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1.
JMIR Form Res ; 6(11): e38904, 2022 Nov 04.
Article in English | MEDLINE | ID: covidwho-2109560

ABSTRACT

BACKGROUND: The Dutch CoronaMelder (CM) app is the official Dutch contact-tracing app (CTA). It has been used to contain the spread of the SARS-CoV-2 in the Netherlands. It allows its users and those of connected apps to anonymously exchange warnings about potentially high-risk contacts with individuals infected with the SARS-CoV-2. OBJECTIVE: The goal of this mixed methods study is to understand the use of CTA in the pandemic and its integration into the Municipal Health Services (MHS) efforts of containment through contact tracing. Moreover, the study aims to investigate both the motivations and user experience-related factors concerning adherence to quarantine and isolation measures. METHODS: A topic analysis of 56 emails and a web-based survey of 1937 adults from the Netherlands, combined with a series of 48 in-depth interviews with end users of the app and 14 employees of the Dutch MHS involved in contact tracing, were conducted. Mirroring sessions were held (n=2) with representatives from the development (n=2) and communication teams (n=2) responsible for the creation and implementation of the CM app. RESULTS: Topic analysis and interviews identified procedural and technical issues in the use of the CTA. Procedural issues included the lack of training of MHS employees in the use of CTAs. Technical issues identified for the end users included the inability to send notifications without phone contact with the MHS, unwarranted notifications, and nightly notifications. Together, these issues undermined confidence in and satisfaction with the app's use. The interviews offered a deeper understanding of the various factors at play and their effects on users; for example, the mixed experiences of the app's users, the end user's own fears, and uncertainties concerning the SARS-CoV-2; problematic infrastructure at the time of the app's implementation on the side of the health services; the effects of the society-wide efforts in containment of the SARS-CoV-2 on the CM app's perception, resulting in further doubts concerning the app's effectiveness among MHS workers and citizens; and problems with adherence to behavioral measures propagated by the app because of the lack of confidence in the app and uncertainty concerning the execution of the behavioral measures. All findings were evaluated with the app's creators and have since contributed to improvements. CONCLUSIONS: Although most participants perceived the app positively, procedural and technical issues identified in this study limited satisfaction and confidence in the CM app and affected its adoption and long-term use. Moreover, these same issues negatively affected the CM app's effectiveness in improving compliance with behavioral measures aimed at reducing the spread of the SARS-CoV-2. This study offers lessons learned for future eHealth interventions in pandemics. Lessons that can aid in more effective design, implementation, and communication for more effective and readily adoptable eHealth applications.

2.
Int J Environ Res Public Health ; 19(22)2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-2110066

ABSTRACT

A number of mobile health apps related to coronavirus infectious disease 2019 (COVID-19) have been developed, but research into app content analytics for effective surveillance and management is still in its preliminary stages. The present study aimed to identify the purpose and functions of the currently available COVID-19 apps using content analysis. The secondary aim was to propose directions for the future development of apps that aid infectious disease surveillance and control with a focus on enhancing the app content and quality. Prior to conducting an app search in the App Store and the Google Play Store, we reviewed previous studies on COVID-19 apps found in Google Scholar and PubMed to examine the main purposes of the apps. Using the five selected keywords based on the review, we searched the two app stores to retrieve eligible COVID-19 apps including those already addressed in the reviewed literature. We conducted descriptive and content analyses of the selected apps. We classified the purpose types of the COVID-19 apps into the following five categories: Information provision, tracking, monitoring, mental health management, and engagement. We identified 890 apps from the review articles and the app stores: 47 apps met the selection criteria and were included in the content analysis. Among the selected apps, iOS apps outnumbered Android apps, 27 apps were government-developed, and most of the apps were created in the United States. The most common function for the iOS apps (63.6%) and Android apps (62.5%) was to provide COVID-19-related knowledge. The most common function among the tracking apps was to notify users of contact with infected people by the iOS apps (40.9%) and Android apps (37.5%). About 29.5% of the iOS apps and 25.0% of the Android apps were used to record symptoms and self-diagnose. Significantly fewer apps targeted mental health management and engagement. Six iOS apps (6/44, 13.6%) and four Android apps (4/24, 16.7%) provided behavioral guidelines about the pandemic. Two iOS apps (2/44, 4.5%) and two Android apps (2/24, 8.3%) featured communication functions. The present content analysis revealed that most of the apps provided unilateral information and contact tracing or location tracking. Several apps malfunctioned. Future research and development of COVID-19 apps or apps for other emerging infectious diseases should address the quality and functional improvements, which should begin with continuous monitoring and actions to mitigate any technical errors.


