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Zhang D, Britton T. An SEIR network epidemic model with manual and digital contact tracing allowing delays. Math Biosci 2024:109231. [PMID: 38914260 DOI: 10.1016/j.mbs.2024.109231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/26/2024]
Abstract
We consider an SEIR epidemic model on a network also allowing random contacts, where recovered individuals could either recover naturally or be diagnosed. Upon diagnosis, manual contact tracing is triggered such that each infected network contact is reported, tested and isolated with some probability and after a random delay. Additionally, digital tracing (based on a tracing app) is triggered if the diagnosed individual is an app-user, and then all of its app-using infectees are immediately notified and isolated. The early phase of the epidemic with manual and/or digital tracing is approximated by different multi-type branching processes, and three respective reproduction numbers are derived. The effectiveness of both contact tracing mechanisms is numerically quantified through the reduction of the reproduction number. This shows that app-using fraction plays an essential role in the overall effectiveness of contact tracing. The relative effectiveness of manual tracing compared to digital tracing increases if: more of the transmission occurs on the network, when the tracing delay is shortened, and when the network degree distribution is heavy-tailed. For realistic values, the combined tracing case can reduce R0 by 20%-30%, so other preventive measures are needed to reduce the reproduction number down to 1.2-1.4 for contact tracing to make it successful in avoiding big outbreaks.
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Affiliation(s)
- Dongni Zhang
- Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden.
| | - Tom Britton
- Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden
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2
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Duval D, Evans B, Sanders A, Hill J, Simbo A, Kavoi T, Lyell I, Simmons Z, Qureshi M, Pearce-Smith N, Arevalo CR, Beck CR, Bindra R, Oliver I. Non-pharmaceutical interventions to reduce COVID-19 transmission in the UK: a rapid mapping review and interactive evidence gap map. J Public Health (Oxf) 2024; 46:e279-e293. [PMID: 38426578 PMCID: PMC11141784 DOI: 10.1093/pubmed/fdae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/15/2024] [Accepted: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) were crucial in the response to the COVID-19 pandemic, although uncertainties about their effectiveness remain. This work aimed to better understand the evidence generated during the pandemic on the effectiveness of NPIs implemented in the UK. METHODS We conducted a rapid mapping review (search date: 1 March 2023) to identify primary studies reporting on the effectiveness of NPIs to reduce COVID-19 transmission. Included studies were displayed in an interactive evidence gap map. RESULTS After removal of duplicates, 11 752 records were screened. Of these, 151 were included, including 100 modelling studies but only 2 randomized controlled trials and 10 longitudinal observational studies.Most studies reported on NPIs to identify and isolate those who are or may become infectious, and on NPIs to reduce the number of contacts. There was an evidence gap for hand and respiratory hygiene, ventilation and cleaning. CONCLUSIONS Our findings show that despite the large number of studies published, there is still a lack of robust evaluations of the NPIs implemented in the UK. There is a need to build evaluation into the design and implementation of public health interventions and policies from the start of any future pandemic or other public health emergency.
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Affiliation(s)
- D Duval
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - B Evans
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - A Sanders
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - J Hill
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - A Simbo
- Evaluation and Epidemiological Science Division, UKHSA, Colindale NW9 5EQ, UK
| | - T Kavoi
- Cheshire and Merseyside Health Protection Team, UKHSA, Liverpool L3 1DS, UK
| | - I Lyell
- Greater Manchester Health Protection Team, UKHSA, Manchester M1 3BN, UK
| | - Z Simmons
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - M Qureshi
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - N Pearce-Smith
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - C R Arevalo
- Research, Evidence and Knowledge Division, UK Health Security Agency (UKHSA), London E14 5EA, UK
| | - C R Beck
- Evaluation and Epidemiological Science Division, UKHSA, Salisbury SP4 0JG, UK
| | - R Bindra
- Clinical and Public Health Response Division, UKHSA, London E14 5EA, UK
| | - I Oliver
- Director General Science and Research and Chief Scientific Officer, UKHSA, London E14 5EA, UK
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Chambers T, Anglemyer A, Chen A, Atkinson J, Baker MG. Population and contact tracer uptake of New Zealand's QR-code-based digital contact tracing app for COVID-19. Epidemiol Infect 2024; 152:e66. [PMID: 38629265 PMCID: PMC11062780 DOI: 10.1017/s0950268824000608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/27/2024] [Accepted: 04/10/2024] [Indexed: 04/30/2024] Open
Abstract
This study aimed to understand the population and contact tracer uptake of the quick response (QR)-code-based function of the New Zealand COVID Tracer App (NZCTA) used for digital contact tracing (DCT). We used a retrospective cohort of all COVID-19 cases between August 2020 and February 2022. Cases of Asian and other ethnicities were 2.6 times (adjusted relative risk (aRR) 2.58, 99 per cent confidence interval (95% CI) 2.18, 3.05) and 1.8 times (aRR 1.81, 95% CI 1.58, 2.06) more likely than Māori cases to generate a token during the Delta period, and this persisted during the Omicron period. Contact tracing organization also influenced location token generation with cases handled by National Case Investigation Service (NCIS) staff being 2.03 (95% CI 1.79, 2.30) times more likely to generate a token than cases managed by clinical staff at local Public Health Units (PHUs). Public uptake and participation in the location-based system independent of contact tracer uptake were estimated at 45%. The positive predictive value (PPV) of the QR code system was estimated to be close to nil for detecting close contacts but close to 100% for detecting casual contacts. Our paper shows that the QR-code-based function of the NZCTA likely made a negligible impact on the COVID-19 response in New Zealand (NZ) in relation to isolating potential close contacts of cases but likely was effective at identifying and notifying casual contacts.
