1
|
Peel L, Peixoto TP, De Domenico M. Statistical inference links data and theory in network science. Nat Commun 2022; 13:6794. [PMID: 36357376 PMCID: PMC9649740 DOI: 10.1038/s41467-022-34267-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications. Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.
Collapse
|
2
|
Yamamoto T, Yamaguchi T. Human-to-Human Position Estimation System Using RSSI in Outdoor Environment. SENSORS (BASEL, SWITZERLAND) 2022; 22:7621. [PMID: 36236720 PMCID: PMC9573188 DOI: 10.3390/s22197621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/30/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
Methods to prevent collisions between people to avoid traffic accidents are receiving significant attention. To measure the position in the non-line-of-sight (NLOS) area, which cannot be directly visually recognized, position-measuring methods use wireless-communication-type GPS and propagation characteristics of radio signals, such as received signal strength indication (RSSI). However, conventional position estimation methods using RSSI require multiple receivers, which decreases the position estimation accuracy, owing to the presence of surrounding buildings. This study proposes a system to solve this challenge using a receiver and position estimation method based on RSSI MAP simulation and particle filter. Moreover, this study utilizes BLE peripheral/central functions capable of advertising as the transmitter/receiver. By using the advertising radio waves, our method provides a framework for estimating the position of unspecified transmitters. The effectiveness of the proposed system is evaluated in this study through simulations and experiments in actual environments. We obtained an error average of the distance to be 1.6 m from the simulations, which shows the precision of the proposed method. In the actual environment, the proposed method showed an error average of the distance to be 3.3 m. Furthermore, we evaluated the accuracy of the proposed method when both the transmitter and receiver are in motion, which can be considered as a moving person in the outdoor NLOS area. The result shows an error of 4.5 m. Consequently, we concluded that the accuracy was comparable when the transmitter is stationary and when it is moving. Compared with conventional path loss, the model can measure distances of 3 m to 10 m, whereas the proposed method can estimate the "position" with the same accuracy in an outdoor environment. In addition, it can be expected to be used as a collision avoidance system that confirms the presence of strangers in the NLOS area.
Collapse
Affiliation(s)
- Takashi Yamamoto
- Master’s Programs in Intelligent and Mechanical Interaction Systems, University of Tsukuba, Tsukuba 305-8573, Japan
| | - Tomoyuki Yamaguchi
- Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba 305-8573, Japan
| |
Collapse
|
3
|
Flood R, Chan SC, Chen W, Aspinall D. Checking Contact Tracing App Implementations with Bespoke Static Analysis. SN COMPUTER SCIENCE 2022; 3:496. [PMID: 36193263 PMCID: PMC9517973 DOI: 10.1007/s42979-022-01357-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 08/01/2022] [Indexed: 11/27/2022]
Abstract
In the wake of the COVID-19 pandemic, contact tracing apps have been developed based on digital contact tracing frameworks. These allow developers to build privacy-conscious apps that detect whether an infected individual is in close proximity with others. Given the urgency of the problem, these apps have been developed at an accelerated rate with a brief testing period. Such quick development may have led to mistakes in the apps’ implementations, resulting in problems with their functionality, privacy and security. To mitigate these concerns, we develop and apply a methodology for evaluating the functionality, privacy and security of Android apps using the Google/Apple Exposure Notification API. This is a three-pronged approach consisting of a manual analysis, general static analysis and a bespoke static analysis, using a tool we have developed, dubbed MonSTER. As a result, we have found that, although most apps met the basic standards outlined by Google/Apple, there are issues with the functionality of some of these apps that could impact user safety.
Collapse
Affiliation(s)
- Robert Flood
- LFCS, University of Edinburgh, Edinburgh, Scotland, UK
| | | | - Wei Chen
- LFCS, University of Edinburgh, Edinburgh, Scotland, UK
| | - David Aspinall
- LFCS, University of Edinburgh, Edinburgh, Scotland, UK
- Alan Turing Institute, London, England, UK
| |
Collapse
|
4
|
Jia N, Shu H, Wang X, Xu B, Xi Y, Xue C, Liu Y, Wang Z. Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal. SENSORS (BASEL, SWITZERLAND) 2022; 22:7345. [PMID: 36236443 PMCID: PMC9571867 DOI: 10.3390/s22197345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/19/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz-23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.
Collapse
Affiliation(s)
- Naizheng Jia
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Haoran Shu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Xinheng Wang
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Bowen Xu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Yuzhang Xi
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Can Xue
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Youming Liu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| | - Zhi Wang
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China
| |
Collapse
|
5
|
Ho SM, Liu X, Seraj MS, Dickey S. Social distance "nudge:" a context aware mHealth intervention in response to COVID pandemics. COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY 2022; 29:1-24. [PMID: 36106126 PMCID: PMC9461402 DOI: 10.1007/s10588-022-09365-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
The impact of the COVID pandemic to our society is unprecedented in our time. As coronavirus mutates, maintaining social distance remains an essential step in defending personal as well as public health. This study conceptualizes the social distance "nudge" and explores the efficacy of mHealth digital intervention, while developing and validating a choice architecture that aims to influence users' behavior in maintaining social distance for their own self-interest. End-user nudging experiments were conducted via a mobile phone app that was developed as a research artifact. The accuracy of social distance nudging was validated in both United States and Japan. Future work will consider behavioral studies to better understand the effectiveness of this digital nudging intervention.
