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

10.2196/22594 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e22594
Author(s):  
Felix Nikolaus Wirth ◽  
Marco Johns ◽  
Thierry Meurers ◽  
Fabian Prasser
Keyword(s):  
Infectious Disease ◽  
Disease Management ◽  
Data Protection ◽  
Spatial Data ◽  
Privacy Protection ◽  
Use Cases ◽  
Contact Tracing ◽  
Use Case ◽  
Protection Measures ◽  
Individual Level

Background The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches. Objective Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented. Methods We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions. Results We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases. Conclusions We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

scholarly journals Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review (Preprint)

2020 ◽  
Author(s):  
Felix Nikolaus Wirth ◽  
Marco Johns ◽  
Thierry Meurers ◽  
Fabian Prasser
Keyword(s):  
Infectious Disease ◽  
Disease Management ◽  
Data Protection ◽  
Spatial Data ◽  
Privacy Protection ◽  
Use Cases ◽  
Contact Tracing ◽  
Use Case ◽  
Protection Measures ◽  
Individual Level

BACKGROUND The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches. OBJECTIVE Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented. METHODS We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions. RESULTS We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases. CONCLUSIONS We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

scholarly journals Data Integration for Future Medicine (DIFUTURE)

2018 ◽  
Vol 57 (S 01) ◽  
pp. e57-e65 ◽  
Author(s):  
Fabian Prasser ◽  
Oliver Kohlbacher ◽  
Ulrich Mansmann ◽  
Bernhard Bauer ◽  
Klaus Kuhn
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Data Sharing ◽  
Data Protection ◽  
State Of The Art ◽  
Data Access ◽  
Use Cases ◽  
Working Environment ◽  
Security And Privacy ◽  
Use Case

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers. Objectives: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing. Governance and Policies: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change. Architectural Framework and Methodology: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing. Use Cases: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios. Discussion: Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture.

scholarly journals Innovation under pressure: Implications for data privacy during the Covid-19 pandemic

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172097668
Author(s):  
Gemma Newlands ◽  
Christoph Lutz ◽  
Aurelia Tamò-Larrieux ◽  
Eduard Fosch Villaronga ◽  
Rehana Harasgama ◽  
...  
Keyword(s):  
Data Protection ◽  
Data Privacy ◽  
Contact Tracing ◽  
Remote Work ◽  
Protection Measures ◽  
Global Pandemic ◽  
Trade Offs ◽  
Digital Surveillance ◽  
Travel Restrictions ◽  
Modern Era

The global Covid-19 pandemic has resulted in social and economic disruption unprecedented in the modern era. Many countries have introduced severe measures to contain the virus, including travel restrictions, public event bans, non-essential business closures and remote work policies. While digital technologies help governments and organizations to enforce protection measures, such as contact tracing, their rushed deployment and adoption also raises profound concerns about surveillance, privacy and data protection. This article presents two critical cases on digital surveillance technologies implemented during the Covid-19 pandemic and delineates the privacy implications thereof. We explain the contextual nature of privacy trade-offs during a pandemic and explore how regulatory and technical responses are needed to protect privacy in such circumstances. By providing a multi-disciplinary conversation on the value of privacy and data protection during a global pandemic, this article reflects on the implications digital solutions have for the future and raises the question of whether there is a way to have expedited privacy assessments that could anticipate and help mitigate adverse privacy implications these may have on society.

scholarly journals Schrems II: Will It Really Increase the Level of Privacy Protection against Mass Surveillance?

