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1.
Int J Environ Res Public Health ; 20(4)2023 Feb 19.
Article in English | MEDLINE | ID: covidwho-2245812

ABSTRACT

BACKGROUND: Neurological disorders are the leading cause of disability and the second leading cause of death worldwide. Teleneurology (TN) allows neurology to be applied when the doctor and patient are not present in the same place, and sometimes not at the same time. In February 2021, the Spanish Ministry of Health requested a health technology assessment report on the implementation of TN as a complement to face-to-face neurological care. METHODS: A scoping review was conducted to answer the question on the ethical, legal, social, organisational, patient (ELSI) and environmental impact of TN. The assessment of these aspects was carried out by adapting the EUnetHTA Core Model 3.0 framework, the criteria established by the Spanish Network of Health Technology Assessment Agencies and the analysis criteria of the European Validate (VALues In Doing Assessments of healthcare TEchnologies) project. Key stakeholders were invited to discuss their concerns about TN in an online meeting. Subsequently, the following electronic databases were consulted from 2016 to 10 June 2021: MEDLINE and EMBASE. RESULTS: 79 studies met the inclusion criteria. This scoping review includes 37 studies related to acceptability and equity, 15 studies developed during COVID and 1 study on environmental aspects. Overall, the reported results reaffirm the necessary complementarity of TN with the usual face-to-face care. CONCLUSIONS: This need for complementarity relates to factors such as acceptability, feasibility, risk of dehumanisation and aspects related to privacy and the confidentiality of sensitive data.


Subject(s)
COVID-19 , Physicians , Humans , Confidentiality , Privacy
2.
Math Biosci Eng ; 20(2): 1820-1840, 2023 01.
Article in English | MEDLINE | ID: covidwho-2245522

ABSTRACT

Recent works have illustrated that many facial privacy protection methods are effective in specific face recognition algorithms. However, the COVID-19 pandemic has promoted the rapid innovation of face recognition algorithms for face occlusion, especially for the face wearing a mask. It is tricky to avoid being tracked by artificial intelligence only through ordinary props because many facial feature extractors can determine the ID only through a tiny local feature. Therefore, the ubiquitous high-precision camera makes privacy protection worrying. In this paper, we establish an attack method directed against liveness detection. A mask printed with a textured pattern is proposed, which can resist the face extractor optimized for face occlusion. We focus on studying the attack efficiency in adversarial patches mapping from two-dimensional to three-dimensional space. Specifically, we investigate a projection network for the mask structure. It can convert the patches to fit perfectly on the mask. Even if it is deformed, rotated and the lighting changes, it will reduce the recognition ability of the face extractor. The experimental results show that the proposed method can integrate multiple types of face recognition algorithms without significantly reducing the training performance. If we combine it with the static protection method, people can prevent face data from being collected.


Subject(s)
Artificial Intelligence , COVID-19 , Humans , Pandemics , Privacy , Pattern Recognition, Automated/methods , Algorithms
3.
Sci Data ; 9(1): 776, 2022 12 21.
Article in English | MEDLINE | ID: covidwho-2185972

ABSTRACT

Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-off: the statistical validity of an open medical dataset based on the German National Pandemic Cohort Network (NAPKON), which was prepared for publication using a strong anonymization procedure. Descriptive statistics and results of regression analyses were compared before and after anonymization of multiple variants of the original dataset. Despite significant differences in value distributions, the statistical bias was found to be small in all cases. In the regression analyses, the median absolute deviations of the estimated adjusted odds ratios for different sample sizes ranged from 0.01 [minimum = 0, maximum = 0.58] to 0.52 [minimum = 0.25, maximum = 0.91]. Disproportionate impact on the statistical properties of data is a common argument against the use of anonymization. Our analysis demonstrates that anonymization can actually preserve validity of statistical results in relatively low-dimensional data.


