Figure 11 - available via license: Creative Commons Attribution 4.0 International
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A network connectivity diagram showing person-of-interest (POI) key-ins to locations of interest, as well as key-ins by other users at those same locations within the overlap window.
Source publication
Background:
The novel coronavirus (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...
Citations
... They also contribute to the rationalization of patient management processes and enhance regulatory compliance [1]. In response to pandemics like COVID-19, DH technologies are proposed for contagion tracing [30], [31], [32], pandemic preparedness [33], and improving patient assistance and outcomes [34]. ...
... impact the adoption of services and close the gap in the Value Network [1], [18], [19], [29], [20], [22], [25], [27], [31], [32], [33], [34], [40], [43] Universities & Research Institutes conduct analysis, studies and R&D activities [20], [25], [27], [30], [33], [28], [39], [43], [44] Caregivers supporting patients healthcare activities [19], [25], [27], [39] Health Insurances implement new insurance coverage models [23], [21], [22], [25], [37] Incubators, Innovation Centres support DH business development [41], [42] Ambassadors, Opinion Leaders communicate and promote DH innovations to a wide audience [25], [42] D. Value Delivery Business in DH require the entire set of typical organisational activities of Product Lifecycle Management (PLM), namely: (i) Requirements analysis [18]; (ii) Product/Service design and development [23], [22], [30], [34], [38], [39] also in collaboration with Academia [21], healthcare organizations [37] and other final users (see TABLE I. ); (iii) Testing and validation [31] (e.g. through clinical trials); (iv) Marketing and Sales [23], [21], [38]; (v) Maintenance and Customer support [23], [37]. Other supporting activities relates to strategic and growth management [25], communication and promotion [28], [45] and Intellectual Property (IP) Protection [25], [46]. ...
... impact the adoption of services and close the gap in the Value Network [1], [18], [19], [29], [20], [22], [25], [27], [31], [32], [33], [34], [40], [43] Universities & Research Institutes conduct analysis, studies and R&D activities [20], [25], [27], [30], [33], [28], [39], [43], [44] Caregivers supporting patients healthcare activities [19], [25], [27], [39] Health Insurances implement new insurance coverage models [23], [21], [22], [25], [37] Incubators, Innovation Centres support DH business development [41], [42] Ambassadors, Opinion Leaders communicate and promote DH innovations to a wide audience [25], [42] D. Value Delivery Business in DH require the entire set of typical organisational activities of Product Lifecycle Management (PLM), namely: (i) Requirements analysis [18]; (ii) Product/Service design and development [23], [22], [30], [34], [38], [39] also in collaboration with Academia [21], healthcare organizations [37] and other final users (see TABLE I. ); (iii) Testing and validation [31] (e.g. through clinical trials); (iv) Marketing and Sales [23], [21], [38]; (v) Maintenance and Customer support [23], [37]. Other supporting activities relates to strategic and growth management [25], communication and promotion [28], [45] and Intellectual Property (IP) Protection [25], [46]. ...
Digital Health is an emerging topic that relates to the use of digital technologies in the healthcare domain. Although in recent years digital transformation in healthcare has led to an increasing application of such technologies, partly due to the recent COVID-19 pandemic, the inadequacy of current business strategies has been recognized as one of the reason for their limited wide-scale adoption. This paper tries to highlight the characteristics of current business models adopted in Digital Health through a bibliometric analysis and a systematic literature review. The study revealed several features for the main elements of a business model framework, namely Value Proposition, Value Capture, Value Network and Value Delivery.
... In the 3-item measure of the willingness to use mHealth tools, 2 items asked about participants' willingness to download a mobile app and 1 item asked about participants' willingness to use a website to track symptoms and possible exposures and get recommendations. A prior study evaluating a web browser-based app intended to be compatible across different smartphone operating systems (eg, Android vs iOS) found that many participants would have preferred a native app that would presumably require a download [41,42]. This study suggests that regardless of user preference for a browser-based versus a native app, the items collectively assessed an underlying construct of the willingness to use mHealth tools, broadly. ...
Background
There are no psychometrically validated measures of the willingness to engage in public health screening and prevention efforts, particularly mobile health (mHealth)–based tracking, that can be adapted to future crises post–COVID-19.
Objective
The psychometric properties of a novel measure of the willingness to participate in pandemic-related screening and tracking, including the willingness to use pandemic-related mHealth tools, were tested.
Methods
Data were from a cross-sectional, national probability survey deployed in 3 cross-sectional stages several weeks apart to adult residents of the United States (N=6475; stage 1 n=2190, 33.82%; stage 2 n=2238, 34.56%; and stage 3 n=2047, 31.62%) from the AmeriSpeak probability-based research panel covering approximately 97% of the US household population. Five items asked about the willingness to use mHealth tools for COVID-19–related screening and tracking and provide biological specimens for COVID-19 testing.