Collapse
Affiliation(s)
- Shuyuan Mary Ho
- School of Information, Florida State University, 142 Collegiate Loop, P.O. Box 3062100, Tallahassee, FL 32306-2100 USA
| | - Xiuwen Liu
- Department of Computer Science, Florida State University, 1017 Academy Way, Tallahassee, FL 32304 USA
| | - Md Shamim Seraj
- Department of Computer Science, Florida State University, 1017 Academy Way, Tallahassee, FL 32304 USA
| | - Sabrina Dickey
- College of Nursing, Florida State University, 98 Varsity Way, Tallahassee, FL 32306-4310 USA
| |
Collapse
|
6
|
Wang Z, Xiong H, Tang M, Boukhechba M, Flickinger TE, Barnes LE. Mobile Sensing in the COVID-19 Era: A Review. HEALTH DATA SCIENCE 2022; 2022:9830476. [PMID: 36408201 PMCID: PMC9629686 DOI: 10.34133/2022/9830476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/18/2022] [Indexed: 12/03/2022]
Abstract
Background During the COVID-19 pandemic, mobile sensing and data analytics techniques have demonstrated their capabilities in monitoring the trajectories of the pandemic, by collecting behavioral, physiological, and mobility data on individual, neighborhood, city, and national scales. Notably, mobile sensing has become a promising way to detect individuals' infectious status, track the change in long-term health, trace the epidemics in communities, and monitor the evolution of viruses and subspecies. Methods We followed the PRISMA practice and reviewed 60 eligible papers on mobile sensing for monitoring COVID-19. We proposed a taxonomy system to summarize literature by the time duration and population scale under mobile sensing studies. Results We found that existing literature can be naturally grouped in four clusters, including remote detection, long-term tracking, contact tracing, and epidemiological study. We summarized each group and analyzed representative works with regard to the system design, health outcomes, and limitations on techniques and societal factors. We further discussed the implications and future directions of mobile sensing in communicable diseases from the perspectives of technology and applications. Conclusion Mobile sensing techniques are effective, efficient, and flexible to surveil COVID-19 in scales of time and populations. In the post-COVID era, technical and societal issues in mobile sensing are expected to be addressed to improve healthcare and social outcomes.
Collapse
Affiliation(s)
- Zhiyuan Wang
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
| | - Haoyi Xiong
- Big Data Lab, Baidu Research, Baidu Inc., BeijingChina
| | - Mingyue Tang
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
| | - Mehdi Boukhechba
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
| | - Tabor E. Flickinger
- Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Laura E. Barnes
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
| |
Collapse
|
7
|
Hernández-Orallo E, Manzoni P, Calafate CT, Cano JC. A methodology for evaluating digital contact tracing apps based on the COVID-19 experience. Sci Rep 2022; 12:12728. [PMID: 35882975 PMCID: PMC9321289 DOI: 10.1038/s41598-022-17024-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022] Open
Abstract
Controlling the spreading of infectious diseases has been shown crucial in the COVID-19 pandemic. Traditional contact tracing is used to detect newly infected individuals by tracing their previous contacts, and by selectively checking and isolating any individuals likely to have been infected. Digital contact tracing with the utilisation of smartphones was contrived as a technological aid to improve this manual, slow and tedious process. Nevertheless, despite the high hopes raised when smartphone-based contact tracing apps were introduced as a measure to reduce the spread of the COVID-19, their efficiency has been moderately low. In this paper, we propose a methodology for evaluating digital contact tracing apps, based on an epidemic model, which will be used not only to evaluate the deployed Apps against the COVID-19 but also to determine how they can be improved for future pandemics. Firstly, the model confirms the moderate effectiveness of the deployed digital contact tracing, confirming the fact that it could not be used as the unique measure to fight against the COVID-19, and had to be combined with additional measures. Secondly, several improvements are proposed (and evaluated) to increase the efficiency of digital control tracing to become a more useful tool in the future.
Collapse
Affiliation(s)
- Enrique Hernández-Orallo
- Computer Engineering Department (DISCA), Universitat Politècnica de València, 46022, Valencia, Spain.
| | - Pietro Manzoni
- Computer Engineering Department (DISCA), Universitat Politècnica de València, 46022, Valencia, Spain
| | - Carlos T Calafate
- Computer Engineering Department (DISCA), Universitat Politècnica de València, 46022, Valencia, Spain
| | - Juan-Carlos Cano
- Computer Engineering Department (DISCA), Universitat Politècnica de València, 46022, Valencia, Spain
| |
Collapse
|
8
|
Nayan NA, Jie Yi C, Suboh MZ, Mazlan NF, Periyasamy P, Abdul Rahim MYZ, Shah SA. COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology. Front Public Health 2022; 10:920849. [PMID: 35928478 PMCID: PMC9343670 DOI: 10.3389/fpubh.2022.920849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter.