Subject(s)
COVID-19 , Humans , Bias , Data Anonymization , Models, Theoretical , Privacy , Data Interpretation, Statistical , Datasets as Topic
4.
Sci Rep ; 12(1): 21254, 2022 12 08.
Article in English | MEDLINE | ID: covidwho-2151099

ABSTRACT

The mobility data of citizens provide important information on the epidemic spread including Covid-19. However, the privacy versus security dilemma hinders the utilization of such data. This paper proposed a method to generate pseudo mobility data on a per-agent basis, utilizing the actual geographical environment data provided by LBS to generate the agent-specific mobility trajectories and export them as GPS-like data. Demographic characteristics such as behavior patterns, gender, age, vaccination, and mask-wearing status are also assigned to the agents. A web-based data generator was implemented, enabling users to make detailed settings to meet different research needs. The simulated data indicated the usability of the proposed methods.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Privacy
5.
J Am Med Inform Assoc ; 29(12): 2050-2056, 2022 11 14.
Article in English | MEDLINE | ID: covidwho-2062922

ABSTRACT

OBJECTIVE: Digital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State's experience utilizing these constructs to design data-driven evaluation approaches. METHODS: We conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs. RESULTS: We identified 8 useful performance indicators within reach, engagement, and effectiveness constructs. DISCUSSION: We use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure. CONCLUSION: Continued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies.


Subject(s)
COVID-19 , Humans , Privacy , Public Health , Disease Notification , Washington , Pandemics/prevention & control , Contact Tracing/methods
6.
Telemed J E Health ; 28(10): 1440-1448, 2022 10.
Article in English | MEDLINE | ID: covidwho-2062840

ABSTRACT

Introduction: Privacy concerns are a major barrier to online technology adoption. However, when consumers are facing personal risks (being ill) and environmental risks (pandemic), the effect of privacy concerns on continued use intention of telemedicine is unknown. The large user pool of virtual visits during COVID-19 provides a great opportunity to understand consumers' privacy concerns when facing personal and environmental risks. Objective: This research investigates how patients weigh personal risks (e.g., illness) and environmental risks (e.g., pandemic) against privacy concerns when deciding whether to utilize telemedicine as an option for being treated for an acute illness. Methods: Respondents (1,059 qualified) meeting the following criteria: ≥18 years old, U.S. residents, virtual patient for acute conditions during COVID-19, and a Human Intelligence Task approval rate of >95%, were recruited utilizing Amazon Mechanical Turk during the middle of the pandemic. An online survey was conducted to collect data. Results: Analysis indicates that first-time telepatients (82% of respondents) have greater privacy concerns than repeat users. Findings also indicate that patients who are female and have some college education or less reported greater privacy concerns. Interestingly, privacy concerns are positively related to continued use intention. This result holds when satisfaction and user characteristics are controlled. Conclusions: When consumers are ill, privacy concerns still play an important role in telemedicine adoption. However, under environmental risks such as the COVID-19 pandemic, privacy concerns do not negatively impact their continued use intention, and satisfaction is positively associated with continued use intention.


Subject(s)
COVID-19 , Telemedicine , Adolescent , COVID-19/epidemiology , Female , Humans , Intention , Male , Pandemics , Privacy
7.
Nat Med ; 28(9): 1773-1784, 2022 09.
Article in English | MEDLINE | ID: covidwho-2042327

ABSTRACT

The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.


Subject(s)
Artificial Intelligence , Pandemics , Electronic Health Records , Humans , Privacy
8.
J R Soc Interface ; 19(194): 20220369, 2022 09.
Article in English | MEDLINE | ID: covidwho-2037613

ABSTRACT

As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual's ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.


