Use of a Real-Time Locating System for Contact Tracing of Health Care Workers During the COVID-19 Pandemic at an Infectious Disease Center in Singapore: Validation Study

Identifying Contact Time Required for Secondary Transmission of Clostridioides difficile Infections by Using Real-Time Locating System

Considering patient room shortages and prevalence of other communicable diseases, reassessing the isolation of patients with Clostridioides difficile infection (CDI) is imperative. We conducted a retrospective study to investigate the secondary CDI transmission rate in a hospital in South Korea, where patients with CDI were not isolated. Using data from a real-time locating system and electronic medical records, we investigated patients who had both direct and indirect contact with CDI index patients. The primary outcome was secondary CDI transmission, identified by whole-genome sequencing. Among 909 direct and 2,711 indirect contact cases, 2 instances of secondary transmission were observed (2 [0.05%] of 3,620 cases), 1 transmission via direct contact and 1 via environmental sources. A low level of direct contact (113 minutes) was required for secondary CDI transmission. Our findings support the adoption of exhaustive standard preventive measures, including environmental decontamination, rather than contact isolation of CDI patients in nonoutbreak settings.

Infection Risk Prediction in Healthcare Settings: Lessons from COVID-19 Contact Tracing

Objective

Contact tracing aids epidemic control by enabling early detection and isolation without overburdening healthcare systems despite potential challenges. This study aimed to evaluate the practical application of contact and risk assessment-based models in predicting SARS-CoV-2 infection following exposure among healthcare workers in a large tertiary public university hospital in Türkiye.

Materials and Methods

The study was designed as a retrospective cohort study, including contact tracing data from 3389 exposed healthcare workers from March 23, 2020, to October 22, 2021. Contact-based (mask use, contact duration and distance) and exposure risk-assessment-based (low, medium, high-risk) models with and without having symptoms were generated using logistic regression. SARS-CoV-2 infection was defined as having a positive SARS-CoV-2 RT-PCR test result. Adjustments were made to the models for demographic and occupational variables, previous infection, and vaccination. Model parameters were compared.

Results

Of 3389 exposed healthcare workers, 2451 underwent RT-PCR testing. Among those tested, RT-PCR positivity was 5.9% (144/2451). Lack of personal protective equipment use (odds ratio [OR]=1.64, 95% confidence interval [CI]=1.03-2.66) and ≥15 minutes of contact duration (1.89, 1.21-3.09) were significantly associated with RT-PCR positivity. In the risk-assessment model, being a high-risk contact increased the odds of RT-PCR positivity (OR=2.76, 95% CI=1.61-5.03). Adding the presence of symptoms to contact-based and risk assessment models improved model parameters (Akaike information criterion [AIC]: from 1086.1 to 1083.1; Tjur's R2: from 0.016 to 0.019, respectively).

Conclusion

The inclusion of being symptomatic improved the contact-based and risk assessment-based models. Institutions should be encouraged to incorporate symptom inquiries into risk assessment protocols in response to newly emerging respiratory virus epidemics. Institutions lacking the capacity for extensive contact tracing are recommended, at minimum, to track symptomatic exposed workers for epidemic control.

Evaluating completion rates of COVID-19 contact tracing surveys in New York City

IMPORTANCE

Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs.

OBJECTIVE

To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021.

METHODS

We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used.

RESULTS

The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age <  = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age <  = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%.

CONCLUSION

These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.

Harnessing a real-time location system for contact tracing in a busy emergency department

BACKGROUND

With the persistent threat of emerging infectious diseases (EIDs), digital contact tracing (CT) tools can augment conventional CT for the prevention of healthcare-associated infectious disease transmission. However, their performance has yet to be evaluated comprehensively in the fast-paced emergency department (ED) setting.

OBJECTIVE

This study compared the CT performance of a radiofrequency identification (RFID)-based real-time location system (RTLS) with conventional electronic medical record (EMR) review against continuous direct observation of close contacts ('gold standard') in a busy ED during the coronavirus disease 2019 pandemic period.

METHODS

This cross-sectional study was conducted at the ED of a large tertiary care hospital in Singapore from December 2020 to April 2021. CT performance [sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and kappa] of the RTLS, EMR review and a combination of the two approaches (hybrid CT) was compared with direct observation. Finally, the mean absolute error (MAE) in the duration of each contact episode found via the RTLS and direct observation was calculated.

RESULTS

In comparison with EMR review, both the RTLS and the hybrid CT approach had higher sensitivity (0.955 vs 0.455 for EMR review) and a higher NPV (0.997 vs 0.968 for EMR review). The RTLS had the highest PPV (0.777 vs 0.714 for EMR review vs 0.712 for hybrid CT). The RTLS had the strongest agreement with direct observation (kappa=0.848). The MAE between contact durations of 80 direct observations and their respective RTLS contact times was 1.81 min.

CONCLUSION

The RTLS was validated to be a high-performing CT tool, with significantly higher sensitivity than conventional CT via EMR review. The RTLS can be used with confidence in time-strapped EDs for time-sensitive CT for the prevention of healthcare-associated transmission of EIDs.

Continued Use of Contact-Tracing Apps in the United States and the United Kingdom: Insights From a Comparative Study Through the Lens of the Health Belief Model

BACKGROUND

To contain the spread of SARS-CoV-2, contact-tracing (CT) mobile apps were developed and deployed to identify and notify individuals who have exposure to the virus. However, the effectiveness of these apps depends not only on their adoption by the general population but also on their continued use in the long term. Limited research has investigated the facilitators of and barriers to the continued use of CT apps.

OBJECTIVE

In this study, we aimed to examine factors influencing the continued use intentions of CT apps based on the health belief model. In addition, we investigated the differences between users and nonusers and between the US and UK populations.