Subject(s)
COVID-19 , Communicable Diseases , Mobile Applications , Telemedicine , Humans , Pandemics/prevention & control , COVID-19/epidemiology
3.
Behaviour & Information Technology ; : 1-16, 2022.
Article in English | Academic Search Complete | ID: covidwho-2106756

ABSTRACT

This study aims to identify the critical factors influencing the user experience of contact tracing apps and the sentiments around them. For this purpose, we used Google play reviews of Aarogya Setu, a contact tracing app developed in India. First, we establish the relationship between review sentiment and review rating using regression between sentiment polarity and review rating. Then, we used a hybrid aspect-based sentiment analysis approach that uses unsupervised linguistic techniques to determine statistically significant concepts present in the review texts and cluster them into representative aspects that were then tagged under human supervision. Finally, supervised deep learning methods were applied for exhaustive extraction of the aspects and associated sentiments from the reviews. The final exercise of determining the key influencing factors was done by grouping these aspects under factors identified by marketing experts. A total of nine factors were identified, with the usefulness of the app being the most important factor. The findings of this study are essential for the development team and government to improve the application and increase adoption. [ FROM AUTHOR]

4.
eClinicalMedicine ; 55:101726, 2023.
Article in English | ScienceDirect | ID: covidwho-2104825

ABSTRACT

Summary Background Case investigation and contact tracing (CICT) is an important tool for communicable disease control, both to proactively interrupt chains of transmission and to collect information for situational awareness. We run the first randomized trial of COVID-19 CICT to investigate the utility of manual (i.e., call-based) vs. automated (i.e., survey-based) CICT for pandemic surveillance. Methods Between December 15, 2021 and February 5, 2022, a stepped wedge cluster randomized trial was run in which Santa Clara County ZIP Codes progressively transitioned from manual to automated CICT. Eleven individual-level data fields on demographics and disease dynamics were observed for non-response. The data contains 106,522 positive cases across 29 ZIP Codes. Findings Automated CICT reduced overall collected information by 29 percentage points (SE = 0.08, p < 0.01), as well as the response rate for individual fields. However, we find no evidence of differences in information loss by race or ethnicity. Interpretations Automated CICT can serve as a useful alternative to manual CICT, with no substantial evidence of skewing data along racial or ethnic lines, but manual CICT improves completeness of key data for monitoring epidemiologic patterns. Funding This research was supported in part by the Stanford Office of Community Engagement and the Stanford Institute for Human-Centered Artificial Intelligence.

5.
J Virol Methods ; 312: 114648, 2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-2105515

ABSTRACT

In 2020, the novel coronavirus, SARS-CoV-2, caused a pandemic, which is still raging at the time of writing this. Here, we present results from SpikeSeq, the first published Sanger sequencing-based method for the detection of Variants of Concern (VOC) and key mutations, using a 1 kb amplicon from the recognized ARTIC Network primers. The proposed setup relies entirely on materials and methods already in use in diagnostic RT-qPCR labs and on existing commercial infrastructure offering sequencing services. For data analysis, we provide an automated, open source, and browser-based mutation calling software (https://github.com/kblin/covid-spike-classification, https://ssi.biolib.com/covid-spike-classification). We validated the setup on 195 SARS-CoV-2 positive samples, and we were able to profile 85% of RT-qPCR positive samples, where the last 15% largely stemmed from samples with low viral count. We compared the SpikeSeq results to WGS results. SpikeSeq has been used as the primary variant identification tool on > 10.000 SARS-CoV-2 positive clinical samples during 2021. At approximately 4€ per sample in material cost, minimal hands-on time, little data handling, and a short turnaround time, the setup is simple enough to be implemented in any SARS-CoV-2 RT-qPCR diagnostic lab. Our protocol provides results that can be used to choose antibodies in a clinical setting and for the tracking and surveillance of all positive samples for new variants and known ones such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1) Delta (B.1.617.2), Omicron BA.1(B.1.1.529), BA.2, BA.4/5, BA.2.75.x, and many more, as of October 2022.