Subject(s)
COVID-19 , Contact Tracing , COVID-19/epidemiology , COVID-19/prevention & control , Contact Tracing/methods , Humans , Pandemics/prevention & control , Privacy
9.
Med Princ Pract ; 31(5): 424-432, 2022.
Article in English | MEDLINE | ID: covidwho-2020586

ABSTRACT

OBJECTIVE: The novel coronavirus 2019 (COVID-19) pandemic has triggered public anxiety around the world. So far, the evidence suggests that prevention on a public scale is the most effective health measure for thwarting the progress of COVID-19. Another critical aspect of preventing COVID-19 is contact tracing. We aimed to investigate the effectiveness of digital contact tracing applications currently available in the context of the COVID-19 pandemic. METHODS: We undertook a systematic review and narrative synthesis of all literature relating to digital contact tracing applications in the context of COVID-19. We searched 3 major scientific databases. Only articles that were published in English and were available as full-text articles were selected for review. Data were extracted and narrative syntheses conducted. RESULTS: Five studies relating to COVID-19 were included in the review. Our results suggest that digitalized contact tracing methods can be beneficial for impeding the progress of COVID-19. Three key themes were generated from this systematic review. First, the critical mass of adoption of applications must be attained at the population level before the sensitivity and positive predictive value of the solution can be increased. Second, usability factors such as access, ease of use, and the elimination of barriers are essential in driving this uptake. Third, privacy must be ensured where possible as it is the single most significant barrier against achieving critical mass. CONCLUSION: Contact tracing methods have proved to be beneficial for impeding the progress of COVID-19 as compared to older, more labour-intensive manual methods.


Subject(s)
COVID-19 , Contact Tracing , Humans , Contact Tracing/methods , Pandemics/prevention & control , SARS-CoV-2 , Privacy
10.
Comput Math Methods Med ; 2022: 7078764, 2022.
Article in English | MEDLINE | ID: covidwho-2020524

ABSTRACT

Due to the high transmission rate and high pathogenicity of the novel coronavirus (COVID-19), there is an urgent need for the diagnosis and treatment of outbreaks around the world. In order to diagnose quickly and accurately, an auxiliary diagnosis method is proposed for COVID-19 based on federated learning and blockchain, which can quickly and effectively enable collaborative model training among multiple medical institutions. It is beneficial to address data sharing difficulties and issues of privacy and security. This research mainly includes the following sectors: in order to address insufficient medical data and the data silos, this paper applies federated learning to COVID-19's medical diagnosis to achieve the transformation and refinement of big data values. With regard to third-party dependence, blockchain technology is introduced to protect sensitive information and safeguard the data rights of medical institutions. To ensure the model's validity and applicability, this paper simulates realistic situations based on a real COVID-19 dataset and analyses problems such as model iteration delays. Experimental results demonstrate that this method achieves a multiparty participation in training and a better data protection and would help medical personnel diagnose coronavirus disease more effectively.


Subject(s)
Blockchain , COVID-19 , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Humans , Learning , Privacy , SARS-CoV-2
11.
Front Public Health ; 10: 847184, 2022.
Article in English | MEDLINE | ID: covidwho-1963585

ABSTRACT

COVID-19 contact-tracing applications (CTAs) offer enormous potential to mitigate the surge of positive coronavirus cases, thus helping stakeholders to monitor high-risk areas. The Kingdom of Saudi Arabia (KSA) is among the countries that have developed a CTA known as the Tawakkalna application, to manage the spread of COVID-19. Thus, this study aimed to examine and predict the factors affecting the adoption of Tawakkalna CTA. An integrated model which comprises the technology acceptance model (TAM), privacy calculus theory (PCT), and task-technology fit (TTF) model was hypothesized. The model is used to understand better behavioral intention toward using the Tawakkalna mobile CTA. This study performed structural equation modeling (SEM) analysis as well as artificial neural network (ANN) analysis to validate the model, using survey data from 309 users of CTAs in the Kingdom of Saudi Arabia. The findings revealed that perceived ease of use and usefulness has positively and significantly impacted the behavioral intention of Tawakkalna mobile CTA. Similarly, task features and mobility positively and significantly influence task-technology fit, and significantly affect the behavioral intention of the CTA. However, the privacy risk, social concerns, and perceived benefits of social interaction are not significant factors. The findings provide adequate knowledge of the relative impact of key predictors of the behavioral intention of the Tawakkalna contact-tracing app.