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Affiliation(s)
- Tim Chambers
- Department of Public Health, University of Otago, Wellington, New Zealand
| | - Andrew Anglemyer
- Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand
| | - Andrew Chen
- Koi Tū: The Centre for Informed Futures, The University of Auckland, Auckland, New Zealand
| | - June Atkinson
- Department of Public Health, University of Otago, Wellington, New Zealand
| | - Michael G. Baker
- Department of Public Health, University of Otago, Wellington, New Zealand
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4
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Song S, Park J, Rho MJ. Effectiveness and intention to use a COVID-19 self-management app for epidemiological investigation: a web-based survey study. Front Public Health 2024; 12:1343734. [PMID: 38601508 PMCID: PMC11004299 DOI: 10.3389/fpubh.2024.1343734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
Introduction Numerous COVID-19-related apps were widely used during the COVID-19 pandemic. Among them, those supporting epidemiological investigations were particularly useful. This study explored the effectiveness of apps that support epidemiological investigations, factors influencing users' intention to use them, and ways to encourage their use. Methods We developed and evaluated the KODARI app to demonstrate its importance in epidemiological investigations. After adapting a questionnaire based on an existing evaluation framework for COVID-19-related apps, we collected data from 276 participants through an online survey conducted between April 28 and May 25, 2023. We conducted two independent sample t-tests to determine the differences between each variable according to demographic characteristics and a multiple regression analysis to identify factors affecting intention to use. Results Users were generally satisfied with the KODARI. We observed differences in sex, age, marital status, occupational characteristics, and experience with epidemiological investigation. Females rated the app's information accuracy higher than males. Males had a higher intention to use than females. Participants aged under 35 years rated information accuracy and transparency highly, whereas single participants rated information accuracy higher than married participants. Occupational groups with frequent interactions with others evaluated their self-determination regarding the application. The app's self-determination was highly valued among participants with experience in epidemiological investigations. By investigating the factors affecting the intention to use the app, we confirmed that effectiveness, self-determination, and usability significantly affected the intention to use. Discussion This study demonstrated the effectiveness of app supporting epidemiological investigations, identified meaningful factors that influence intention to use, and confirmed the applicability of our new framework by considering the specificity of infectious disease situations such as COVID-19. This study provides a new basis for future epidemiological studies.
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Affiliation(s)
- Sihyun Song
- Department of Healthcare Service Management, Graduate School of Health and Welfare, Dankook University, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Jihwan Park
- College of Liberal Arts, Dankook University, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Mi Jung Rho
- College of Health Science, Dankook University, Cheonan-si, Chungcheongnam-do, Republic of Korea
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5
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He K, Foerster S, Vora NM, Blaney K, Keeley C, Hendricks L, Varma JK, Long T, Shaman J, Pei S. Evaluating completion rates of COVID-19 contact tracing surveys in New York City. BMC Public Health 2024; 24:414. [PMID: 38331772 PMCID: PMC10854191 DOI: 10.1186/s12889-024-17920-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/29/2024] [Indexed: 02/10/2024] Open
Abstract
IMPORTANCE Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. OBJECTIVE To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. METHODS We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. RESULTS The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%. CONCLUSION These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.