Collapse
Affiliation(s)
- Nazrul Anuar Nayan
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
- Institute Islam Hadhari, Universiti Kebangsaan Malaysia, Bangi, Malaysia
- *Correspondence: Nazrul Anuar Nayan
| | - Choon Jie Yi
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Mohd Zubir Suboh
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Nur-Fadhilah Mazlan
- Institute for Environment and Development, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | | | | | | |
Collapse
|
9
|
Lindemann C, Mehlhose FM. Kontakt-Tracing-Apps. INFORMATIK SPEKTRUM 2022. [PMCID: PMC8938967 DOI: 10.1007/s00287-022-01444-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Zur Bekämpfung der Coronapandemie wurden erstmals mobile Applikationen zur Kontakterfassung und -nachverfolgung, also dem systematischen Erfassen aller Personen mit zeitlicher und räumlicher Nähe zu einer infizierten Person, eingesetzt. In Deutschland haben hierzu vor allem die Corona-Warn-App und die Luca-App Verbreitung gefunden. Dieser Artikel diskutiert die beiden Apps vor allem im Hinblick auf Datenschutz, Sicherheit sowie deren Effektivität zur Bekämpfung der Pandemie. Im Gegensatz zu der Vielzahl an populärwissenschaftlichen Artikeln liegt der Fokus in diesem Artikel auf den technischen Aspekten der Kontakt-Tracing-Apps.
Collapse
Affiliation(s)
- Christoph Lindemann
- Lehrstuhl für Rechnernetze und verteilte Systeme, Institut für Informatik, Universität Leipzig, Leipzig, Deutschland
| | - Frank Martin Mehlhose
- Lehrstuhl für Rechnernetze und verteilte Systeme, Institut für Informatik, Universität Leipzig, Leipzig, Deutschland
| |
Collapse
|
10
|
Keenan KE, Aumentado J, Booth H, Briggman KA, Kraft M, Martin MN, Peralta RC, Robinson AY, Shalm K, Stephens MS, Townsend EA, Nam SW. Development and Evaluation of Bluetooth Low-Energy Device for Electronic Encounter Metrics. JOURNAL OF RESEARCH OF THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 2022; 126:126043. [PMID: 39070986 PMCID: PMC11249359 DOI: 10.6028/jres.126.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/06/2021] [Indexed: 07/30/2024]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic led to the need for tracking of physical contacts and potential exposure to disease. Traditional contact tracing can be augmented by electronic tools called "electronic contact tracing" or "exposure notification.". Some methods were built to work with smartphones; however, smartphones are not prevalent in some high-contact areas (e.g., schools and nursing homes). We present the design and initial testing of low-cost, highly privacy preserving wearable exposure notification devices. Several devices were constructed based on existing hardware and operated independently of a smartphone. The method (devices and analyses) was not able to reliably use the received signal strength indicator (RSSI) as a proxy for distance between pairs of devices; the accuracy of RSSI as a proxy for distance decreased dramatically outside of the idealized conditions. However, even an imperfect device could be useful for research on how people use and move through spaces. With some improvement, these devices could be used to understand disease spread and human or animal interaction in indoor environments.
Collapse
Affiliation(s)
- Kathryn E. Keenan
- National Institute of Standards and Technology,
Boulder, CO 80305
USA
| | - José Aumentado
- National Institute of Standards and Technology,
Boulder, CO 80305
USA
| | - Harold Booth
- National Institute of Standards and Technology,
Gaithersburg, MD 20899
USA
| | | | - Mikail Kraft
- National Institute of Standards and Technology,
Boulder, CO 80305
USA
| | - Michele N. Martin
- National Institute of Standards and Technology,
Boulder, CO 80305
USA
| | - René C. Peralta
- National Institute of Standards and Technology,
Gaithersburg, MD 20899
USA
| | - Angela Y. Robinson
- National Institute of Standards and Technology,
Gaithersburg, MD 20899
USA
| | - Krister Shalm
- National Institute of Standards and Technology,
Boulder, CO 80305
USA
- 3Department of Physics, 390 UCB, University of Colorado,
Boulder, CO 80309
USA
| | | | - Emily A. Townsend
- National Institute of Standards and Technology,
Gaithersburg, MD 20899
USA
- Joint Quantum Institute, University of Maryland,
College Park, MD 20742
USA
| | - Sae Woo Nam
- National Institute of Standards and Technology,
Boulder, CO 80305
USA
| |
Collapse
|
11
|
Admiraal R, Millen J, Patel A, Chambers T. A Case Study of Bluetooth Technology as a Supplemental Tool in Contact Tracing. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2022; 6:208-227. [PMID: 35079686 PMCID: PMC8773400 DOI: 10.1007/s41666-021-00112-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/21/2021] [Accepted: 12/06/2021] [Indexed: 11/25/2022]
Abstract
We present results from a 7-day trial of a Bluetooth-enabled card by the New Zealand Ministry of Health to investigate its usefulness in contact tracing. A comparison of the card with traditional contact tracing, which relies on self-reports of contacts to case investigators, demonstrated significantly higher levels of internal consistency in detected contact events by Bluetooth-enabled cards with 88% of contact events being detected by both cards involved in an interaction as compared to 64% for self-reports of contacts to case investigators. We found no clear evidence of memory recall worsening in reporting contact events that were further removed in time from the date of a case investigation. Roughly 66% of contact events between trial participants that were indicated by cards went unreported to case investigators, simultaneously highlighting the shortcomings of traditional contact tracing and the value of Bluetooth technology in detecting contact events that may otherwise go unreported. At the same time, cards detected only 65% of self-reported contact events, in part due to increasing non-compliance as the study progressed. This would suggest that Bluetooth technology can only be considered as a supplemental tool in contact tracing and not a viable replacement to traditional contact tracing unless measures are introduced to ensure greater compliance.