Subject(s)
COVID-19 , Mobile Applications , Contact Tracing , Humans , Privacy , Surveys and Questionnaires
12.
PLoS One ; 17(6): e0269097, 2022.
Article in English | MEDLINE | ID: covidwho-1963000

ABSTRACT

BACKGROUND: One common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population. OBJECTIVES: Develop an accurate risk estimator for the sample-to-population attack. METHODS: A type of estimator based on creating a synthetic variant of a population dataset was developed to estimate the re-identification risk for an adversary performing a sample-to-population attack. The accuracy of the estimator was evaluated through a simulation on four different datasets in terms of estimation error. Two estimators were considered, a Gaussian copula and a d-vine copula. They were compared against three other estimators proposed in the literature. RESULTS: Taking the average of the two copula estimates consistently had a median error below 0.05 across all sampling fractions and true risk values. This was significantly more accurate than existing methods. A sensitivity analysis of the estimator accuracy based on variation in input parameter accuracy provides further application guidance. The estimator was then used to assess re-identification risk and de-identify a large Ontario COVID-19 behavioral survey dataset. CONCLUSIONS: The average of two copula estimators consistently provides the most accurate re-identification risk estimate and can serve as a good basis for managing privacy risks when data are de-identified and shared.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Information Dissemination , Privacy , Probability , Risk
13.
PLoS One ; 17(7): e0270279, 2022.
Article in English | MEDLINE | ID: covidwho-1951542

ABSTRACT

OBJECTIVE: To understand which factors affect how willing people are to share their personal information to combat the Covid-19 pandemic, and compare them to factors that affect other public health behaviors. METHOD: We analyze data from three pre-registered online experiments conducted over eight months during the Covid-19 pandemic in the United States (April 3 2020 -November 25, 2020). Our primary analysis tests whether support for data sharing and intention to practice protective behavior increase in response to relationship-centered messages about prosociality, disease spread, and financial hardship. We then conduct a secondary correlational analysis to compare the demographic and attitudinal factors associated with willingness to share data, protective behavior, and intent to get vaccinated. Our sample (N = 650) is representative to socio-demographic characteristics of the U.S. population. RESULTS: We find the altruistic condition increased respondents' willingness to share data. In our correlational analysis, we find interactive effects of political ID and socio-demographic traits on likelihood to share data. In contrast, we found health behavior was most strongly associated with political ID, and intent to vaccinate was more associated with socio-demographic traits. CONCLUSIONS: Our findings suggest that some public health messaging, even when it is not about data sharing or privacy, may increase public willingness to share data. We also find the role of socio-demographic factors in moderating the effect of political party ID varies by public health behavior.


Subject(s)
COVID-19 , Privacy , COVID-19/epidemiology , COVID-19/prevention & control , Friends , Health Behavior , Humans , Pandemics/prevention & control , United States/epidemiology , Vaccination
14.
Ethics Hum Res ; 44(4): 2-13, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1929793

ABSTRACT

We assessed public perspectives of microbiome research privacy risks before and after a nationwide emergency was declared in the United States regarding the Covid-19 pandemic. From January to July of 2020, we conducted an online survey of perceived privacy risks of microbiome research among U.S. adults. Among 3,106 participants (the preemergency group), most expressed that the microbiome posed privacy risks similar to those associated with DNA (60.3%) or medical records (50.6%) and that they would prefer detailed explanations (70.2%) of risk in consent materials. Only 8.9% reported moderate to high familiarity with microbiome privacy risks. In adjusted analyses, individuals who participated in the study after the Covid-19 emergency was declared (the Covid-19 emergency group) were less likely to express that microbiome privacy risks were similar to those of DNA or medical records and more likely to report familiarity with the privacy risks of microbiomes. There was a trend toward increased concern after the Covid-19 emergency was declared (p = 0.053). Overall, the study revealed that many U.S. adults believe that microbiome privacy risks are similar to those associated with DNA or medical records, and they prefer detailed explanations in consent documents. Individuals who participated after the Covid-19 emergency was declared reported greater knowledge of microbiome privacy risks but had more concern.