ResultsIn the first, exploratory sample, 3 of 5 items loaded onto 1 underlying factor, the willingness to use pandemic-related mHealth tools, based on exploratory factor analysis (EFA). A 2-factor solution, including the 3-item factor, fit the data (root mean square error of approximation [RMSEA]=0.038, comparative fit index [CFI]=1.000, standardized root mean square residual [SRMR]=0.005), and the factor loadings for the 3 items ranged from 0.849 to 0.893. In the second, validation sample, the reliability of the 3-item measure was high (Cronbach α=.90), and 1 underlying factor for the 3 items was confirmed using confirmatory factor analysis (CFA): RMSEA=0, CFI=1.000, SRMR=0 (a saturated model); factor loadings ranged from 1.000 to 0.962. The factor was independently associated with COVID-19–preventive behaviors (eg, “worn a face mask”: r=0.313, SE=0.041, P
... [1][2][3] Furthermore, universities had to deal with the very real health consequences of COVID-19 and had to learn to prevent or manage outbreaks within their own populations. [4][5][6][7] During the early phases of the COVID-19 pandemic, universities often expanded their capacities for public health work, developing efforts to conduct case investigation and contact tracing (henceforth CICT), essential public health strategies mobilized to prevent the spread of infectious diseases. Case investigation includes notifying individuals with confirmed infection, or those suspected of exposure due to direct sustained contact with a confirmed case, and then interviewing them to elicit information about recent contacts to identify others who may have been exposed to the virus. ...
... Contact tracing involves locating, notifying, and interviewing the individuals who may have been in contact with confirmed cases, and educating them on the relevant quarantine and isolation strategies to restrict additional transmission of the virus. 8 While some universities utilized digital, app-based programs for contact tracing, 4,5 COVID-19 response across many universities also involved identifying, training, and even hiring individuals to conduct CICT work in preparation for future outbreaks as students began to return to campuses for in-person activities. 6,7 Across the United States, university faculty, staff, and students were mobilized for CICT work, either as part of a volunteer brigade [9][10][11][12] or through formal employment, 13,14 as universities rushed to manage both internal outbreaks of COVID and to support local departments of health overburdened by a surging pandemic. ...
Objective: We describe and analyze case investigation and contact tracing (CICT) efforts across Ohio's public universities in response to COVID-19 to distill challenges and lessons learned and suggest future opportunities for universities to mobilize in the face of emergent public health crises. Participants: Faculty, staff, and graduate students from Ohio's fourteen public universities. Methods: In-depth, semi-structured interviews were conducted with representatives from nine of the 14 universities; representatives from the remaining five universities completed a brief questionnaire. Interviews were transcribed in their entirety and thematically analyzed. Results: Emergent themes include the significance of local relationships for implementing locally tailored solutions; the presence of discrete challenges in doing CICT work with university and local communities, and the importance of university students in pandemic response. Conclusions: There are unique challenges associated with disease control across university populations and surrounding communities, but students from diverse academic background are a potential source of assistance.
... Pandemic distance work and online teaching presented educational challenges and effects on mental health due to isolation and lack of social interaction 1,4,5 . Businesses and college campuses put mitigation methods into practice, including wearing masks, social distancing, isolation and contact tracing, and hand and surface sanitizing [6][7][8] . In addition, various surveillance methods were developed, including quantitative detection of SARS-CoV-2 in wastewater from student dorms 9 . ...
Background
The COVID-19 pandemic affected universities and institutions and caused campus shutdowns with a transition to online teaching models. To detect infections that might spread on campus, we pursued research towards detecting SARS-CoV-2 in air samples inside student dorms.
Methods
We sampled air in two large dormitories for 3.5 months and a separate isolation suite containing a student who had tested positive for COVID-19. We developed novel techniques employing four methods to collect air samples: Filter Cassettes, Button Sampler, BioSampler, and AerosolSense sampler combined with direct qRT-PCR SARS-CoV-2 analysis.
Results
For the two large dorms with the normal student population, we detected SARS-CoV-2 in 11 samples. When compared with student nasal swab qRT-PCR testing, we detected SARS-CoV-2 in air samples when a PCR positive COVID-19 student was living on the same floor of the sampling location with a detection rate of 75%. For the isolation dorm, we had a 100% SARS-CoV-2 detection rate with Aerosol Sense sampler.
Conclusion
Our data suggest air sampling may be an important SARS-CoV-2 surveillance technique, especially for buildings with congregant living settings (dorms, correctional facilities, barracks). Future building designs and public health policies should consider implementation of HVAC surveillance.
... 4 The Chinese app (Chinese health code system), South Korean app (Corona 100 m), and the UK's (NHS COVID-19) app have been proposed with similar characteristics. Research studies with different ways of contact tracing such as leveraging healthcare reports, clinical documents, and barcodes for location similarity have also been proposed [45,46]. There have been other studies that proposed the contact tracing algorithms, but face the same fate of being ineffective when it comes to scalability. ...