METHODS

We administered a survey in the United States and the United Kingdom. Respondents included individuals who had previously used CT technologies and those without experience. We used the structural equation modeling technique to validate the proposed research model and hypotheses.

RESULTS

Analysis of data collected from 362 individuals showed that perceived benefits, self-efficacy, perceived severity, perceived susceptibility, and cues to action positively predicted the continued use intentions of CT apps, while perceived barriers could reduce them. We observed few differences between the US and UK groups; the only exception was the effect of COVID-19 threat susceptibility, which was significant for the UK group but not for the US group. Finally, we found that the only significant difference between users and nonusers was related to perceived barriers, which may not influence nonusers' continued use intentions but significantly reduce experienced users' intentions.

CONCLUSIONS

Our findings have implications for technological design and policy. These insights can potentially help governments, technology companies, and media outlets to create strategies and policies to promote app adoption for new users and sustain continued use for existing users in the long run.

An Enhanced Ambulatory Surgery Experience for Patients with Cancer Through End-to-End Patient Engagement

Ambulatory surgery centers (ASC) serve an important role for hospital systems of increasing operating capacity and offloading patient volume. When seeking to perform more complex cancer surgeries at an ASC, a systematic approach with care pathways can yield success by facilitating quick recovery for patients and reducing complication rates. End-to-end patient engagement is a key component of patient-centered care at the Josie Robertson Surgery Center and begins the moment the decision to have surgery is made and extends to the postdischarge period to track recovery. Engagement includes comprehensive education, standardization of processes, and setting clear expectations for recovery and discharge.

Measuring nursing movements using real time tracking data at the Royal Wolverhampton Hospital Trust

BACKGROUND

Real time location systems (RTLS) are increasingly used in healthcare with applications that include contract tracing and staffing. However, their potential to provide organizational insights requires staff compliance with the system.

MATERIALS AND METHODS

Our goal is to assess how many nurses are using the RTLS correctly (i.e. complying to the system). We collect RTLS data on the movements of nurses at the Royal Wolverhampton NHS Trust. We identify the number of RTLS active nurses and compare it to what expected from the nurses' rotas.

RESULTS

We find that a significant number of nurses appear not to be active from the RTLS data. For approximately 15% of the active users, RTLS records below 10 movements per day. Nevertheless, most of the active users have daily RTLS times consistent with the average shift length.

CONCLUSION

Applications of RTLS data may need to account for imperfect compliance of staff to the system.

Contact tracing using real-time location system (RTLS): a simulation exercise in a tertiary hospital in Singapore

OBJECTIVE

We aim to assess the effectiveness of contact tracing using real-time location system (RTLS) compared with the conventional (electronic medical records (EMRs)) method via an emerging infectious disease (EID) outbreak simulation exercise. The aims of the study are: (1) to compare the time taken to perform contact tracing and list of contacts identified for RTLS versus EMR; (2) to compare manpower and manpower-hours required to perform contact tracing for RTLS versus EMR; and (3) to extrapolate the cost incurred by RTLS versus EMR.

DESIGN

Prospective case study.

SETTING

Sengkang General Hospital, a 1000-bedded public tertiary hospital in Singapore.

PARTICIPANTS

1000 out of 4000 staff wore staff tags in this study.

INTERVENTIONS

A simulation exercise to determine and compare the list of contacts, time taken, manpower and manpower-hours required between RTLS and conventional methods of contact tracing. Cost of both methods were compared.

PRIMARY AND SECONDARY OUTCOME MEASURES

List of contacts, time taken, manpower required, manpower-hours required and cost incurred.

RESULTS

RTLS identified almost three times the number of contacts compared with conventional methods, while achieving that with a 96.2% reduction in time taken, 97.6% reduction in manpower required and 97.5% reduction in manpower-hours required. However, RTLS incurred significant equipment cost and might take many contact tracing episodes before providing economic benefit.

CONCLUSION

Although costly, RTLS is effective in contact tracing. RLTS might not be ready at present time to replace conventional methods, but with further refinement, RTLS has the potential to be the gold standard in contact tracing methods of the future, particularly in the current pandemic.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

BACKGROUND

Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

METHODS

We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.

FINDINGS

The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).

INTERPRETATION

Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.

FUNDING

Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Assessing the Implementation of Digital Innovations in Response to the COVID-19 Pandemic to Address Key Public Health Functions: Scoping Review of Academic and Nonacademic Literature

Background

Digital technologies have been central to efforts to respond to the COVID-19 pandemic. In this context, a range of literature has reported on developments regarding the implementation of new digital technologies for COVID-19–related surveillance, prevention, and control.

Objective

In this study, scoping reviews of academic and nonacademic literature were undertaken to obtain an overview of the evidence regarding digital innovations implemented to address key public health functions in the context of the COVID-19 pandemic. This study aimed to expand on the work of existing reviews by drawing on additional data sources (including nonacademic sources) by considering literature published over a longer time frame and analyzing data in terms of the number of unique digital innovations.

Methods

We conducted a scoping review of the academic literature published between January 1, 2020, and September 15, 2020, supplemented by a further scoping review of selected nonacademic literature published between January 1, 2020, and October 13, 2020. Both reviews followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach.

Results

A total of 226 academic articles and 406 nonacademic articles were included. The included articles provided evidence of 561 (academic literature) and 497 (nonacademic literature) unique digital innovations. The most common implementation settings for digital innovations were the United States, China, India, and the United Kingdom. Technologies most commonly used by digital innovations were those belonging to the high-level technology group of integrated and ubiquitous fixed and mobile networks. The key public health functions most commonly addressed by digital innovations were communication and collaboration and surveillance and monitoring.

