scholarly journals Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a commuter bus

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250826
Author(s):  
Douglas J. Leith ◽  
Stephen Farrell
Keyword(s):  
Exposure Duration ◽  
Detection Rate ◽  
Weak Dependence ◽  
Contact Tracing ◽  
Improve Performance ◽  
Measurement Study ◽  
Proximity Detection ◽  
The People ◽  
Duration Threshold ◽  
Attenuation Level

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.

scholarly journals How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Konstantin D. Pandl ◽  
Scott Thiebes ◽  
Manuel Schmidt-Kraepelin ◽  
Ali Sunyaev
Keyword(s):  
Disease Spread ◽  
Contact Tracing ◽  
Behavioral Characteristics ◽  
Policy Makers ◽  
Detection Range ◽  
Proximity Detection ◽  
Effective Technology ◽  
Different Types ◽  
Effectiveness And Efficiency ◽  
The Impact

AbstractTo 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.

scholarly journals Transmission dynamics and the effects of non-pharmaceutical interventions in the COVID-19 outbreak resurged in Beijing, China: a descriptive and modelling study

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e047227
Author(s):  
Xiaoming Cui ◽  
Lin Zhao ◽  
Yuhao Zhou ◽  
Xin Lin ◽  
Runze Ye ◽  
...  
Keyword(s):  
Close Contact ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Population Mobility ◽  
Wholesale Market ◽  
The People ◽  
Spatiotemporal Analyses ◽  
Epidemiological Characteristics ◽  
Pharmaceutical Interventions ◽  
Modelling Study

ObjectiveTo evaluate epidemiological characteristics and transmission dynamics of COVID-19 outbreak resurged in Beijing and to assess the effects of three non-pharmaceutical interventions.DesignDescriptive and modelling study based on surveillance data of COVID-19 in Beijing.SettingOutbreak in Beijing.ParticipantsThe database included 335 confirmed cases of COVID-19.MethodsTo conduct spatiotemporal analyses of the outbreak, we collected individual records on laboratory-confirmed cases of COVID-19 from 11 June 2020 to 5 July 2020 in Beijing, and visitor flow and products transportation data of Xinfadi Wholesale Market. We also built a modified susceptible-exposed-infected-removed model to investigate the effect of interventions deployed in Beijing.ResultsWe found that the staff working in the market (52.2%) and the people around 10 km to this epicentre (72.5%) were most affected, and the population mobility entering-exiting Xinfadi Wholesale Market significantly contributed to the spread of COVID-19 (p=0.021), but goods flow of the market had little impact on the virus spread (p=0.184). The prompt identification of Xinfadi Wholesale Market as the infection source could have avoided a total of 25 708 (95% CI 13 657 to 40 625) cases if unnoticed transmission lasted for a month. Based on the model, we found that active screening on targeted population by nucleic acid testing alone had the most significant effect.ConclusionsThe non-pharmaceutical interventions deployed in Beijing, including localised lockdown, close-contact tracing and community-based testing, were proved to be effective enough to contain the outbreak. Beijing has achieved an optimal balance between epidemic containment and economic protection.

scholarly journals A fair efficacy formula for assessing the effectiveness of contact tracing applications

2020 ◽  
Author(s):  
Adam Fowler
Keyword(s):  
Mobile Applications ◽  
Mobile Apps ◽  
Time Windows ◽  
Real Life ◽  
Contact Tracing ◽  
Proximity Detection ◽  
Epidemiological Risk ◽  
Efficacy Measure ◽  
Impact Of Technology ◽  
The Impact

AbstractMobile contact tracing apps have been developed by many countries in response to the COVID-19 pandemic. Trials have focussed on unobserved population trials or staged scenarios aimed to simulate real life. No efficacy measure has been developed that assesses the fundamental ability of any proximity detection protocol to accurately detect, measure, and therefore assess the epidemiological risk that a mobile phone owner has been placed at. This paper provides a fair efficacy formula that can be applied to any mobile contact tracing app, using any technology, allowing it’s likely epidemiological effectiveness to be assessed. This paper defines such a formula and provides results for several simulated protocols as well as one real life protocol tested according to the standard methodology set out in this paper. The results presented show that protocols that use time windows greater than 30 seconds or that bucket their distance analogue (E.g. RSSI for Bluetooth) provide poor estimates of risk, showing an efficacy rating of less than 6%. The fair efficacy formula is shown in this paper to be able to be used to calculate the ‘Efficacy of contact tracing’ variable value as used in two papers on using mobile applications for contact tracing [6]. The output from the formulae in this paper, therefore, can be used to directly assess the impact of technology on the spread of a disease outbreak. This formula can be used by nations developing contact tracing applications to assess the efficacy of their applications. This will allow them to reassure their populations and increase the uptake of contact tracing mobile apps, hopefully having an effect on slowing the spread of COVID-19 and future epidemics.

scholarly journals Improving power theft detection using efficient clustering and ensemble classification

