Digital contact tracing technologies in epidemics: a rapid review

Anglemyer, Andrew; Moore, Theresa Hm; Parker, Lisa; Chambers, Tim; Grady, Alice; Chiu, Kellia; Parry, Matthew; Wilczynska, Magdalena; Flemyng, Ella; Bero, Lisa

doi:10.1002/14651858.cd013699

Cited by 175 publications

(183 citation statements)

References 49 publications

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“…The disease states and transition paths are shown in Figure 24. Individuals of different age groups have different infectivity and susceptibility; dwell time distributions and state transition probability distributions are stratified by the following age groups: preschool (0-4 years), students (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17), adults , older adults (50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64) and seniors (65+). Furthermore, individuals that are vaccinated have different disease parameter values than those that are not vaccinated.…”

Section: Simulation Parameterizationmentioning

confidence: 99%

“…The location model constructs a set of spatially embedded locations L consisting of residence locations where households live, and activity locations where people conduct their non-Home activities. This construction is highly granular and is rooted in data such as the MS Building data [44], HERE/NAVTEQ data [33] for points-of-interest (POIs) and land-use classifications, National Center for Education Statistics (NCES) [49] data for public schools, as well as LandScan 3 , OpenStreetMap 4 , and Gridded Populations of the World (GPW) v4 5 . A point plot of the locations of Virginia is shown in Figure 12.…”

Section: A1 Generating Synthetic Populations and Networkmentioning

confidence: 99%

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Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

Chen

Hoops

Marathe

et al. 2021

Preprint

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We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatiotemporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56−110k infections, 3.2− 5.4k hospitalizations, and 700−900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3−6 million fewer infections, 181−306k fewer hospitalizations, and 51−62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.

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Section: Simulation Parameterizationmentioning

confidence: 99%

Section: A1 Generating Synthetic Populations and Networkmentioning

confidence: 99%

Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

Chen

Hoops

Marathe

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The rationales for using DPT apps as pandemic mitigation tools are based on a modelling study, which suggests that DPT alone has the ability to stop the spread of the COVID-19 pandemic [ 3 , 4 ]. Classic contact tracing is labor- and time-consuming, and exposed contacts can sometimes only be reached and notified with substantial time lags [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Drivers of Acceptance of COVID-19 Proximity Tracing Apps in Switzerland: Panel Survey Analysis

Wyl¹,

Höglinger²,

Sieber³

et al. 2021

JMIR Public Health Surveill

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Background Digital proximity tracing apps have been released to mitigate the transmission of SARS-CoV-2, the virus known to cause COVID-19. However, it remains unclear how the acceptance and uptake of these apps can be improved. Objective This study aimed to investigate the coverage of the SwissCovid app and the reasons for its nonuse in Switzerland during a period of increasing incidence of COVID-19 cases. Methods We collected data between September 28 and October 8, 2020, via a nationwide online panel survey (COVID-19 Social Monitor, N=1511). We examined sociodemographic and behavioral factors associated with app use by using multivariable logistic regression, whereas reasons for app nonuse were analyzed descriptively. Results Overall, 46.5% (703/1511) of the survey participants reported they used the SwissCovid app, which was an increase from 43.9% (662/1508) reported in the previous study wave conducted in July 2020. A higher monthly household income (ie, income >CHF 10,000 or >US $11,000 vs income ≤CHF 6000 or <US $6600 [reference]: odds ratio [OR] 1.92, 95% CI 1.40-2.64), more frequent internet use (ie, daily [reference] vs less than weekly: OR 0.37, 95% CI 0.16-0.85), better adherence to recommendations for wearing masks (ie, always or most of the time [reference] vs rarely or never: OR 0.28, 95% CI 0.15-0.52), and nonsmoker status (OR 1.32, 95% CI 1.01-1.71) were associated with an increased likelihood for app uptake. Citizenship status (ie, non-Swiss citizenship vs. Swiss [reference]: OR 0.61, 95% CI 0.43-0.87), and language region (French vs Swiss German [reference]: OR 0.61, 95% CI 0.46-0.80) were associated with a lower likelihood for app uptake. Further analysis in a randomly selected subsample (n=712) with more detailed information showed that higher levels of trust in government and health authorities were also associated with a higher likelihood for app uptake (ie, high vs low [reference] trust: OR 3.13, 95% CI 1.58-6.22). The most frequent reasons for app nonuse were lack of perceived benefit of using the app (297/808, 36.8%), followed by the lack of a compatible phone (184/808, 22.8%), and privacy concerns (181/808, 22.4%). Conclusions Eliminating technical hurdles and communicating the benefits of digital proximity tracing apps are crucial to promote further uptake and adherence of such apps and, ultimately, enhance their effectiveness to aid pandemic mitigation strategies.