6.
Indian J Community Med ; 47(3): 420-424, 2022.
Article in English | MEDLINE | ID: covidwho-2100012

ABSTRACT

Background: Contact tracing (CT) is an effective tool for breaking the chains of transmission in infectious disease outbreaks. This study was conducted to observe the trend of isolation and quarantine, assess the source of infection and contacts, and assess the effectiveness of CT in the early detection of infection among health-care workers (HCWs). Methods: This study was conducted using secondary analysis of routine CT records of HCWs of a tertiary care hospital in Mumbai from April 9, 2020, to December 31, 2020. Details of all HCWs exposed or infected with COVID-19 were collected in a standard format developed for this purpose telephonically. The exposed HCWs were further divided into high-risk (HR)/low-risk (LR) contacts and quarantined. Results: A total of 744 HCWs were isolated during this period and 1486 contacts were quarantined against them. Majority of the HCWs affected from COVID-19 were resident doctors, interns, and nursing staff. More than 81% of the positive HCWs were symptomatic. The overall ratio between isolated HCWs and quarantined HCWs is 1:2. A total of 88 (6%) HCWs tested positive from quarantine. The test positivity rate among HR contacts was 9.01% and among LR contacts was 2.72%. Conclusions: Effective CT of positive HCWs greatly aids in the early identification of contacts and timely quarantine. Over a period of time, the number of HCWs getting isolated or quarantined is found to decrease. This is the true success of CT. This strategy can be implemented among other medical colleges and hospitals too.

7.
Int J Environ Res Public Health ; 19(21)2022 Nov 02.
Article in English | MEDLINE | ID: covidwho-2099507

ABSTRACT

The COVID-19 pandemic posed challenges to governments in terms of contact tracing. Like many other countries, Germany introduced a mobile-phone-based digital contact tracing solution ("Corona Warn App"; CWA) in June 2020. At the time of its release, however, it was hard to assess how effective such a solution would be, and a political and societal debate arose regarding its efficiency, also in light of its high costs. This study aimed to analyze the effectiveness of the CWA, considering prevented infections, hospitalizations, intensive care treatments, and deaths. In addition, its efficiency was to be assessed from a monetary point of view, and factors with a significant influence on the effectiveness and efficiency of the CWA were to be determined. Mathematical and statistical modeling was used to calculate infection cases prevented by the CWA, along with the numbers of prevented complications (hospitalizations, intensive care treatments, deaths) using publicly available CWA download numbers and incidences over time. The monetized benefits of these prevented cases were quantified and offset against the costs incurred. Sensitivity analysis was used to identify factors critically influencing these parameters. Between June 2020 and April 2022, the CWA prevented 1.41 million infections, 17,200 hospitalizations, 4600 intensive care treatments, and 7200 deaths. After offsetting costs and benefits, the CWA had a net present value of EUR 765 m in April 2022. Both the effectiveness and efficiency of the CWA are decisively and disproportionately positively influenced by the highest possible adoption rate among the population and a high rate of positive infection test results shared via the CWA.


Subject(s)
COVID-19 , Mobile Applications , Humans , Contact Tracing/methods , COVID-19/epidemiology , COVID-19/prevention & control , Pandemics/prevention & control , Socioeconomic Factors
8.
Euro Surveill ; 27(43)2022 10.
Article in English | MEDLINE | ID: covidwho-2099047