Conclusions

Digital innovations implemented in response to the COVID-19 pandemic have been wide ranging in terms of their implementation settings, the digital technologies used, and the public health functions addressed. However, evidence gathered through this study also points to a range of barriers that have affected the successful implementation of digital technologies for public health functions. It is also evident that many digital innovations implemented in response to the COVID-19 pandemic are yet to be formally evaluated or assessed.

A socio-technical systems analysis of the application of RFID-enabled technology to the transport of precious laboratory samples in a large acute teaching hospital

The scale and pace of improvement in patient safety in healthcare has been unacceptably slow. A paucity of research into the application of systems-thinking concepts and a failure to appreciate health systems complexity are cited as barriers to sustainable health systems improvement. This study reports on a socio-technical systems analysis, called the CUBE, of the characteristics of a large acute teaching hospital's system for the transport of precious specimens, a system enabled by radio-frequency identification tracking technology. The CUBE proved itself to be an effective analytic tool. The analysis provided a constructive framework to link diverse data and documentation; explicitly inviting consideration of the roles and understandings of different stakeholders; as well as broader cultural factors that could influence current or future activity. The analysis also supported recommendations to improve and extend operations. This study supports the argument for systems understanding and systems thinking being at the core of new approaches to patient safety.

Nursing Workflow Change in a COVID-19 Inpatient Unit Following the Deployment of Inpatient Telehealth: Observational Study Using a Real-Time Locating System

BACKGROUND

The COVID-19 pandemic prompted widespread implementation of telehealth, including in the inpatient setting, with the goals to reduce potential pathogen exposure events and personal protective equipment (PPE) utilization. Nursing workflow adaptations in these novel environments are of particular interest given the association between nursing time at the bedside and patient safety. Understanding the frequency and duration of nurse-patient encounters following the introduction of a novel telehealth platform in the context of COVID-19 may therefore provide insight into downstream impacts on patient safety, pathogen exposure, and PPE utilization.

OBJECTIVE

The aim of this study was to evaluate changes in nursing workflow relative to prepandemic levels using a real-time locating system (RTLS) following the deployment of inpatient telehealth on a COVID-19 unit.

METHODS

In March 2020, telehealth was installed in patient rooms in a COVID-19 unit and on movable carts in 3 comparison units. The existing RTLS captured nurse movement during 1 pre- and 5 postpandemic stages (January-December 2020). Change in direct nurse-patient encounters, time spent in patient rooms per encounter, and total time spent with patients per shift relative to baseline were calculated. Generalized linear models assessed difference-in-differences in outcomes between COVID-19 and comparison units. Telehealth adoption was captured and reported at the unit level.

RESULTS

Change in frequency of encounters and time spent per encounter from baseline differed between the COVID-19 and comparison units at all stages of the pandemic (all P<.001). Frequency of encounters decreased (difference-in-differences range -6.6 to -14.1 encounters) and duration of encounters increased (difference-in-differences range 1.8 to 6.2 minutes) from baseline to a greater extent in the COVID-19 units relative to the comparison units. At most stages of the pandemic, the change in total time nurses spent in patient rooms per patient per shift from baseline did not differ between the COVID-19 and comparison units (all P>.17). The primary COVID-19 unit quickly adopted telehealth technology during the observation period, initiating 15,088 encounters that averaged 6.6 minutes (SD 13.6) each.

CONCLUSIONS

RTLS movement data suggest that total nursing time at the bedside remained unchanged following the deployment of inpatient telehealth in a COVID-19 unit. Compared to other units with shared mobile telehealth units, the frequency of nurse-patient in-person encounters decreased and the duration lengthened on a COVID-19 unit with in-room telehealth availability, indicating "batched" redistribution of work to maintain total time at bedside relative to prepandemic periods. The simultaneous adoption of telehealth suggests that virtual care was a complement to, rather than a replacement for, in-person care. However, study limitations preclude our ability to draw a causal link between nursing workflow change and telehealth adoption. Thus, further evaluation is needed to determine potential downstream implications on disease transmission, PPE utilization, and patient safety.

Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals

Background

Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals.

Objective

This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals.

Methods

This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time.

Results

From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system—1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53).

Conclusions

As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.

An Effective Mechanism for the Early Detection and Containment of Healthcare Worker Infections in the Setting of the COVID-19 Pandemic: A Systematic Review and Meta-Synthesis

The COVID-19 pandemic has exposed healthcare workers (HCWs) to serious infection risks. In this context, the proactive monitoring of HCWs is the first step toward reducing intrahospital transmissions and safeguarding the HCW population, as well as reflecting the preparedness and response of the healthcare system. As such, this study systematically reviewed the literature on evidence-based effective monitoring measures for HCWs during the COVID-19 pandemic. This was followed by a meta-synthesis to compile the key findings, thus, providing a clearer overall understanding of the subject. Effective monitoring measures of syndromic surveillance, testing, contact tracing, and exposure management are distilled and further integrated to create a whole-process monitoring workflow framework. Taken together, a mechanism for the early detection and containment of HCW infections is, thus, constituted, providing a composite set of practical recommendations to healthcare facility leadership and policy makers to reduce nosocomial transmission rates while maintaining adequate staff for medical services. In this regard, our study paves the way for future studies aimed at strengthening surveillance capacities and upgrading public health system resilience, in order to respond more efficiently to future pandemic threats.