Collapse
Affiliation(s)
- Ryan Admiraal
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
| | | | - Ankit Patel
- Precision Data Science, Wellington, New Zealand
| | - Tim Chambers
- Health, Environment & Infection Research Unit, Department of Public Health, University of Otago, Wellington, New Zealand
| |
Collapse
|
12
|
Alo UR, Nkwo FO, Nweke HF, Achi II, Okemiri HA. Non-Pharmaceutical Interventions against COVID-19 Pandemic: Review of Contact Tracing and Social Distancing Technologies, Protocols, Apps, Security and Open Research Directions. SENSORS (BASEL, SWITZERLAND) 2021; 22:280. [PMID: 35009822 PMCID: PMC8749862 DOI: 10.3390/s22010280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/17/2022]
Abstract
The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19.
Collapse
Affiliation(s)
- Uzoma Rita Alo
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Friday Onwe Nkwo
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Henry Friday Nweke
- Centre for Research in Machine Learning, Artificial Intelligence and Network Systems, Computer Science Department, Ebonyi State University, P.M.B 053, Abakaliki 480211, Ebonyi State, Nigeria;
| | - Ifeanyi Isaiah Achi
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| | - Henry Anayo Okemiri
- Department of Computer Science and Informatics, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo P.M.B 1010, Abakaliki 480211, Ebonyi State, Nigeria; (F.O.N.); (I.I.A.); (H.A.O.)
| |
Collapse
|
13
|
Meijerink H, Mauroy C, Johansen MK, Braaten SM, Lunde CUS, Arnesen TM, Feruglio SL, Nygård K, Madslien EH. The First GAEN-Based COVID-19 Contact Tracing App in Norway Identifies 80% of Close Contacts in "Real Life" Scenarios. Front Digit Health 2021; 3:731098. [PMID: 34870268 PMCID: PMC8637921 DOI: 10.3389/fdgth.2021.731098] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 10/11/2021] [Indexed: 11/24/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) response in most countries has relied on testing, isolation, contact tracing, and quarantine (TITQ), which is labor- and time-consuming. Therefore, several countries worldwide launched Bluetooth-based apps as supplementary tools. The aim of using contact tracing apps is to rapidly notify people about their possible exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and thus make the process of TITQ more efficient, especially upon exposure in public places. We evaluated the Norwegian Google Apple exposure notification (GAEN)-based contact tracing app Smittestopp v2 under relevant "real-life" test scenarios. We used a total of 40 devices, representing six different brands, and compared two different exposure configurations, experimented with different time thresholds and weights of the Bluetooth attenuation levels (buckets), and calculated the true notification rates among close contacts (≤2 m and ≥15 min) and false notification of sporadic contacts. In addition, we assessed the impact of using different operating systems and locations of the phone (hand/pocket). The best configuration tested to trigger exposure notification resulted in the correct notification of 80% of the true close contacts and incorrect notification of 34% of the sporadic contacts. Among those who incorrectly received notifications, most (67%) were within 2 m but the duration of contact was <15 min and thus they were not, per se, considered as "close contacts." Lower sensitivity was observed when using the iOS operating systems or carrying the phone in the pocket instead of in the hand. The results of this study were used to improve and evaluate the performance of the Norwegian contact-tracing app Smittestopp.