ABSTRACT

BackgroundTracking person-to-person SARS-CoV-2 transmission in the population is important to understand the epidemiology of community transmission and may contribute to the containment of SARS-CoV-2. Neither contact tracing nor genomic surveillance alone, however, are typically sufficient to achieve this objective.AimWe demonstrate the successful application of the integrated genomic surveillance (IGS) system of the German city of Düsseldorf for tracing SARS-CoV-2 transmission chains in the population as well as detecting and investigating travel-associated SARS-CoV-2 infection clusters.MethodsGenomic surveillance, phylogenetic analysis, and structured case interviews were integrated to elucidate two genetically defined clusters of SARS-CoV-2 isolates detected by IGS in Düsseldorf in July 2021.ResultsCluster 1 (n = 67 Düsseldorf cases) and Cluster 2 (n = 36) were detected in a surveillance dataset of 518 high-quality SARS-CoV-2 genomes from Düsseldorf (53% of total cases, sampled mid-June to July 2021). Cluster 1 could be traced back to a complex pattern of transmission in nightlife venues following a putative importation by a SARS-CoV-2-infected return traveller (IP) in late June; 28 SARS-CoV-2 cases could be epidemiologically directly linked to IP. Supported by viral genome data from Spain, Cluster 2 was shown to represent multiple independent introduction events of a viral strain circulating in Catalonia and other European countries, followed by diffuse community transmission in Düsseldorf.ConclusionIGS enabled high-resolution tracing of SARS-CoV-2 transmission in an internationally connected city during community transmission and provided infection chain-level evidence of the downstream propagation of travel-imported SARS-CoV-2 cases.


Subject(s)
COVID-19 , Communicable Diseases, Imported , Humans , SARS-CoV-2/genetics , Travel , Communicable Diseases, Imported/epidemiology , COVID-19/epidemiology , Phylogeny , Contact Tracing , Germany/epidemiology , Genomics
9.
J Med Internet Res ; 24(10): e40558, 2022 10 14.
Article in English | MEDLINE | ID: covidwho-2099001

ABSTRACT

BACKGROUND: Digital contact tracing (DCT) apps have been implemented as a response to the COVID-19 pandemic. Research has focused on understanding acceptance and adoption of these apps, but more work is needed to understand the factors that may contribute to their sustained use. This is key to public health because DCT apps require a high uptake rate to decrease the transmission of the virus within the general population. OBJECTIVE: This study aimed to understand changes in the use of the National Health Service Test & Trace (T&T) COVID-19 DCT app and explore how public trust in the app evolved over a 1-year period. METHODS: We conducted a longitudinal mixed methods study consisting of a digital survey in December 2020 followed by another digital survey and interview in November 2021, in which responses from 9 participants were explored in detail. Thematic analysis was used to analyze the interview transcripts. This paper focuses on the thematic analysis to unpack the reasoning behind participants' answers. RESULTS: In this paper, 5 themes generated through thematic analysis are discussed: flaws in the T&T app, usefulness and functionality affecting trust in the app, low trust in the UK government, varying degrees of trust in other stakeholders, and public consciousness and compliance dropping over time. Mistrust evolved from participants experiencing sociotechnical flaws in the app and led to concerns about the app's usefulness. Similarly, mistrust in the government was linked to perceived poor pandemic handling and the creation and procurement of the app. However, more variability in trust in other stakeholders was highlighted depending on perceived competence and intentions. For example, Big Tech companies (ie, Apple and Google), large hospitality venues, and private contractors were seen as more capable, but participants mistrust their intentions, and small hospitality venues, local councils, and the National Health Service (ie, public health system) were seen as well-intentioned but there is mistrust in their ability to handle pandemic matters. Participants reported complying, or not, with T&T and pandemic guidance to different degrees but, overall, observed a drop in compliance over time. CONCLUSIONS: These findings contribute to the wider implications of changes in DCT app use over time for public health. Findings suggest that trust in the wider T&T app ecosystem could be linked to changes in the use of the app; however, further empirical and theoretical work needs to be done to generalize the results because of the small, homogeneous sample. Initial novelty effects occurred with the app, which lessened over time as public concern and media representation of the pandemic decreased and normalization occurred. Trust in the sociotechnical capabilities of the app, stakeholders involved, and salience maintenance of the T&T app in conjunction with other measures are needed for sustained use.


Subject(s)
COVID-19 , Mobile Applications , COVID-19/prevention & control , Contact Tracing/methods , Ecosystem , Humans , Pandemics/prevention & control , State Medicine , Trust , United Kingdom
10.
Public Health Rep ; 137(2_suppl): 40S-45S, 2022.
Article in English | MEDLINE | ID: covidwho-2098161