After affecting the world in unexpected ways, the virus has started mutating which is evident with the insurgence of its new variants. The governments, hospitals, schools, industries, and humans, in general, are looking for a potential solution in the vaccine which will eventually be available, but its timeline for eradicating the virus is yet unknown. Several researchers have encouraged and recommended the use of good practices such as physical healthcare monitoring, immunity boosting, personal hygiene, mental healthcare, and contact tracing for slowing down the spread of the virus. In this article, we propose the use of smart sensors integrated with the Internet of Medical Things to cover the spectrum of good practices in an automated manner. We present hypothetical frameworks for each of the good practice modules and propose the VIrus Resistance Framework using the Internet of Medical Things (VIRFIM) to tie all the individual modules in a unified architecture. Furthermore, we validate the realization of VIRFIM framework with two case studies related to physical activity monitoring and stress detection services. We envision that VIRFIM would be influential in assisting people with the new normal for current and future pandemics as well as instrumental in halting the economic losses, respectively. We also provide potential challenges and their probable solutions in compliance with the proposed VIRFIM.
... 1,4,5 Businesses and college campuses put mitigation methods into practice, including wearing masks, social distancing, isolation and contact tracing, and hand and surface sanitizing. [6][7][8] In addition, various surveillance methods were developed, including quantitative detection of SARS-CoV-2 in wastewater from student dorms. 9 For example, the University of Arizona 10 implemented a surveillance program that successfully contained campus outbreaks using wastewater sampling and nasopharyngeal swab sample testing. ...
Background: The COVID-19 pandemic affected universities and institutions and caused campus shutdowns with a transition to online teaching models. To detect infections that might spread on campus, we pursued research towards detecting SARS-CoV-2 in air samples inside student dorms. Methods: We sampled air in 2 large dormitories for 3.5 months and a separate isolation suite containing a student who had tested positive for COVID-19. We developed novel techniques employing 4 methods to collect air samples: Filter Cassettes, Button Sampler, BioSampler, and AerosolSense sampler combined with direct qRT-PCR SARS-CoV-2 analysis. Results: For the 2 large dorms with the normal student population, we detected SARS-CoV-2 in 11 samples. When compared with student nasal swab qRT-PCR testing, we detected SARS-CoV-2 in air samples when a PCR positive COVID-19 student was living on the same floor of the sampling location with a detection rate of 75%. For the isolation dorm, we had a 100% SARS-CoV-2 detection rate with AerosolSense sampler. Conclusions: Our data suggest air sampling may be an important SARS-CoV-2 surveillance technique, especially for buildings with congregant living settings (dorms, correctional facilities, barracks). Future building designs and public health policies should consider implementation of Heating, Ventilation, and Air Conditioning surveillance.
... We previously described an alternative digital contact tracing tool, MyCOVIDKey, that is designed to supplement existing contact tracing infrastructure [29]. Our primary motivation was to develop a tool that would be less invasive while retaining efficacy. ...
... Users are assigned a status of CLEAR or NOT CLEAR and are then provided personalized recommendations based on their risk and their location. A thorough detailing of the development, implementation, and utility of the app for contact tracing is shown elsewhere, but briefly: over the duration of the pilot study, 45 unique accounts were created, 227 self-assessments were performed, and users performed 1410 key-ins at 48 unique locations (out of a possible 71 locations) [29]. The main screens of the MyCOVIDKey web app: (A) the landing page, which presents a user's status after a valid login and allows them to access self-assessments and key-ins; B) the login and account creation pages; (C) the screens for CLEAR (left) and NOT CLEAR (right) statuses; (D) the brief COVID-19 risk assessment; and (E) the key-in feature, which enables users to scan location-specific bar codes. ...
Background:
The novel coronavirus (COVID-19) 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 lead-up 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 for COVID-19 control 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 application to assist COVID-19 contact tracing efforts during the 6-week pilot period.
Methods:
A six-week study was conducted on the Vanderbilt University campus in Nashville, TN. The population, consisting primarily of graduate students, postdoctoral researchers, and faculty in the Chemistry Department at Vanderbilt University, was asked to use the MyCOVIDKey web application during the course of the study period. Paradata was collected as users engaged with the MyCOVIDKey web application. 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:
MyCOVIDKey enrolled 45 users during the pilot period. An analysis of their enrollment suggests that initial recruiting efforts were effective, however participant recruitment and engagement efforts at the mid-point of the study were less effective. Application usage paralleled the number of users, indicating incentives were useful for recruiting new sign-ups, but did not result in users trying to artificially inflate their usage as a result of prize offers. Time-to-completion of key tasks were low, indicating that the main features of the application could be quickly used. Of the 45 users, 30 provided feedback through a post-pilot 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 improvements.
Conclusions:
The MyCOVIDKey application 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 application framework with a native application format, or changing the placement of the scanner within the app workflow. After these updates, this tool could readily be deployed and could easily be adapted for other settings across the country. The need for digital contact tracing tools is becoming increasingly apparent, particularly as case numbers continue to increase while more businesses begin to re-open.