Wireless Technologies for Social Distancing in the Time of COVID-19: Literature Review, Open Issues, and Limitations

This research aims to provide a comprehensive background on social distancing as well as effective technologies that can be used to facilitate the social distancing practice. Scenarios of enabling wireless and emerging technologies are presented, which are especially effective in monitoring and keeping distance amongst people. In addition, detailed taxonomy is proposed summarizing the essential elements such as implementation type, scenarios, and technology being used. This research reviews and analyzes existing social distancing studies that focus on employing different kinds of technologies to fight the Coronavirus disease (COVID-19) pandemic. This study main goal is to identify and discuss the issues, challenges, weaknesses and limitations found in the existing models and/or systems to provide a clear understanding of the area. Articles were systematically collected and filtered based on certain criteria and within ten years span. The findings of this study will support future researchers and developers to solve specific issues and challenges, fill research gaps, and improve social distancing systems to fight pandemics similar to COVID-19.

Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions

The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Pilot Evaluations of Two Bluetooth Contact Tracing Approaches on a University Campus: Mixed Methods Study

BACKGROUND

Many have proposed the use of Bluetooth technology to help scale up contact tracing for COVID-19. However, much remains unknown about the accuracy of this technology in real-world settings, the attitudes of potential users, and the differences between delivery formats (mobile app vs carriable or wearable devices).

OBJECTIVE

We pilot tested 2 separate Bluetooth contact tracing technologies on a university campus to evaluate their sensitivity and specificity, and to learn from the experiences of the participants.

METHODS

We used a convergent mixed methods study design, and participants included graduate students and researchers working on a university campus during June and July 2020. We conducted separate 2-week pilot studies for each Bluetooth technology. The first was for a mobile phone app ("app pilot"), and the second was for a small electronic "tag" ("tag pilot"). Participants validated a list of Bluetooth-identified contacts daily and reported additional close contacts not identified by Bluetooth. We used these data to estimate sensitivity and specificity. Participants completed a postparticipation survey regarding appropriateness, usability, acceptability, and adherence, and provided additional feedback via free text. We used tests of proportions to evaluate differences in survey responses between participants from each pilot, paired t tests to measure differences between compatible survey questions, and qualitative analysis to evaluate the survey's free-text responses.

RESULTS

Among 25 participants in the app pilot, 53 contact interactions were identified by Bluetooth and an additional 61 by self-report. Among 17 participants in the tag pilot, 171 contact interactions were identified by Bluetooth and an additional 4 by self-report. The tag had significantly higher sensitivity compared with the app (46/49, 94% vs 35/61, 57%; P<.001), as well as higher specificity (120/126, 95% vs 123/141, 87%; P=.02). Most participants felt that Bluetooth contact tracing was appropriate on campus (26/32, 81%), while significantly fewer participants felt that using other technologies, such as GPS or Wi-Fi, was appropriate (17/31, 55%; P=.02). Most participants preferred technology developed and managed by the university rather than a third party (27/32, 84%) and preferred not to have tracing apps on their personal phones (21/32, 66%), due to "concerns with privacy." There were no significant differences in self-reported adherence rates across pilots.

CONCLUSIONS

Convenient and carriable Bluetooth technology may improve tracing efficiency while alleviating privacy concerns by shifting data collection away from personal devices. With accuracy comparable to, and in this case, superior to, mobile phone apps, such approaches may be suitable for workplace or school settings with the ability to purchase and maintain physical devices.

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic

OBJECTIVE

This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019.

MATERIALS AND METHODS

We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process.

RESULTS

Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall.

CONCLUSION

Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions.

Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review

BACKGROUND

Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown.

OBJECTIVE

This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature.

METHODS

An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality.

RESULTS

Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread.

CONCLUSIONS

Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality.

Performance of hospital-based contact tracing for COVID-19 during Australia's second wave

BACKGROUND

Hospital-based contact tracing aims to limit spread of COVID-19 within healthcare facilities. In large outbreaks, this can stretch resources and workforce due to quarantine of uninfected staff. We analysed the performance of a manual contact tracing system for healthcare workers (HCW) at a multi-site healthcare facility in Melbourne, Australia, from June-September 2020, during an epidemic of COVID-19.

METHODS

All HCW close contacts were quarantined for 14 days, and tested around day 11, if not already diagnosed with COVID-19. We examined the prevalence and timing of symptoms in cases detected during quarantine, described this group as proportions of all close contacts and of all cases, and used logistic regression to assess factors associated with infection.

RESULTS

COVID-19 was diagnosed during quarantine in 52 furloughed HCWs, from 483 quarantine episodes (11%), accounting for 19% (52/270) of total HCW cases. In 361 exposures to a clear index case, odds of infection were higher after contact with an infectious patient compared to an infectious HCW (aOR: 4.69, 95% CI: 1.98-12.14). Contact with cases outside the workplace increased odds of infection compared to workplace contact only (aOR: 7.70, 95% CI: 2.63-23.05). We estimated 30%, 78% and 95% of symptomatic cases would develop symptoms by days 3, 7, and 11 of quarantine, respectively.

CONCLUSION

In our setting, hospital-based contact tracing detected and contained a significant proportion of HCW cases, without excessive quarantine of uninfected staff. Effectiveness of contact tracing is determined by a range of dynamic factors, so system performance should be monitored in real-time.