ABSTRACT

OBJECTIVES: We evaluated 2 innovative approaches that supported COVID-19 case investigation and contact tracing (CI/CT) in Chicago communities: (1) early engagement of people diagnosed with COVID-19 by leveraging the existing Healthcare Alert Network to send automated telephone calls and text messages and (2) establishment of a network of on-site case investigators and contact tracers within partner health care facilities (HCFs) and community-based organizations (CBOs). METHODS: The Chicago Department of Public Health used Healthcare Alert Network data to calculate the proportion of people with confirmed COVID-19 who successfully received an automated telephone call or text message during December 27, 2020-April 24, 2021. The department also used CI/CT data to calculate the proportion of cases successfully interviewed and named contacts successfully notified, as well as the time to successful case interview and to successful contact notification. RESULTS: Of 67 882 people with COVID-19, 94.3% (n = 64 011) received an automated telephone call and 91.7% (n = 62 239) received a text message. Of the 65 470 COVID-19 cases pulled from CI/CT data, 24 450 (37.3%) interviews were completed, including 6212 (61.3%) of the 10 126 cases diagnosed in HCFs. The median time from testing to successful case interview was 3 days for Chicago Department of Public Health investigators and 4 days for HCF investigators. Overall, 34 083 contacts were named; 13 117 (38.5%) were successfully notified, including 9068 (36.6%) of the 24 761 contacts assigned to CBOs. The median time from contact elicitation to completed notification by CBOs was <24 hours. CONCLUSIONS: Partnerships with HCFs and CBOs helped deliver timely CI/CT during the COVID-19 pandemic, suggesting a potential benefit of engaging non-public health institutions in CI/CT for existing and emerging diseases.


Subject(s)
COVID-19 , Contact Tracing , Humans , COVID-19/epidemiology , Pandemics , Chicago/epidemiology , Public Health
11.
Public Health Rep ; 137(2_suppl): 67S-75S, 2022.
Article in English | MEDLINE | ID: covidwho-2098160

ABSTRACT

OBJECTIVES: Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic. MATERIALS AND METHODS: California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy. RESULTS: We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2. PRACTICE IMPLICATIONS: Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , SARS-CoV-2 , Disease Notification , Contact Tracing/methods , California/epidemiology
12.
Computational Collective Intelligence, Iccci 2022 ; 13501:272-282, 2022.
Article in English | Web of Science | ID: covidwho-2094415

ABSTRACT

The graph data modeling is the most convenient process to work with in order to answer almost all questions that came across our minds when it comes to covid-19 infection (when was I infected? where? and who gave it me ?). Most of us wonder when we enter a closed space if we are going to face someone who tested positive, but is there any way to predict that? Luckily yes! This could be done by streaming contact data of citizens to a server that looks for any contacts between infected people and non infected ones and sends back a notification for the purpose. The collection, management and exploitation of data in the field of health continues to require the attention of researchers in relation to confidentiality, security, privacy and protection of personal data. Research is active in this area and we are willing to use simulation technologies in order to anticipate the contamination area and predict the propagation due to lack of shared data for reasons listed above. These data flows also constitute a considerable input for the extraction of knowledge and the application of artificial intelligence algorithms and more precisely of machine learning to predict and prevent the evolution of the epidemic on the affected persons as well as its geographical spread, after correlating sensitive data securely from all users, the initial objective is to model the system in the form of a graph and apply graph flow algorithms to predict the propagation network and notify the users if contaminated.

13.
Life (Basel) ; 12(11)2022 Oct 26.
Article in English | MEDLINE | ID: covidwho-2090268

ABSTRACT

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy.

14.
Int J Environ Res Public Health ; 19(21)2022 Oct 27.
Article in English | MEDLINE | ID: covidwho-2090155

ABSTRACT

The spread of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has raised major health policy questions. Direct transmission via respiratory droplets seems to be the dominant route of its transmission. However, indirect transmission via shared contact of contaminated objects may also occur. The contribution of each transmission route to epidemic spread might change during lock-down scenarios. Here, we simulate viral spread of an abstract epidemic considering both routes of transmission by use of a stochastic, agent-based SEIR model. We show that efficient contact tracing (CT) at a high level of incidence can stabilize daily cases independently of the transmission route long before effects of herd immunity become relevant. CT efficacy depends on the fraction of cases that do not show symptoms. Combining CT with lock-down scenarios that reduce agent mobility lowers the incidence for exclusive direct transmission scenarios and can even eradicate the epidemic. However, even for small fractions of indirect transmission, such lockdowns can impede CT efficacy and increase case numbers. These counterproductive effects can be reduced by applying measures that favor distancing over reduced mobility. In summary, we show that the efficacy of lock-downs depends on the transmission route. Our results point to the particular importance of hygiene measures during mobility lock-downs.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Fomites , COVID-19/prevention & control , Communicable Disease Control/methods , Contact Tracing/methods
15.
JMIR Public Health Surveill ; 8(10): e40233, 2022 10 27.
Article in English | MEDLINE | ID: covidwho-2089642