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Affiliation(s)
- Kaiyu He
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Steffen Foerster
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Neil M Vora
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Kathleen Blaney
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | | | | | - Jay K Varma
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, 10065, USA
| | - Theodore Long
- NYC Health + Hospitals, New York, NY, USA
- Department of Population Health, New York University, New York, NY, 10016, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
- Columbia Climate School, Columbia University, New York, NY, 10025, USA
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
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Heaton D, Nichele E, Clos J, Fischer JE. Perceptions of the Agency and Responsibility of the NHS COVID-19 App on Twitter: Critical Discourse Analysis. J Med Internet Res 2024; 26:e50388. [PMID: 38300688 PMCID: PMC10836414 DOI: 10.2196/50388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/20/2023] [Accepted: 10/20/2023] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Since September 2020, the National Health Service (NHS) COVID-19 contact-tracing app has been used to mitigate the spread of COVID-19 in the United Kingdom. Since its launch, this app has been a part of the discussion regarding the perceived social agency of decision-making algorithms. On the social media website Twitter, a plethora of views about the app have been found but only analyzed for sentiment and topic trajectories thus far, leaving the perceived social agency of the app underexplored. OBJECTIVE We aimed to examine the discussion of social agency in social media public discourse regarding algorithm-operated decisions, particularly when the artificial intelligence agency responsible for specific information systems is not openly disclosed in an example such as the COVID-19 contact-tracing app. To do this, we analyzed the presentation of the NHS COVID-19 App on Twitter, focusing on the portrayal of social agency and the impact of its deployment on society. We also aimed to discover what the presentation of social agents communicates about the perceived responsibility of the app. METHODS Using corpus linguistics and critical discourse analysis, underpinned by social actor representation, we used the link between grammatical and social agency and analyzed a corpus of 118,316 tweets from September 2020 to July 2021 to see whether the app was portrayed as a social actor. RESULTS We found that active presentations of the app-seen mainly through personalization and agency metaphor-dominated the discourse. The app was presented as a social actor in 96% of the cases considered and grew in proportion to passive presentations over time. These active presentations showed the app to be a social actor in 5 main ways: informing, instructing, providing permission, disrupting, and functioning. We found a small number of occasions on which the app was presented passively through backgrounding and exclusion. CONCLUSIONS Twitter users presented the NHS COVID-19 App as an active social actor with a clear sense of social agency. The study also revealed that Twitter users perceived the app as responsible for their welfare, particularly when it provided instructions or permission, and this perception remained consistent throughout the discourse, particularly during significant events. Overall, this study contributes to understanding how social agency is discussed in social media discourse related to algorithmic-operated decisions This research offers valuable insights into public perceptions of decision-making digital contact-tracing health care technologies and their perceptions on the web, which, even in a postpandemic world, may shed light on how the public might respond to forthcoming interventions.
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Affiliation(s)
- Dan Heaton
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Elena Nichele
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
- Lincoln International Business School, University of Lincoln, United Kingdom
| | - Jérémie Clos
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Joel E Fischer
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
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Ferretti L, Wymant C, Petrie J, Tsallis D, Kendall M, Ledda A, Di Lauro F, Fowler A, Di Francia A, Panovska-Griffiths J, Abeler-Dörner L, Charalambides M, Briers M, Fraser C. Digital measurement of SARS-CoV-2 transmission risk from 7 million contacts. Nature 2024; 626:145-150. [PMID: 38122820 PMCID: PMC10830410 DOI: 10.1038/s41586-023-06952-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023]
Abstract
How likely is it to become infected by SARS-CoV-2 after being exposed? Almost everyone wondered about this question during the COVID-19 pandemic. Contact-tracing apps1,2 recorded measurements of proximity3 and duration between nearby smartphones. Contacts-individuals exposed to confirmed cases-were notified according to public health policies such as the 2 m, 15 min guideline4,5, despite limited evidence supporting this threshold. Here we analysed 7 million contacts notified by the National Health Service COVID-19 app6,7 in England and Wales to infer how app measurements translated to actual transmissions. Empirical metrics and statistical modelling showed a strong relation between app-computed risk scores and actual transmission probability. Longer exposures at greater distances had risk similar to that of shorter exposures at closer distances. The probability of transmission confirmed by a reported positive test increased initially linearly with duration of exposure (1.1% per hour) and continued increasing over several days. Whereas most exposures were short (median 0.7 h, interquartile range 0.4-1.6), transmissions typically resulted from exposures lasting between 1 h and several days (median 6 h, interquartile range 1.4-28). Households accounted for about 6% of contacts but 40% of transmissions. With sufficient preparation, privacy-preserving yet precise analyses of risk that would inform public health measures, based on digital contact tracing, could be performed within weeks of the emergence of a new pathogen.