Occupational Health and Safety Measures in Healthcare Settings during COVID-19: Strategies for Protecting Staff, Patients and Visitors

The COVID-19 (SARS-CoV-2) pandemic has profoundly impacted almost every aspect of healthcare systems worldwide, placing the health and safety of frontline healthcare workers at risk, and it still continues to remain an important public health challenge. Several hospitals have put in place strategies to manage space, staff, and supplies in order to continue to deliver optimum care to patients while at the same time protecting the health and safety of staff and patients. However, the emergence of the second and third waves of the virus with the influx of new cases continue to add an additional level of complexity to the already challenging situation of containing the spread and lowering the rate of transmission, thus pushing healthcare systems to the limit. In this narrative review paper, we describe various strategies including administrative controls, environmental controls, and use of personal protective equipment, implemented by occupational health and safety departments for the protection of healthcare workers, patients, and visitors from SARS-CoV-2 virus infection. The protection and safeguard of the health and safety of healthcare workers and patients through the implementation of effective infection control measures, adequate management of possible outbreaks and minimization of the risk of nosocomial transmission is an important and effective strategy of SARS-CoV-2 pandemic management in any healthcare facility. High quality patient care hinges on ensuring that the care providers are well protected and supported so they can provide the best quality of care to their patients.

Psychosocial determinants of healthcare personnel's willingness to carry real-time locating system tags during daily inpatient care in hospital managing COVID-19 patients: insights from a mixed-methods analysis

Objective

Real-time locating systems (RTLS) enable contact tracing and hand hygiene reminders, to improve hospital safety. Successful implementation requires healthcare personnel (HCP) to carry RTLS tags continuously. We assessed for determinants of HCP’s willingness to use RTLS tags during routine inpatient care, and evaluated concerns using mixed-methods analysis.

Materials and Methods

We conducted a cross-sectional study in the 330-bed purpose-built National Centre for Infectious Diseases in Singapore, from January 15 through February 4, 2020. The anonymous survey comprised 24 questions based on constructs from behavioral models and an open-ended question. Principal component analysis was performed to derive the latent factor structure applied in the multivariable logistic regression analysis. Concerns were analyzed using thematic analysis.

Results

Of 260 HCP (nurses [40.8%], ancillary and administrative staff [23.1%], allied health professionals [18.5%], and physicians [17.7%]), 75% were willing to use the RTLS tag. After adjusting for age, gender, healthcare professional group, and duration of practice, the acceptance of the use of the RTLS tag (adjusted OR 11.28 [95% CI 4.39–29.00], P < .001) was highly associated with the willingness to use the RTLS tag. HCP who perceived the tag to be easy to use (adjusted OR 2.80 [95% CI 1.37–5.72], P = .005), were also more willing to use the tag. HCP were willing to carry the RTLS tag for the purpose of contact tracing despite privacy concerns.

Conclusion

More communications on the intentions and data protection standards of the RTLS, and accessory enhancements for HCP’s convenient and sustained use of the RTLS tag are crucial, to optimize RTLS’s usefulness during the COVID-19 pandemic.

One Year on: An Overview of Singapore's Response to COVID-19-What We Did, How We Fared, How We Can Move Forward

Background—One year has passed since the first COVID-19 case in Singapore. This scoping review commemorates Singaporean researchers that have expanded the knowledge on this novel virus. We aim to provide an overview of healthcare-related articles published in peer-reviewed journals, authored by the Singapore research community about COVID-19 during the first year of the pandemic. Methods—This was reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) protocol. It included healthcare-related articles about COVID-19 published between 23 January 2020 and 22 January 2021 with a Singapore-affiliated author. MEDLINE, Embase, Scopus, Web of Science, CINAHL, PsycINFO, Google Scholar, and local journals were searched. The articles were screened independently by two reviewers. Results—The review included 504 articles. Most of the articles narrated the changes to hospital practice (210), while articles on COVID-19 pathology (94) formed most of the non-narrative papers. Publications on public health (61) and the indirect impacts to clinical outcomes (45) were other major themes explored by the research community. The remaining articles detailed the psychological impact of the pandemic (35), adaptations of medical education (30), and narratives of events (14). Conclusion—Amidst a resurgence of community cases involving variant COVID-19 strains, the resources from the research community will provide valuable guidance to navigate these uncertain times.

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

BACKGROUND

Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention.

METHODS

We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction.

RESULTS

ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested.

CONCLUSIONS

ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.

Evaluation of silver nanoparticles for the prevention of SARS-CoV-2 infection in health workers: In vitro and in vivo

SARS-CoV-2 infection in hospital areas is of a particular concern, since the close interaction between health care personnel and patients diagnosed with COVID-19, which allows virus to be easily spread between them and subsequently to their families and communities. Preventing SARS-CoV-2 infection among healthcare personnel is essential to reduce the frequency of infections and outbreaks during the pandemic considering that they work in high-risk areas. In this research, silver nanoparticles (AgNPs) were tested in vitro and shown to have an inhibitory effect on SARS-CoV-2 infection in cultured cells. Subsequently, we assess the effects of mouthwash and nose rinse with ARGOVIT® silver nanoparticles (AgNPs), in the prevention of SARS-CoV-2 contagion in health workers consider as high-risk group of acquiring the infection in the General Tijuana Hospital, Mexico, a hospital for the exclusive recruitment of patients diagnosed with COVID-19. We present a prospective randomized study of 231 participants that was carried out for 9 weeks (during the declaration of a pandemic). The "experimental" group was instructed to do mouthwash and nose rinse with the AgNPs solution; the "control" group was instructed to do mouthwashes and nose rinse in a conventional way. The incidence of SARS-CoV-2 infection was significantly lower in the "experimental" group (two participants of 114, 1.8%) compared to the "control" group (thirty-three participants of 117, 28.2%), with an 84.8% efficiency. We conclude that the mouth and nasal rinse with AgNPs helps in the prevention of SARS-CoV-2 infection in health personnel who are exposed to patients diagnosed with COVID-19.

The Clinical Information Systems Response to the COVID-19 Pandemic

OBJECTIVE

The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.

METHODS

PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.

RESULTS

CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.