Collapse
Affiliation(s)
- Hinta Meijerink
- Department of Infectious Diseases and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Camilla Mauroy
- Department of Infectious Diseases and Prevention, Norwegian Institute of Public Health, Oslo, Norway
| | - Mia Karoline Johansen
- Department of Infectious Diseases and Prevention, Norwegian Institute of Public Health, Oslo, Norway
| | - Sindre Møgster Braaten
- Department of Information Technology (IT) Systems Bergen, Norwegian Institute of Public Health, Bergen, Norway
| | | | - Trude Margrete Arnesen
- Department of Infectious Diseases and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Siri Laura Feruglio
- Department of Infectious Diseases and Prevention, Norwegian Institute of Public Health, Oslo, Norway
| | - Karin Nygård
- Department of Infectious Diseases and Prevention, Norwegian Institute of Public Health, Oslo, Norway
| | - Elisabeth Henie Madslien
- Department of Infectious Diseases and Prevention, Norwegian Institute of Public Health, Oslo, Norway
- Norwegian Defence Research Establishment (FFI), Kjeller, Norway
| |
Collapse
|
14
|
Elmokashfi A, Sundnes J, Kvalbein A, Naumova V, Reinemo SA, Florvaag PM, Stensland HK, Lysne O. Nationwide rollout reveals efficacy of epidemic control through digital contact tracing. Nat Commun 2021; 12:5918. [PMID: 34635661 PMCID: PMC8505561 DOI: 10.1038/s41467-021-26144-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 09/21/2021] [Indexed: 11/08/2022] Open
Abstract
Fuelled by epidemiological studies of SARS-CoV-2, contact tracing by mobile phones has been put to use in many countries. Over a year into the pandemic, we lack conclusive evidence on its effectiveness. To address this gap, we used a unique real world contact data set, collected during the rollout of the first Norwegian contact tracing app in the Spring of 2020. Our dataset involves millions of contacts between 12.5% of the adult population, which enabled us to measure the real-world app performance. The technological tracing efficacy was measured at 80%, and we estimated that at least 11.0% of the discovered close contacts could not have been identified by manual contact tracing. Our results also indicated that digital contact tracing can flag individuals with excessive contacts, which can help contain superspreading related outbreaks. The overall effectiveness of digital tracing depends strongly on app uptake, but significant impact can be achieved for moderate uptake numbers. Used as a supplement to manual tracing and other measures, digital tracing can be instrumental in controlling the pandemic. Our findings can thus help informing public health policies in the coming months.
Collapse
Affiliation(s)
- Ahmed Elmokashfi
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
| | | | - Amund Kvalbein
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Valeriya Naumova
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | | | | | - Håkon Kvale Stensland
- Simula Research Laboratory, Lysaker, Norway
- Institutt for informatikk, University of Oslo, Oslo, Norway
| | - Olav Lysne
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| |
Collapse
|
15
|
Madoery PG, Detke R, Blanco L, Comerci S, Fraire J, Gonzalez Montoro A, Bellassai JC, Britos G, Ojeda S, Finochietto JM. Feature selection for proximity estimation in COVID-19 contact tracing apps based on Bluetooth Low Energy (BLE). PERVASIVE AND MOBILE COMPUTING 2021; 77:101474. [PMID: 34602920 PMCID: PMC8475095 DOI: 10.1016/j.pmcj.2021.101474] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 08/28/2021] [Accepted: 09/08/2021] [Indexed: 06/13/2023]
Abstract
During the COVID-19 pandemic, contact tracing apps based on the Bluetooth Low Energy (BLE) technology found in smartphones have been deployed by multiple countries despite BLE's debatable performance for determining close contacts among users. Current solutions estimate proximity based on a single feature: the mean attenuation of the BLE signal. In this context, a new generation of these apps which better exploits data from the BLE signal and other sensors available on phones can be fostered. Collected data can be used to extract multiple features that feed machine learning models which can potentially improve the accuracy of today's solutions. In this work, we consider the use of machine learning models to evaluate different feature sets that can be extracted from the received BLE signal, and assess the performance gain as more features are introduced in these models. Since indoor conditions have a strong impact in assessing the risk of being exposed to the SARS-CoV-2, we analyze the environment (indoor or outdoor) role in these models, aiming at understanding the need for apps that could increase proximity accuracy if aware of its environment. Results show that a better accuracy can be obtained in outdoor locations with respect to indoor ones, and that indoor proximity estimation can benefit more from the introduction of more features with respect to the outdoor estimation case. Accuracy can be increased about 10% when multiple features are considered if the device is aware of its environment, reaching a performance of up to 83% in indoor spaces and up to 91% in outdoor ones. These results encourage future contact tracing apps to integrate this awareness not only to better assess the associated risk of a given environment but also to improve the proximity accuracy for detecting close contacts.
Collapse
Affiliation(s)
- Pablo G Madoery
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
- Instituto de Estudios Avanzados en Ingeniería y Tecnología (IDIT) - CONICET, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Ramiro Detke
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Lucas Blanco
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Sandro Comerci
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Juan Fraire
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Aldana Gonzalez Montoro
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Juan Carlos Bellassai
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Grisel Britos
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Silvia Ojeda
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Jorge M Finochietto
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
- Instituto de Estudios Avanzados en Ingeniería y Tecnología (IDIT) - CONICET, Av. Velez Sarsfield 1611, Córdoba, Argentina
| |
Collapse
|
16
|
Abstract
[Figure: see text].