ABSTRACT

BACKGROUND: In the post-COVID-19 pandemic era, many countries have launched apps to trace contacts of COVID-19 infections. Each contact-tracing app (CTA) faces a variety of issues owing to different national policies or technologies for tracing contacts. OBJECTIVE: In this study, we aimed to investigate all the CTAs used to trace contacts in various countries worldwide, including the technology used by each CTA, the availability of knowledge about the CTA from official websites, the interoperability of CTAs in various countries, and the infection detection rates and policies of the specific country that launched the CTA, and to summarize the current problems of the apps based on the information collected. METHODS: We investigated CTAs launched in all countries through Google, Google Scholar, and PubMed. We experimented with all apps that could be installed and compiled information about apps that could not be installed or used by consulting official websites and previous literature. We compared the information collected by us on CTAs with relevant previous literature to understand and analyze the data. RESULTS: After screening 166 COVID-19 apps developed in 197 countries worldwide, we selected 98 (59%) apps from 95 (48.2%) countries, of which 63 (66.3%) apps were usable. The methods of contact tracing are divided into 3 main categories: Bluetooth, geolocation, and QR codes. At the technical level, CTAs face 3 major problems. First, the distance and time for Bluetooth- and geolocation-based CTAs to record contact are generally set to 2 meters and 15 minutes; however, this distance should be lengthened, and the time should be shortened for more infectious variants. Second, Bluetooth- or geolocation-based CTAs also face the problem of lack of accuracy. For example, individuals in 2 adjacent vehicles during traffic jams may be at a distance of ≤2 meters to make the CTA trace contact, but the 2 users may actually be separated by car doors, which could prevent transmission and infection. In addition, we investigated infection detection rates in 33 countries, 16 (48.5%) of which had significantly low infection detection rates, wherein CTAs could have lacked effectiveness in reducing virus propagation. Regarding policy, CTAs in most countries can only be used in their own countries and lack interoperability among other countries. In addition, 7 countries have already discontinued CTAs, but we believe that it was too early to discontinue them. Regarding user acceptance, 28.6% (28/98) of CTAs had no official source of information that could reduce user acceptance. CONCLUSIONS: We surveyed all CTAs worldwide, identified their technological policy and acceptance issues, and provided solutions for each of the issues we identified. This study aimed to provide useful guidance and suggestions for updating the existing CTAs and the subsequent development of new CTAs.


Subject(s)
COVID-19 , Mobile Applications , Humans , Contact Tracing/methods , Pandemics/prevention & control , Policy
16.
Eur Heart J Case Rep ; 6(10): ytac404, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2087760

ABSTRACT

Background: COVID-19 has affected individuals across the globe, and those with cardiac implantable electronic devices (CIEDs) likely represent a high-risk group. These devices can be interrogated to reveal information about the patient activity, heart rate parameters, and respiratory rate. Case summary: Four patients with CIEDs and left ventricular dysfunction were admitted to a single institution for COVID-19 infection. Each patient survived hospitalization, and none required intensive care. Retrospectively, CIED interrogation revealed each patient had decreased activity level prior to their reporting COVID-19 symptoms. Similarly, respiratory rate increased before symptom onset for three of the patients, while one did not have these data available. Of the three patients with heart rate variability (HRV) available, two had decreased HRV before they developed symptoms. After hospital discharge, these parameters returned to their baseline. Discussion: This case series suggests physiologic changes identifiable through interrogation of CIEDs may occur prior to the reported onset of COVID-19 symptoms. These data may provide objective evidence on which to base more sensitive assessments of infectious risk when performing contact tracing in communities.