CONCLUSION

Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

Using a Real-Time Locating System to Evaluate the Impact of Telemedicine in an Emergency Department During COVID-19: Observational Study

BACKGROUND

Telemedicine has been deployed by health care systems in response to the COVID-19 pandemic to enable health care workers to provide remote care for both outpatients and inpatients. Although it is reasonable to suspect telemedicine visits limit unnecessary personal contact and thus decrease the risk of infection transmission, the impact of the use of such technology on clinician workflows in the emergency department is unknown.

OBJECTIVE

This study aimed to use a real-time locating system (RTLS) to evaluate the impact of a new telemedicine platform, which permitted clinicians located outside patient rooms to interact with patients who were under isolation precautions in the emergency department, on in-person interaction between health care workers and patients.

METHODS

A pre-post analysis was conducted using a badge-based RTLS platform to collect movement data including entrances and duration of stay within patient rooms of the emergency department for nursing and physician staff. Movement data was captured between March 2, 2020, the date of the first patient screened for COVID-19 in the emergency department, and April 20, 2020. A new telemedicine platform was deployed on March 29, 2020. The number of entrances and duration of in-person interactions per patient encounter, adjusted for patient length of stay, were obtained for pre- and postimplementation phases and compared with t tests to determine statistical significance.

RESULTS

There were 15,741 RTLS events linked to 2662 encounters for patients screened for COVID-19. There was no significant change in the number of in-person interactions between the pre- and postimplementation phases for both nurses (5.7 vs 7.0 entrances per patient, P=.07) and physicians (1.3 vs 1.5 entrances per patient, P=.12). Total duration of in-person interactions did not change (56.4 vs 55.2 minutes per patient, P=.74) despite significant increases in telemedicine videoconference frequency (0.6 vs 1.3 videoconferences per patient, P<.001 for change in daily average) and duration (4.3 vs 12.3 minutes per patient, P<.001 for change in daily average).

CONCLUSIONS

Telemedicine was rapidly adopted with the intent of minimizing pathogen exposure to health care workers during the COVID-19 pandemic, yet RTLS movement data did not reveal significant changes for in-person interactions between staff and patients under investigation for COVID-19 infection. Additional research is needed to better understand how telemedicine technology may be better incorporated into emergency departments to improve workflows for frontline health care clinicians.

State of the Art in Adoption of Contact Tracing Apps and Recommendations Regarding Privacy Protection and Public Health: Systematic Review

BACKGROUND

During the COVID-19 pandemic, contact tracing apps have received a lot of public attention. The ongoing debate highlights the challenges of the adoption of data-driven innovation. We reflect on how to ensure an appropriate level of protection of individual data and how to maximize public health benefits that can be derived from the collected data.

OBJECTIVE

The aim of the study was to analyze available COVID-19 contact tracing apps and verify to what extent public health interests and data privacy standards can be fulfilled simultaneously in the process of the adoption of digital health technologies.

METHODS

A systematic review of PubMed and MEDLINE databases, as well as grey literature, was performed to identify available contact tracing apps. Two checklists were developed to evaluate (1) the apps' compliance with data privacy standards and (2) their fulfillment of public health interests. Based on both checklists, a scorecard with a selected set of minimum requirements was created with the goal of estimating whether the balance between the objective of data privacy and public health interests can be achieved in order to ensure the broad adoption of digital technologies.

RESULTS

Overall, 21 contact tracing apps were reviewed. In total, 11 criteria were defined to assess the usefulness of each digital technology for public health interests. The most frequently installed features related to contact alerting and governmental accountability. The least frequently installed feature was the availability of a system of medical or organizational support. Only 1 app out of 21 (5%) provided a threshold for the population coverage needed for the digital solution to be effective. In total, 12 criteria were used to assess the compliance of contact tracing apps with data privacy regulations. Explicit user consent, voluntary use, and anonymization techniques were among the most frequently fulfilled criteria. The least often implemented criteria were provisions of information about personal data breaches and data gathered from children. The balance between standards of data protection and public health benefits was achieved best by the COVIDSafe app and worst by the Alipay Health Code app.

CONCLUSIONS

Contact tracing apps with high levels of compliance with standards of data privacy tend to fulfill public health interests to a limited extent. Simultaneously, digital technologies with a lower level of data privacy protection allow for the collection of more data. Overall, this review shows that a consistent number of apps appear to comply with standards of data privacy, while their usefulness from a public health perspective can still be maximized.

Utilizing the electronic health records to create a syndromic staff surveillance system during the COVID-19 outbreak

Objectives

Since December 2019, COVID-19 has caused a worldwide pandemic and Singapore has seen escalating cases with community spread. Aggressive contact tracing and identification of suspects has helped to identify local community clusters, surveillance being the key to early intervention. Healthcare workers (HCWs) have contracted COVID-19 infection both at the workplace and community. We aimed to create a prototype staff surveillance system for the detection of acute respiratory infection (ARI) clusters amongst our HCWs and describe its effectiveness.

Methods

A prototypical surveillance system was built on existing electronic health record infrastructure.

Results

Over a 10-week period, we investigated 10 ARI clusters amongst 7 departments. One of the ARI clusters was later determined to be related to COVID-19 infection. We demonstrate the feasibility of syndromic surveillance to detect ARI clusters during the COVID-19 outbreak.

Conclusion

The use of syndromic surveillance to detect ARI clusters amongst HCWs in the COVID-19 pandemic may enable early case detection and prevent onward transmission. It could be an important tool in infection prevention within healthcare institutions.

How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

To combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.

Accounting for health inequities in the design of contact tracing interventions: A rapid review

Background

Contact tracing has been a central control measure for coronavirus disease 2019 (COVID-19) transmission. However, without consideration of the needs of specific populations, public health interventions can exacerbate health inequities.