Collapse
Affiliation(s)
- Susan Landau
- The Fletcher School and School of Engineering, Department of Computer Science, Tufts University, Medford, MA 02155, USA.,Department of Computer Science, University College London, London, UK
| |
Collapse
|
17
|
Bahle G, Fortes Rey V, Bian S, Bello H, Lukowicz P. Using Privacy Respecting Sound Analysis to Improve Bluetooth Based Proximity Detection for COVID-19 Exposure Tracing and Social Distancing. SENSORS (BASEL, SWITZERLAND) 2021; 21:5604. [PMID: 34451046 PMCID: PMC8402400 DOI: 10.3390/s21165604] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 11/24/2022]
Abstract
We propose to use ambient sound as a privacy-aware source of information for COVID-19-related social distance monitoring and contact tracing. The aim is to complement currently dominant Bluetooth Low Energy Received Signal Strength Indicator (BLE RSSI) approaches. These often struggle with the complexity of Radio Frequency (RF) signal attenuation, which is strongly influenced by specific surrounding characteristics. This in turn renders the relationship between signal strength and the distance between transmitter and receiver highly non-deterministic. We analyze spatio-temporal variations in what we call "ambient sound fingerprints". We leverage the fact that ambient sound received by a mobile device is a superposition of sounds from sources at many different locations in the environment. Such a superposition is determined by the relative position of those sources with respect to the receiver. We present a method for using the above general idea to classify proximity between pairs of users based on Kullback-Leibler distance between sound intensity histograms. The method is based on intensity analysis only, and does not require the collection of any privacy sensitive signals. Further, we show how this information can be fused with BLE RSSI features using adaptive weighted voting. We also take into account that sound is not available in all windows. Our approach is evaluated in elaborate experiments in real-world settings. The results show that both Bluetooth and sound can be used to differentiate users within and out of critical distance (1.5 m) with high accuracies of 77% and 80% respectively. Their fusion, however, improves this to 86%, making evident the merit of augmenting BLE RSSI with sound. We conclude by discussing strengths and limitations of our approach and highlighting directions for future work.
Collapse
Affiliation(s)
- Gernot Bahle
- German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany; (V.F.R.); (S.B.); (H.B.); (P.L.)
| | - Vitor Fortes Rey
- German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany; (V.F.R.); (S.B.); (H.B.); (P.L.)
| | - Sizhen Bian
- German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany; (V.F.R.); (S.B.); (H.B.); (P.L.)
| | - Hymalai Bello
- German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany; (V.F.R.); (S.B.); (H.B.); (P.L.)
| | - Paul Lukowicz
- German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663 Kaiserslautern, Germany; (V.F.R.); (S.B.); (H.B.); (P.L.)
- Informatics Department, University of Kaiserslautern, Erwin-Schroedinger-Strasse 1, 67663 Kaiserslautern, Germany
| |
Collapse
|
18
|
Landmark-Assisted Compensation of User's Body Shadowing on RSSI for Improved Indoor Localisation with Chest-Mounted Wearable Device. SENSORS 2021; 21:s21165405. [PMID: 34450846 PMCID: PMC8398681 DOI: 10.3390/s21165405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 07/30/2021] [Accepted: 08/06/2021] [Indexed: 11/16/2022]
Abstract
Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.
Collapse
|
19
|
Hogan K, Macedo B, Macha V, Barman A, Jiang X. Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation. JMIR Med Inform 2021; 9:e27449. [PMID: 34254937 PMCID: PMC8291141 DOI: 10.2196/27449] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/03/2021] [Accepted: 04/14/2021] [Indexed: 02/06/2023] Open
Abstract
The global and national response to the COVID-19 pandemic has been inadequate due to a collective lack of preparation and a shortage of available tools for responding to a large-scale pandemic. By applying lessons learned to create better preventative methods and speedier interventions, the harm of a future pandemic may be dramatically reduced. One potential measure is the widespread use of contact tracing apps. While such apps were designed to combat the COVID-19 pandemic, the time scale in which these apps were deployed proved a significant barrier to efficacy. Many companies and governments sprinted to deploy contact tracing apps that were not properly vetted for performance, privacy, or security issues. The hasty development of incomplete contact tracing apps undermined public trust and negatively influenced perceptions of app efficacy. As a result, many of these apps had poor voluntary public uptake, which greatly decreased the apps' efficacy. Now, with lessons learned from this pandemic, groups can better design and test apps in preparation for the future. In this viewpoint, we outline common strategies employed for contact tracing apps, detail the successes and shortcomings of several prominent apps, and describe lessons learned that may be used to shape effective contact tracing apps for the present and future. Future app designers can keep these lessons in mind to create a version that is suitable for their local culture, especially with regard to local attitudes toward privacy-utility tradeoffs during public health crises.