2021 ◽  
Vol 11 (5) ◽  
pp. 3704
Author(s):  
Hassan Ghaedi ◽  
Seyed Reza Kamel Tabbakh Farizani ◽  
Reza Ghaemi
Keyword(s):  
High Performance ◽  
Detection Rate ◽  
Cross Validation ◽  
Search Algorithm ◽  
Ensemble Classification ◽  
Improve Performance ◽  
The World ◽  
Clustering And Classification ◽  
Validation Technique ◽  
Power Generation Systems

One of the main concerns of power generation systems around the world is power theft. This research proposes a framework that merges clustering and classification together in order to power theft detection. Due to the fact that most datasets do not have abnormal samples or are few, we have added abnormal samples to the original datasets using artificial attacks to create balance in the datasets and increase the correct detection rate. We improved the crow search algorithm (CSA) and used the weight feature of Crows to improve performance of clustering phase. Also, to create balance between diversification and intensification, we calculated the awareness probability parameter (AP) dynamically at iterations of the algorithm. To evaluate the performance, we used the cross validation technique have used the stacking technique in its training phase. The results of extensive experiments on three reference datasets showed high performance to detect power theft. The evaluation results showed that if the data is collected correctly and sufficiently, this framework can effectively detect power theft in any actual power grid. Also, for new attacks, if their patterns can be detected from the data, it is easily possible to implement these types of attacks.

scholarly journals Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study

2020 ◽  
Author(s):  
Lee Worden ◽  
Rae Wannier ◽  
Seth Blumberg ◽  
Alex Y. Ge ◽  
George W. Rutherford ◽  
...  
Keyword(s):  
San Francisco ◽  
Statistical Estimation ◽  
Detection Rate ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Reproduction Numbers ◽  
The Impact ◽  
The City ◽  
Testing Coverage ◽  
Over Time

AbstractThe current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco’s shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, −20.1%–81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.

scholarly journals Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 87 ◽  
Author(s):  
Viktoriia Shubina ◽  
Sylvia Holcer ◽  
Michael Gould ◽  
Elena Simona Lohan
Keyword(s):  
Mobile Devices ◽  
Large Scale ◽  
Data Science ◽  
Research Field ◽  
Mobile Internet ◽  
Contact Tracing ◽  
Future Research ◽  
Location Tracking ◽  
Proximity Detection ◽  
User Location

Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

scholarly journals Potential reduction in transmission of COVID-19 by digital contact tracing systems

2020 ◽  
Author(s):  
Michael J Plank ◽  
Alex James ◽  
Audrey Lustig ◽  
Nicholas Steyn ◽  
Rachelle N Binny ◽  
...  
Keyword(s):  
Uptake Rate ◽  
Process Model ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Location Tracking ◽  
Public Health Agencies ◽  
Proximity Detection ◽  
Health Agencies ◽  
Alternative Approaches ◽  
Age Structured

Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking, and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days, and the probability of elimination. We show that effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. We conclude that, for digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

scholarly journals A novel comprehensive metric to assess COVID-19 testing outcomes: Effects of geography, government, and policy response

2020 ◽  
Author(s):  
Anthony C. Kuster ◽  
Hans J. Overgaard
Keyword(s):  
Detection Rate ◽  
Case Fatality ◽  
Multiple Linear Regression Model ◽  
System Capacity ◽  
Contact Tracing ◽  
Extrinsic Factors ◽  
Detection Rates ◽  
Case Identification ◽  
Rate Test ◽  
Reported Data

AbstractTesting and case identification are key strategies in controlling the COVID-19 pandemic. Contact tracing and isolation are only possible if cases have been identified. The effectiveness of testing must be tracked, but a single comprehensive metric is not available to assess testing effectiveness, and no timely estimates of case detection rate are available globally, making inter-country comparisons difficult. The purpose of this paper was to propose a single, comprehensive metric, called the COVID-19 Testing Index (CovTI) scaled from 0 to 100, that incorporated several testing metrics. The index was based on case-fatality rate, test positivity rate, active cases, and an estimate of the detection rate. It used parsimonious modeling to estimate the true total number of COVID-19 cases based on deaths, testing, health system capacity, and government transparency. Publicly reported data from 188 countries and territories were included in the index. Estimates of detection rates aligned with previous estimates in literature (R2=0.97). As of June 3, 2020, the states with the highest CovTI included Iceland, Australia, New Zealand, Hong Kong, and Thailand, and some island nations. Globally, CovTI increased from April 20 to June 3 but declined in ca. 10% of countries. Bivariate analyses showed the average in countries with open public testing policies (59.7, 95% CI 55.6-63.8) were significantly higher than countries with no testing policy (30.2, 95% CI 18.1-42.3) (p<0.0001). A multiple linear regression model assessed the association of independent grouping variables with CovTI. Open public testing and extensive contact tracing were shown to significantly increase CovTI, after adjusting for extrinsic factors, including geographic isolation and centralized forms of government. This tool may be useful for policymakers to assess testing effectiveness, inform decisions, and identify model countries. It may also serve as a tool for researchers in analyses by combining it with other databases.

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