Aim

The purpose of this rapid review was to determine if and how health inequities were included in the design of contact tracing interventions in epidemic settings.

Methods

A search of the electronic databases MEDLINE and Web of Science was conducted. The following inclusion criteria were applied for article selection: (1) described the design of contact tracing interventions, (2) published between 2013 and 2020 in English, French, Spanish, Chinese, or Portuguese, (3) and included at least 50% of empiricism, according to the Automated Classifier of Texts on Scientific Studies (ATCER) tool. Various tools were used to extract data.

Results

Following screening of the titles and abstracts of 230 articles, 39 met the inclusion criteria. Only seven references were retained after full text review. None of the selected studies considered health inequities in the design of contact tracing interventions.

Conclusions

The use of tools/concepts for incorporating health inequities, such as the REFLEX-ISS tool, and ‘proportionate universalism’ when designing contact tracing interventions, would enable practitioners, decision-makers, and researchers to better consider health inequities.

Facility-Based Surveillance Activities for COVID-19 Infection and Outcomes among Healthcare Workers in a Nigerian Tertiary Hospital

COVID-19 in healthcare workers (HCWs) can result in nosocomial transmission, depletion in available workforce, and enhanced community transmission. This article describes surveillance for COVID-19 in HCWs at a tertiary healthcare facility, and documents the outcomes. A descriptive cross-sectional study of all HCWs identified from surveillance for COVID-19 from March 31 to August 31, 2020 was conducted. Healthcare workers were categorized as high risk and low risk using an adapted WHO Risk Assessment tool. Nasopharyngeal and oropharyngeal swab specimens obtained from high-risk subjects were tested by a reverse transcriptase PCR method. Data were analyzed with IBM SPSS version 25.0 software (IBM SPSS Statistics for Windows, Version 25.0, Armonk, NY), and results were presented as frequencies and percentages. The level of significance was set at P < 0.05. During 5 months of surveillance, 1,466 HCWs with a mean age of 38.1 ± 9.7 years were identified as contacts. On risk assessment, 328 (22.4%) were adjudged high risk. High risk was associated with increasing age (P < 0.001), male gender (P = 0.001), and nonclinical staff (P = 0.002). Following testing, 78 (5.3%) in the high-risk category were confirmed to have COVID-19. There was no record of COVID-19 in HCWs adjudged low risk. Forty-four (56.4%) cases were epidemiologically linked to the community, 20 (25.7%) to patients, and 14 (17.9%) to another HCW. Surveillance and risk assessment are crucial to COVID-19 response in healthcare facilities and revealed HCW infections with predominantly nonoccupational epidemiological links in this study.

Risk of SARS-CoV-2 Transmission Among Coworkers in a Surgical Environment

Health care workers are at high risk for contracting coronavirus disease 2019. However, little is known about the risk of transmission between coworkers. The objective of this study was to determine the risk of transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between coworkers in a surgical environment. This was an observational study of 394 health care workers in a surgical environment who were exposed to 2 known SARS-CoV-2-positive coworkers. Standard infection precautions were in place at the time of the exposure. All 394 exposed workers initially underwent nasopharyngeal swab testing for SARS-CoV-2 using the polymerase chain reaction technique. Of the original group, 387 were tested again with the same technique 1 week later. Of 394 SARS-CoV-2-exposed health care workers initially tested, 1 was positive. No new positive cases were found on repeated testing of 387 participants 1 week later. The risk of transmission of SARS-CoV-2 in a health care unit with universal masking and appropriate hand hygiene is low. This finding should provide some reassurance to surgical practices as they reopen.

The Effect of Traceability System and Managerial Initiative on Indonesian Food Cold Chain Performance: A Covid-19 Pandemic Perspective

This study aims to determine the effect of managerial initiatives on the adoption of traceability systems on food cold chain performance during the Covid-19 pandemic. Managerial initiatives are allegedly needed to improve the company's performance because it improves the traceability system in the supply chain. In addition, the effect of the traceability system adoption on the Indonesian food cold-chain performance during the Covid-19 pandemic is also discussed in this study. This study uses a quantitative approach and purposive sampling with a questionnaire research instrument obtained 250 statements of Indonesian consumers and retail employees. Partial least squares for structural equation modeling (PLS-SEM) were used to analyze latent variables' relationships. This study indicates that the traceability system has a significant effect on the performance of the food cold-chain during the Covid-19 pandemic. In addition, the adoption of electronic data exchange (EDI), radio frequency identification (RFID), and blockchain significantly impacted traceability systems during the Covid-19 pandemic. The managerial application of the initiative showed a positive and significant impact on the performance of the food cold-chain during the Covid-19 pandemic. However, the managerial initiative is not able to moderate the adoption of the traceability system.

Real-Time Locating System in Production Management

Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can enrich the available information, these systems started to gain attention in industrial environments in recent years. This paper provides a review of the possible technologies and applications related to production control and logistics, quality management, safety, and efficiency monitoring. This work also provides a workflow to clarify the steps of a typical real-time locating system project, including the cleaning, pre-processing, and analysis of the data to provide a guideline and reference for research and development of indoor positioning-based manufacturing solutions.

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study

BACKGROUND

Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app-TraceTogether-in March 2020 to augment Singapore's contact tracing capabilities.

OBJECTIVE

This study aims to compare the performance of the contact tracing app-TraceTogether-with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening.

METHODS

All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians' 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period.

RESULTS

RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them.

CONCLUSIONS

TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.

Universal Shelter-in-Place Versus Advanced Automated Contact Tracing and Targeted Isolation: A Case for 21st-Century Technologies for SARS-CoV-2 and Future Pandemics

OBJECTIVE

To model and compare effect of digital contact tracing versus shelter-in-place on severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) spread.