17.
Proceedings of the Association for Information Science and Technology ; 59(1):845-847, 2022.
Article in English | Scopus | ID: covidwho-2085201

ABSTRACT

The study explores the use of COVID-19 related apps for contact tracing deployed in New York State (NYS). The project seeks to understand potential differences in perception, adoption, or privacy concerns among racial and ethnic populations and across age groups. Using the Antecedent-Privacy Concerns-Outcomes (APCO) framework and the perceived usefulness construct, this study explores factors influencing the individual level adoption of these apps. Data collected from 120 Amazon Mechanical Turkers located in NYS was analyzed. The results indicate that race and gender are important factors to consider in expanding the Antecedent-Privacy Concerns-Outcomes (APCO) framework. Specifically, race impacted the perception of the seriousness of the pandemic, with Asians and Black being serious about the pandemic. Age played a role in privacy and security concerns. The youngest group of respondents, aged 18–24, did not have many privacy and security concerns about mobile apps. These results provided empirical results and evidence that can contribute to the expansion of the APCO model and help further the model's development. 85th Annual Meeting of the Association for Information Science & Technology ;Oct. 29 – Nov. 1, 2022 ;Pittsburgh, PA. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

18.
Inf Sci (N Y) ; 617: 103-132, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2086320

ABSTRACT

Digital contact tracing (DCT) is one of the weapons to be used against the COVID-19 pandemic, especially in a post-lockdown phase, to prevent or block foci of infection. As DCT systems can handle highly private information about people, great care must be taken to prevent misuse of the system and actions detrimental to people's privacy, up to mass surveillance. This paper presents a new centralized DCT protocol, called ZE2-P3T (Zero Ephemeral Exchanging Privacy-Preserving Proximity Protocol), which relies on smartphone localization but does not give any information about the user's location and identity to the server. Importantly, the fact that no exchange of ephemeral identities among users is required is the basis of the strong security of the protocol, which is proven to be more secure than the state-of-the-art protocol DP-3T/GAEN.

19.
J Med Virol ; : e28248, 2022 Oct 21.
Article in English | MEDLINE | ID: covidwho-2085071

ABSTRACT

With increased transmissibility and novel transmission mode, monkeypox poses new threats to public health globally in the background of the ongoing COVID-19 pandemic. Estimates of the serial interval, a key epidemiological parameter of infectious disease transmission, could provide insights into the virus transmission risks. As of October 2022, little was known about the serial interval of monkeypox due to the lack of contact tracing data. In this study, public-available contact tracing data of global monkeypox cases were collected and 21 infector-infectee transmission pairs were identified. We proposed a statistical method applied to real-world observations to estimate the serial interval of the monkeypox. We estimated a mean serial interval of 5.6 days with the right truncation and sampling bias adjusted and calculated the reproduction number of 1.33 for the early monkeypox outbreaks at a global scale. Our findings provided a preliminary understanding of the transmission potentials of the current situation of monkeypox outbreaks. We highlighted the need for continuous surveillance of monkeypox for transmission risk assessment.

20.
Non-conventional in Russian | WHOIRIS, Grey literature | ID: grc-754839

ABSTRACT

Отслеживание контактов стало краеугольным камнем ответа стран на пандемию COVID-19 и остается одной из ключевых стратегий по прерыванию цепочек распространения SARS-CoV-2 и снижению заболеваемости и смертности, связанной с COVID-19. Хотя пандемия еще не закончилась, многие страны переходят к более устойчивому и комплексному подходу к реагированию на COVID-19. Системы отслеживания контактов приспосабливаются к новым условиям;при этом, чтобы учесть произошедшие изменения в долгосрочной перспективе, адаптацию необходимо углубить. Описанное совещание по отслеживанию контактов в связи с COVID-19 совместно организовано ЕРБ ВОЗ и ECDC и проведено 1 марта 2022 г. В совещании участвовали специалисты по отслеживанию контактов в связи с COVID-19 из 39 стран и территорий Европейского региона ВОЗ, в том числе 24 стран Европейского союза/ Европейской экономической зоны (ЕЭЗ). Полный перечень стран-участниц приведен в приложении 1. Основное внимание уделялось двум ключевым темам: 1) опыт, сложности и решения, относящиеся к отслеживанию контактов в связи с COVID-19;2) как лучше интегрировать отслеживание контактов в процесс укрепления систем здравоохранения и планирование мер по обеспечению готовности к пандемиям. Для того чтобы все страны могли активно поучаствовать в обсуждении вышеуказанных тем, организовано два сеанса работы в отдельных группах.

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