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“…The user acceptability of contact tracing apps in five countries hit by the pandemic using a survey were investigated in Altmann et al [ 17 ]; however, the study did not review specific contact tracing apps. Similarly, several studies [ 18 - 22 ] did not review current COVID-19 apps through direct access of app stores. For instance, the work reported in Anglemyer et al [ 18 ] did a meta-analysis on medical databases to review contact tracing apps.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, several studies [ 18 - 22 ] did not review current COVID-19 apps through direct access of app stores. For instance, the work reported in Anglemyer et al [ 18 ] did a meta-analysis on medical databases to review contact tracing apps. Others used various methodologies to conduct their reviews.…”

Section: Introductionmentioning

confidence: 99%

Technology, Privacy, and User Opinions of COVID-19 Mobile Apps for Contact Tracing: Systematic Search and Content Analysis

Elkhodr¹,

Mubin²,

Iftikhar³

et al. 2021

J Med Internet Res

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Background Many countries across the globe have released their own COVID-19 contact tracing apps. This has resulted in the proliferation of several apps that used a variety of technologies. With the absence of a standardized approach used by the authorities, policy makers, and developers, many of these apps were unique. Therefore, they varied by function and the underlying technology used for contact tracing and infection reporting. Objective The goal of this study was to analyze most of the COVID-19 contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examined the underlying technologies used in contact tracing apps. It also attempted to provide some insights into their level of penetration and to gauge their public reception. This research also investigated the data collection, reporting, retention, and destruction procedures used by each of the apps under review. Methods This research study evaluated 13 apps corresponding to 10 countries based on the underlying technology used. The inclusion criteria ensured that most COVID-19-declared epicenters (ie, countries) were included in the sample, such as Italy. The evaluated apps also included countries that did relatively well in controlling the outbreak of COVID-19, such as Singapore. Informational and unofficial contact tracing apps were excluded from this study. A total of 30,000 reviews corresponding to the 13 apps were scraped from app store webpages and analyzed. Results This study identified seven distinct technologies used by COVID-19 tracing apps and 13 distinct apps. The United States was reported to have released the most contact tracing apps, followed by Italy. Bluetooth was the most frequently used underlying technology, employed by seven apps, whereas three apps used GPS. The Norwegian, Singaporean, Georgian, and New Zealand apps were among those that collected the most personal information from users, whereas some apps, such as the Swiss app and the Italian (Immuni) app, did not collect any user information. The observed minimum amount of time implemented for most of the apps with regard to data destruction was 14 days, while the Georgian app retained records for 3 years. No significant battery drainage issue was reported for most of the apps. Interestingly, only about 2% of the reviewers expressed concerns about their privacy across all apps. The number and frequency of technical issues reported on the Apple App Store were significantly more than those reported on Google Play; the highest was with the New Zealand app, with 27% of the reviewers reporting technical difficulties (ie, 10% out of 27% scraped reviews reported that the app did not work). The Norwegian, Swiss, and US (PathCheck) apps had the least reported technical issues, sitting at just below 10%. In terms of usability, many apps, such as those from Singapore, Australia, and Switzerland, did not provide the users with an option to sign out from their apps. Conclusions This article highlighted the fact that COVID-19 contact tracing apps are still facing many obstacles toward their widespread and public acceptance. The main challenges are related to the technical, usability, and privacy issues or to the requirements reported by some users.

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Digital contact tracing technologies in epidemics: a rapid review

Cited by 175 publications

References 49 publications

Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

Drivers of Acceptance of COVID-19 Proximity Tracing Apps in Switzerland: Panel Survey Analysis

Technology, Privacy, and User Opinions of COVID-19 Mobile Apps for Contact Tracing: Systematic Search and Content Analysis

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