METHODS

Using a classical epidemiologic framework and parameters estimated from literature published between February 1, 2020, and May 25, 2020, we modeled two non-pharmacologic interventions - shelter-in-place and digital contact tracing - to curb spread of SARS-CoV-2. For contact tracing, we assumed an advanced automated contact tracing (AACT) application that sends alerts to individuals advising self-isolation based on individual exposure profile. Model parameters included percentage population ordered to shelter-in-place, adoption rate of AACT, and percentage individuals who appropriately follow recommendations. Under influence of these variables, the number of individuals infected, exposed, and isolated were estimated.

RESULTS

Without any intervention, a high rate of infection (>10 million) with early peak is predicted. Shelter-in-place results in rapid decline in infection rate at the expense of impacting a large population segment. The AACT model achieves reduction in infected and exposed individuals similar to shelter-in-place without impacting a large number of individuals. For example, a 50% AACT adoption rate mimics a shelter-in-place order for 40% of the population and results in a greater than 90% decrease in peak number of infections. However, as compared to shelter-in-place, with AACT significantly fewer individuals would be isolated.

CONCLUSION

Wide adoption of digital contact tracing can mitigate infection spread similar to universal shelter-in-place, but with considerably fewer individuals isolated.

Rapid Deployment of a Free, Privacy-Assured COVID-19 Symptom Tracker for Public Safety During Reopening: System Development and Feasibility Study

BACKGROUND

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of cases of coronavirus disease (COVID-19) in the United States has exponentially increased. Identifying and monitoring individuals with COVID-19 and individuals who have been exposed to the disease is critical to prevent transmission. Traditional contact tracing mechanisms are not structured on the scale needed to address this pandemic. As businesses reopen, institutions and agencies not traditionally engaged in disease prevention are being tasked with ensuring public safety. Systems to support organizations facing these new challenges are critically needed. Most currently available symptom trackers use a direct-to-consumer approach and use personal identifiers, which raises privacy concerns.

OBJECTIVE

Our aim was to develop a monitoring and reporting system for COVID-19 to support institutions conducting monitoring activities without compromising privacy.

METHODS

Our multidisciplinary team designed a symptom tracking system after consultation with experts. The system was designed in the Georgetown University AvesTerra knowledge management environment, which supports data integration and synthesis to identify actionable events and maintain privacy. We conducted a beta test for functionality among consenting Georgetown University medical students.

RESULTS

The symptom tracker system was designed based on guiding principles developed during peer consultations. Institutions are provided access to the system through an efficient onboarding process that uses clickwrap technology to document agreement to limited terms of use to rapidly enable free access. Institutions provide their constituents with a unique identifier to enter data through a web-based user interface to collect vetted symptoms as well as clinical and epidemiologic data. The website also provides individuals with educational information through links to the COVID-19 prevention recommendations from the US Centers for Disease Control and Prevention. Safety features include instructions for people with new or worsening symptoms to seek care. No personal identifiers are collected in the system. The reporter mechanism safeguards data access so that institutions can only access their own data, and it provides institutions with on-demand access to the data entered by their constituents, organized in summary reports that highlight actionable data. Development of the system began on March 15, 2020, and it was launched on March 20, 2020. In the beta test, 48 Georgetown University School of Medicine students or their social contacts entered data into the system from March 31 to April 5, 2020. One of the 48 users (2%) reported active COVID-19 infection and had no symptoms by the end of the monitoring period. No other participants reported symptoms. Only data with the unique entity identifier for our beta test were generated in our summary reports.

CONCLUSIONS

This system harnesses insights into privacy and data sharing to avoid regulatory and legal hurdles to rapid adaption by entities tasked with maintaining public safety. Our pilot study demonstrated feasibility and ease of use. Refinements based on feedback from early adapters included release of a Spanish language version. These systems provide technological advances to complement the traditional contact tracing and digital tracing applications being implemented to limit SARS-CoV-2 transmission during reopening.

Aerosol Dispersion During Mastoidectomy and Custom Mitigation Strategies for Otologic Surgery in the COVID-19 Era

Objective

To investigate small-particle aerosolization from mastoidectomy relevant to potential viral transmission and to test source-control mitigation strategies.

Study Design

Cadaveric simulation.

Setting

Surgical simulation laboratory.

Methods

An optical particle size spectrometer was used to quantify 1- to 10-µm aerosols 30 cm from mastoid cortex drilling. Two barrier drapes were evaluated: OtoTent1, a drape sheet affixed to the microscope; OtoTent2, a custom-structured drape that enclosed the surgical field with specialized ports.

Results

Mastoid drilling without a barrier drape, with or without an aerosol-scavenging second suction, generated large amounts of 1- to 10-µm particulate. Drilling under OtoTent1 generated a high density of particles when compared with baseline environmental levels (P < .001, U = 107). By contrast, when drilling was conducted under OtoTent2, mean particle density remained at baseline. Adding a second suction inside OtoTent1 or OtoTent2 kept particle density at baseline levels. Significant aerosols were released upon removal of OtoTent1 or OtoTent2 despite a 60-second pause before drape removal after drilling (P < .001, U = 0, n = 10, 12; P < .001, U = 2, n = 12, 12, respectively). However, particle density did not increase above baseline when a second suction and a pause before removal were both employed.

Conclusions

Mastoidectomy without a barrier, even when a second suction was added, generated substantial 1- to 10-µm aerosols. During drilling, large amounts of aerosols above baseline levels were detected with OtoTent1 but not OtoTent2. For both drapes, a second suction was an effective mitigation strategy during drilling. Last, the combination of a second suction and a pause before removal prevented aerosol escape during the removal of either drape.