Subject(s)
COVID-19 , Microbiota , Adult , Confidentiality , Humans , Pandemics , Privacy , United States
15.
Stud Health Technol Inform ; 295: 201-204, 2022 Jun 29.
Article in English | MEDLINE | ID: covidwho-1924027

ABSTRACT

The recent advancements in artificial intelligence (AI) and the Internet of Medical Things (IoMT) have opened new horizons for healthcare technology. AI models, however, rely on large data that must be shared with the centralized entity developing the model. Data sharing leads to privacy preservation and legal issues. Federated Learning (FL) enables the training of AI models on distributed data. Hence, a large amount of IoMT data can be put into use without the need for sharing the data. This paper presents the opportunities offered by FL for privacy preservation in IoMT data. With FL, the complicated dynamics and agreements for data-sharing can be avoided. Furthermore, it describes the use cases of FL in facilitating collaborative efforts to develop AI for COVID-19 diagnosis. Since handling data from multiple sites poses its challenges, the paper also highlights the critical challenges associated with FL developments for IoMT data. Addressing these challenges will lead to gaining maximum benefit from data-driven AI technologies in IoMT.


Subject(s)
COVID-19 , Internet of Things , Artificial Intelligence , COVID-19 Testing , Humans , Privacy
16.
Int J Environ Res Public Health ; 19(12)2022 06 20.
Article in English | MEDLINE | ID: covidwho-1911323

ABSTRACT

Since the early stage of the current pandemic, digital contact tracing (DCT) through mobile phone apps, called "Immuni", has been introduced to complement manual contact tracing in Italy. Until 31 December 2021, Immuni identified 44,880 COVID-19 cases, which corresponds to less than 1% of total COVID-19 cases reported in Italy in the same period (5,886,411). Overall, Immuni generated 143,956 notifications. Although the initial download of the Immuni app represented an early interest in the new tool, Immuni has had little adoption across the Italian population, and the recent increase in its download is likely to be related to the mandatory Green Pass certification for conducting most daily activities that can be obtained via the application. Therefore, Immuni failed as a support tool for the contact tracing system. Other European experiences seem to show similar limitations in the use of DTC, leaving open questions about its effectiveness, although in theory, contact tracing could allow useful means of "proximity tracking".


Subject(s)
COVID-19 , Mobile Applications , COVID-19/epidemiology , Contact Tracing , Humans , Pandemics/prevention & control , Privacy
17.
IEEE J Biomed Health Inform ; 26(8): 4187-4196, 2022 08.
Article in English | MEDLINE | ID: covidwho-1891403

ABSTRACT

Worldwide up to May 2022 there have been 515 million cases of COVID-19 infection and over 6 million deaths. The World Health Organization estimated that 115,000 healthcare workers died from COVID-19 from January 2020 to May 2021. This toll on human lives prompted this review on 5G based networking primarily on major components of healthcare delivery: diagnosis, patient monitoring, contact tracing, diagnostic imaging tests, vaccines distribution, emergency medical services, telesurgery and robot-assisted tele-ultrasound. The positive impact of 5G as core technology for COVID-19 applications enabled exchange of huge data sets in fangcang (cabin) hospitals and real-time contact tracing, while the low latency enhanced robot-assisted tele-ultrasound, and telementoring during ophthalmic surgery. In other instances, 5G provided a supportive technology for applications related to COVID-19, e.g., patient monitoring. The feasibility of 5G telesurgery was proven, albeit by a few studies on real patients, in very low samples size in most instances. The important future applications of 5G in healthcare include surveillance of elderly people, the immunosuppressed, and nano- oncology for Internet of Nano Things (IoNT). Issues remain and these require resolution before routine clinical adoption. These include infrastructure and coverage; health risks; security and privacy protection of patients' data; 5G implementation with artificial intelligence, blockchain, and IoT; validation, patient acceptance and training of end-users on these technologies.


Subject(s)
Blockchain , COVID-19 , Aged , Artificial Intelligence , Delivery of Health Care/methods , Humans , Privacy
18.
Int Wound J ; 19(4): 729-730, 2022 05.
Article in English | MEDLINE | ID: covidwho-1794653
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