2020 ◽  
Vol 4 (2) ◽  
pp. 111-128
Author(s):  
Václav Stehlík ◽  
Lusine Vardanyan
Keyword(s):  
Data Protection ◽  
Privacy Protection ◽  
Data Transfer ◽  
Contact Tracing ◽  
Positive Trend ◽  
Main Question ◽  
The European Union ◽  
Economic Dimension ◽  
The Impact ◽  
Mass Surveillance

An important event that once again brought to the forefront issues related to mass surveillance was the judgment of the Court of Justice of the European Union (hereafter referred as CJEU) delivered on July 16, 2020 in the case of Data Protection Commissioner v. Facebook Ireland Ltd. and Maximilian Schrems (Schrems II). It can be considered as the first serious precedent in the field of surveillance, which is aimed at ensuring privacy in the field of national security. Therefore, it becomes an important issue to assess its impact on the legal framework of international transfers of personal data and on the level of privacy protection. The impact of the judgment on the level of privacy protection and mass surveillance is particularly important now that CОVID-19 contact tracing programs are being widely used. In this research we try to trace the formation of the approach to mass surveillance in the case-law of CJEU before and after the Schrems II. We also try to point out some of the difficulties that the process of cross-border data transfer will face after the Schrems II. The main question of the study is whether the approach of the CJEU developed in the Schrems II will actually increase the privacy protection against mass surveillance. We conclude that the Schrems II is an important decision with serious consequences that go beyond the direct impact on data transfer between the EU and the US. It can have controversial influence of the level of privacy protection. Together with the positive trend of formation of more harmonized global data protection standards it can create many unresolved problems in the field of international data transfer and in economic dimension.

COVID-19 Contact Tracing & Privacy Protection, We Are on the Way (Preprint)

2020 ◽  
Author(s):  
Jing Fan
Keyword(s):  
Real World ◽  
Privacy Protection ◽  
Contact Tracing ◽  
Personal Privacy ◽  
The Way

UNSTRUCTURED Smartphone-based contact tracing is proven to be effective in epidemic containment. To maintain its utilization meanwhile ensure the protection of personal privacy, different countries came up with different practices, new exploratory solutions may come into real-world practice soon as well.

scholarly journals Using Actors and Use Cases for Software Size Estimation

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 592
Author(s):  
Radek Silhavy ◽  
Petr Silhavy ◽  
Zdenka Prokopova
Keyword(s):  
Project Planning ◽  
Use Cases ◽  
New Method ◽  
Software Systems ◽  
Software Project ◽  
Use Case ◽  
Size Estimation ◽  
Software Size Estimation ◽  
Use Case Points

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method.

scholarly journals Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽  
2021 ◽  
Author(s):  
Steven Umbrello ◽  
Ibo van de Poel
Keyword(s):  
Artificial Intelligence ◽  
Contact Tracing ◽  
Use Case ◽  
Value Sensitive Design ◽  
Specific Design ◽  
Established Method ◽  
Social Good ◽  
Do No Harm ◽  
Design Requirements ◽  
Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

scholarly journals A Mathematical Model of Contact Tracing during the 2014–2016 West African Ebola Outbreak

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 608
Author(s):  
Danielle Burton ◽  
Suzanne Lenhart ◽  
Christina J. Edholm ◽  
Benjamin Levy ◽  
Michael L. Washington ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Disease Management ◽  
Ebola Virus ◽  
Virus Disease ◽  
Infected Individual ◽  
Vital Role ◽  
West African ◽  
Contact Tracing ◽  
The Novel ◽  
Using Data

The 2014–2016 West African outbreak of Ebola Virus Disease (EVD) was the largest and most deadly to date. Contact tracing, following up those who may have been infected through contact with an infected individual to prevent secondary spread, plays a vital role in controlling such outbreaks. Our aim in this work was to mechanistically represent the contact tracing process to illustrate potential areas of improvement in managing contact tracing efforts. We also explored the role contact tracing played in eventually ending the outbreak. We present a system of ordinary differential equations to model contact tracing in Sierra Leonne during the outbreak. Using data on cumulative cases and deaths, we estimate most of the parameters in our model. We include the novel features of counting the total number of people being traced and tying this directly to the number of tracers doing this work. Our work highlights the importance of incorporating changing behavior into one’s model as needed when indicated by the data and reported trends. Our results show that a larger contact tracing program would have reduced the death toll of the outbreak. Counting the total number of people being traced and including changes in behavior in our model led to better understanding of disease management.

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