before symptom onset. The model correctly predicts, almost all of the time (97%), individuals who will not develop viral-like illness symptoms in the next three days. Conversely, the model correctly predicts as positive 34% of the time, individuals who will develop viral-like illness symptoms in the next three days. This model uses a conservative framework, warning potentially pre-symptomatic individuals to socially isolate while minimizing warnings to individuals with a low likelihood of developing viral-like symptoms in the next three days. To our knowledge, this is the first study using wearables and apps with machine learning to predict the occurrence of viral illness-like symptoms. The demonstrated approach to forecasting the onset of viral illness-like symptoms offers a novel, digital decision-making tool for public health safety by potentially limiting viral transmission.

Attitudes Toward Using COVID-19 mHealth Tools Among Adults With Chronic Health Conditions: Secondary Data Analysis of the COVID-19 Impact Survey

Background

Adults with chronic conditions are disproportionately burdened by COVID-19 morbidity and mortality. Although COVID-19 mobile health (mHealth) apps have emerged, research on attitudes toward using COVID-19 mHealth tools among those with chronic conditions is scarce.

Objective

This study aimed to examine attitudes toward COVID-19, identify determinants of COVID-19 mHealth tool use across demographic and health-related characteristics, and evaluate associations between chronic health conditions and attitudes toward using COVID-19 mHealth tools (eg, mHealth or web-based methods for tracking COVID-19 exposures, symptoms, and recommendations).

Methods

We used nationally representative data from the COVID-19 Impact Survey collected from April to June 2020 (n=10,760). Primary exposure was a history of chronic conditions, which were defined as self-reported diagnoses of cardiometabolic, respiratory, immune-related, and mental health conditions and overweight/obesity. Primary outcomes were attitudes toward COVID-19 mHealth tools, including the likelihood of using (1) a mobile phone app to track COVID-19 symptoms and receive recommendations; (2) a website to track COVID-19 symptoms, track location, and receive recommendations; and (3) an app using location data to track potential COVID-19 exposure. Outcome response options for COVID-19 mHealth tool use were extremely/very likely, moderately likely, or not too likely/not likely at all. Multinomial logistic regression was used to compare the likelihood of COVID-19 mHealth tool use between people with different chronic health conditions, with not too likely/not likely at all responses used as the reference category for each outcome. We evaluated the determinants of each COVID-19 mHealth intervention using Poisson regression.

Results

Of the 10,760 respondents, 21.8% of respondents were extremely/very likely to use a mobile phone app or a website to track their COVID-19 symptoms and receive recommendations. Additionally, 24.1% of respondents were extremely/very likely to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19. After adjusting for age, race/ethnicity, sex, socioeconomic status, and residence, adults with mental health conditions were the most likely to report being extremely/very or moderately likely to use each mHealth intervention compared to those without such conditions. Adults with respiratory-related chronic diseases were extremely/very (conditional odds ratio 1.16, 95% CI 1.00-1.35) and moderately likely (conditional odds ratio 1.23, 95% CI 1.04-1.45) to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19.

Conclusions

Our study demonstrates that attitudes toward using COVID-19 mHealth tools vary widely across modalities (eg, web-based method vs app) and chronic health conditions. These findings may inform the adoption of long-term engagement with COVID-19 apps, which is crucial for determining their potential in reducing disparities in COVID-19 morbidity and mortality among individuals with chronic health conditions.

Evaluation of the Design and Implementation of a Peer-To-Peer COVID-19 Contact Tracing Mobile App (COCOA) in Japan

We evaluate a Bluetooth-based mobile contact-confirming app, COVID-19 Contact-Confirming Application (COCOA), which is being used in Japan to contain the spread of COVID-19, the disease caused by the novel virus termed SARS-COV-2. The app prioritizes the protection of users' privacy from a variety of parties (eg, other users, potential attackers, and public authorities), enhances the capacity to balance the current load of excessive pressure on health care systems (eg, local triage of exposure risk and reduction of in-person hospital visits), increases the speed of responses to the pandemic (eg, automated recording of close contact based on proximity), and reduces operation errors and population mobility. The peer-to-peer framework of COCOA is intended to provide the public with dynamic and credible updates on the COVID-19 pandemic without sacrificing the privacy of their information. However, cautions must be exercised to address critical concerns, such as the rate of participation and delays in data sharing. The results of a simulation imply that the participation rate in Japan needs to be close 90% to effectively control the spread of COVID-19.

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic. Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining. Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Geospatial techniques, especially WebGIS, have even been widely used to visualize the data on a map and were critical to informing the public regarding the spread of the virus, especially during the early days of the pandemic. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our review also highlighted how scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future. Conclusions:  Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic. Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining. Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Although GIS has substantial potential in planning to slow down the spread, surveillance, contact tracing, and identify the trends and hotspots of breakdowns, it was not employed as much as it could have been. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that this geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our systematic review also provides how both scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future. Conclusions:  Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.

Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review

BACKGROUND

The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.

OBJECTIVE

Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.

METHODS

We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.

RESULTS

We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.

CONCLUSIONS

We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).