Collapse
Affiliation(s)
- Katie Hogan
- Department of Bioengineering, Rice University, Houston, TX, United States
| | - Briana Macedo
- School of Engineering, Princeton University, Princeton, NJ, United States
| | - Venkata Macha
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Arko Barman
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, United States
- Data to Knowledge Lab, Rice University, Houston, TX, United States
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
| |
Collapse
|
20
|
Gasteiger N, Gasteiger C, Vedhara K, Broadbent E. The more the merrier! Barriers and facilitators to the general public's use of a COVID-19 contact tracing app in New Zealand. Inform Health Soc Care 2021; 47:132-143. [PMID: 34260338 DOI: 10.1080/17538157.2021.1951274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Contact tracing for infectious diseases can be partially automated using mobile applications. However, the success of these tools is dependent on significant uptake and frequent use by the public. This study explored the barriers and facilitators to the New Zealand (NZ) general public's use of the COVID-19 contact NZ COVID Tracer app. Adults (≥18 years, N = 373) in NZ. Qualitative and quantitative data were gathered from a nation-wide online survey. App use and frequency of use were presented as descriptive statistics. Qualitative data were analyzed thematically. 31% reported using the app frequently, 24% used it sometimes, 21% had installed but not used it, and 24% had not installed it. Barriers to use include technical issues, privacy and security concerns, forgetfulness and a lack of support from businesses. The perceived risk of contracting COVID-19, government recommendations and communications, and the importance of contact tracing facilitated use. Technical, user, business, and government factors influenced the public's use of a COVID-19 contact tracing app. The development of apps requiring minimal user effort and initial user testing may improve uptake. Enabling environments and better risk communication may improve uptake of similar community-driven contact tracing apps during future pandemics.
Collapse
Affiliation(s)
- Norina Gasteiger
- Department of Psychological Medicine, University of Auckland, Auckland, New Zealand
| | - Chiara Gasteiger
- Department of Psychological Medicine, University of Auckland, Auckland, New Zealand
| | - Kavita Vedhara
- Division of Primary Care, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Elizabeth Broadbent
- Department of Psychological Medicine, University of Auckland, Auckland, New Zealand
| |
Collapse
|
21
|
Shubina V, Ometov A, Basiri A, Lohan ES. Effectiveness modelling of digital contact-tracing solutions for tackling the COVID-19 pandemic. JOURNAL OF NAVIGATION 2021; 74:853-886. [PMCID: PMC8060546 DOI: 10.1017/s0373463321000175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 02/15/2021] [Indexed: 05/18/2023]
Abstract
Since the beginning of the coronavirus (COVID-19) global pandemic, digital contact-tracing applications (apps) have been at the centre of attention as a digital tool to enable citizens to monitor their social distancing, which appears to be one of the leading practices for mitigating the spread of airborne infectious diseases. Many countries have been working towards developing suitable digital contact-tracing apps to allow the measurement of the physical distance between citizens and to alert them when contact with an infected individual has occurred. However, the adoption of digital contact-tracing apps has faced several challenges so far, including interoperability between mobile devices and users’ privacy concerns. There is a need to reach a trade-off between the achievable technical performance of new technology, false-positive rates, and social and behavioural factors. This paper reviews a wide range of factors and classifies them into three categories of technical, epidemiological and social ones, and incorporates these into a compact mathematical model. The paper evaluates the effectiveness of digital contact-tracing apps based on received signal strength measurements. The results highlight the limitations, potential and challenges of the adoption of digital contact-tracing apps.
Collapse
Affiliation(s)
- Viktoriia Shubina
- Tampere University, Tampere, Finland
- University ‘Politehnica’ of Bucharest, Bucharest, Romania
- Corresponding author. E-mail:
| | | | | | | |
Collapse
|
22
|
Pandl KD, Thiebes S, Schmidt-Kraepelin M, Sunyaev A. How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19. Sci Rep 2021; 11:9414. [PMID: 33941793 PMCID: PMC8093197 DOI: 10.1038/s41598-021-88768-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/14/2021] [Indexed: 01/12/2023] Open
Abstract
To combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.
Collapse
Affiliation(s)
- Konstantin D Pandl
- Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Scott Thiebes
- Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Manuel Schmidt-Kraepelin
- Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Ali Sunyaev
- Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany.
| |
Collapse
|
23
|
Leith DJ, Farrell S. Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a commuter bus. PLoS One 2021; 16:e0250826. [PMID: 33914810 PMCID: PMC8084251 DOI: 10.1371/journal.pone.0250826] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/29/2021] [Indexed: 11/19/2022] Open
Abstract
We report on the results of a measurement study carried out on a commuter bus in Dublin, Ireland using the Google/Apple Exposure Notification (GAEN) API. This API is likely to be widely used by Covid-19 contact tracing apps. Measurements were collected between 60 pairs of Android handset locations and are publicly available. We find that the attenuation level reported by the GAEN API need not increase with distance between handsets, consistent with there being a complex radio environment inside a bus caused by the metal-rich environment. Changing the people sitting in a pair of seats can cause variations of ±10dB in the attenuation level reported by the GAEN API. Applying the rule used by the Swiss Covid-19 contact tracing app to trigger an exposure notification to our bus measurements we find that no exposure notifications would have been triggered despite the fact that all pairs of handsets were within 2m of one another for at least 15 mins. Applying an alternative threshold-based exposure notification rule can somewhat improve performance to a detection rate of 5% when an exposure duration threshold of 15 minutes is used, increasing to 8% when the exposure duration threshold is reduced to 10 mins. Stratifying the data by distance between pairs of handsets indicates that there is only a weak dependence of detection rate on distance.
Collapse
Affiliation(s)
- Douglas J. Leith
- School of Computer Science & Statistics, Trinity College Dublin, Dublin, Ireland
- * E-mail:
| | - Stephen Farrell
- School of Computer Science & Statistics, Trinity College Dublin, Dublin, Ireland
| |
Collapse
|
24
|
A Bluetooth-Based Architecture for Contact Tracing in Healthcare Facilities. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2020. [DOI: 10.3390/jsan10010002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the latest standard releases, Bluetooth technology is becoming more and more relevant for building and industrial automation. At the same time, Bluetooth is now becoming fundamental for contact tracing applications, to support monitoring and containment of the COVID-19 pandemic. Critical facilities such as nursing homes and hospitals have been severely exposed to the pandemic, but the currently available short-range wireless technology still faces the fundamental limits of proximity accuracy, battery lifetime, and privacy in those complex indoor environments. The aim of this paper is to investigate effective ways of building an architecture with heterogeneous devices to support contact tracing in critical scenarios such as healthcare facilities, while meeting the required level of accuracy and privacy. A framework based on standard Bluetooth mesh networking technology is proposed, and the research challenges are discussed.
Collapse
|
25
|
Wirth FN, Johns M, Meurers T, Prasser F. Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review. JMIR Mhealth Uhealth 2020; 8:e22594. [PMID: 33074833 PMCID: PMC7674146 DOI: 10.2196/22594] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/26/2020] [Accepted: 10/09/2020] [Indexed: 01/18/2023] Open
Abstract
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).
Collapse
Affiliation(s)
- Felix Nikolaus Wirth
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Marco Johns
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Thierry Meurers
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Fabian Prasser
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| |
Collapse
|
26
|
Ranisch R, Nijsingh N, Ballantyne A, van Bergen A, Buyx A, Friedrich O, Hendl T, Marckmann G, Munthe C, Wild V. Digital contact tracing and exposure notification: ethical guidance for trustworthy pandemic management. ETHICS AND INFORMATION TECHNOLOGY 2020; 23:285-294. [PMID: 33106749 PMCID: PMC7577205 DOI: 10.1007/s10676-020-09566-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
There is growing interest in contact tracing apps (CT apps) for pandemic management. It is crucial to consider ethical requirements before, while, and after implementing such apps. In this paper, we illustrate the complexity and multiplicity of the ethical considerations by presenting an ethical framework for a responsible design and implementation of CT apps. Using this framework as a starting point, we briefly highlight the interconnection of social and political contexts, available measures of pandemic management, and a multi-layer assessment of CT apps. We will discuss some trade-offs that arise from this perspective. We then suggest that public trust is of major importance for population uptake of contact tracing apps. Hasty, ill-prepared or badly communicated implementations of CT apps will likely undermine public trust, and as such, risk impeding general effectiveness.
Collapse
Affiliation(s)
- Robert Ranisch
- International Center for Ethics in the Sciences and Humanities, University of Tübingen, Tübingen, Germany
| | - Niels Nijsingh
- Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Angela Ballantyne
- Centre for Biomedical Ethics, National University of Singapore, Singapore, Singapore
| | - Anne van Bergen
- Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Alena Buyx
- Institute for History and Ethics in Medicine, Technical University Munich, Munich, Germany
| | | | - Tereza Hendl
- Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Georg Marckmann
- Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Christian Munthe
- Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Gothenburg, Sweden
| | - Verina Wild
- Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany
| |
Collapse
|
27
|
Leith DJ, Farrell S. Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a light-rail tram. PLoS One 2020; 15:e0239943. [PMID: 32997724 PMCID: PMC7526892 DOI: 10.1371/journal.pone.0239943] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/16/2020] [Indexed: 11/30/2022] Open
Abstract
We report on the results of a Covid-19 contact tracing app measurement study carried out on a standard design of European commuter tram. Our measurements indicate that in the tram there is little correlation between Bluetooth received signal strength and distance between handsets. We applied the detection rules used by the Italian, Swiss and German apps to our measurement data and also characterised the impact on performance of changes in the parameters used in these detection rules. We find that the Swiss and German detection rules trigger no exposure notifications on our data, while the Italian detection rule generates a true positive rate of 50% and a false positive rate of 50%. Our analysis indicates that the performance of such detection rules is similar to that of triggering notifications by randomly selecting from the participants in our experiments, regardless of proximity.
Collapse
Affiliation(s)
- Douglas J. Leith
- School of Computer Science & Statistics, Trinity College Dublin, Dublin, Ireland
| | - Stephen Farrell
- School of Computer Science & Statistics, Trinity College Dublin, Dublin, Ireland
| |
Collapse
|