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Affiliation(s)
- Luca Ferretti
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK.
| | - Chris Wymant
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK
| | - James Petrie
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK
| | | | | | | | - Francesco Di Lauro
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK
| | - Adam Fowler
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK
| | | | - Jasmina Panovska-Griffiths
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK
- UK Health Security Agency, London, UK
| | - Lucie Abeler-Dörner
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK
| | | | | | - Christophe Fraser
- Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford, UK.
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Benzler J. Contact-tracing app predicts risk of SARS-CoV-2 transmission. Nature 2024; 626:42-43. [PMID: 38129614 DOI: 10.1038/d41586-023-04063-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
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Garavand A, Ameri F, Salehi F, Talebi AH, Karbasi Z, Sabahi A. A Systematic Review of Health Management Mobile Applications in COVID-19 Pandemic: Features, Advantages, and Disadvantages. BIOMED RESEARCH INTERNATIONAL 2024; 2024:8814869. [PMID: 38230030 PMCID: PMC10791194 DOI: 10.1155/2024/8814869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 12/01/2023] [Accepted: 12/28/2023] [Indexed: 01/18/2024]
Abstract
Introduction With the increasing accessibility of smartphones, their use has been considered in healthcare services. Mobile applications have played a pivotal role in providing health services during COVID-19. This study is aimed at identifying the features, advantages, and disadvantages of health management mobile applications during COVID-19. Methods This systematic review was conducted in PubMed, Scopus, and Web of Science using the related keywords up to November 2021. The original articles in English about the health management mobile applications in COVID-19 were selected. The study selection was done by two researchers independently according to inclusion and exclusion criteria. Data extraction was done using a data extraction form, and the results were summarized and reported in related tables and figures. Results Finally, 12 articles were included based on the criteria. The benefits of mobile health applications for health management during COVID-19 were in four themes and 19 subthemes, and the most advantages of the application were in disease management and the possibility of recording information by users, digital tracking of calls, and data confidentiality. Furthermore, the disadvantages of them have been presented in two themes and 14 subthemes. The most common disadvantages are reduced adherence to daily symptom reports, personal interpretation of questions, and result bias. Conclusion The study results showed that mobile applications have been effective in controlling the prevalence of COVID-19 by identifying virus-infested environments, identifying and monitoring infected people, controlling social distancing, and maintaining quarantine. It is suggested that usability, ethical and security considerations, protection of personal information, and privacy of users be considered in application design and development.
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Affiliation(s)
- Ali Garavand
- Health Information Management, Department of Health Information Technology, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Fatemeh Ameri
- Health Information Technology, Student Research Committee, Department of Health Information Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Salehi
- Health Information Management, Emam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ali Hajipour Talebi
- Health Information Technology Expert, AJA University of Medical Sciences, Tehran, Iran
| | - Zahra Karbasi
- Health Information Management, School of Management and Medical Informatics, Kerman University of Medical Sciences, Kerman, Iran
| | - Azam Sabahi
- Health Information Management, Department of Health Information Technology, Ferdows School of Health and Allied Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran
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10
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Holl F, Schobel J, Swoboda WJ. Mobile Apps for COVID-19: A Systematic Review of Reviews. Healthcare (Basel) 2024; 12:139. [PMID: 38255029 PMCID: PMC10815093 DOI: 10.3390/healthcare12020139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/05/2024] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND One measure national governments took to react to the acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic was mobile applications (apps). This study aims to provide a high-level overview of published reviews of mobile apps used in association with coronavirus disease 19 (COVID-19), examine factors that contributed to the success of these apps, and provide data for further research into this topic. METHODS We conducted a systematic review of reviews (also referred to as an umbrella review) and searched two databases, Medline and Embase, for peer-reviewed reviews of COVID-19 mobile apps that were written in English and published between January 1st 2020 and April 25th 2022. RESULTS Out of the initial 17,611 studies, 24 studies were eligible for the analysis. Publication dates ranged from May 2020 to January 2022. In total, 54% (n = 13) of the studies were published in 2021, and 33% (n = 8) were published in 2020. Most reviews included in our review of reviews analyzed apps from the USA, the UK, and India. Apps from most of the African and Middle and South American countries were not analyzed in the reviews included in our study. Categorization resulted in four clusters (app overview, privacy and security, MARS rating, and miscellaneous). CONCLUSIONS Our study provides a high-level overview of 24 reviews of apps for COVID-19, identifies factors that contributed to the success of these apps, and identifies a gap in the current literature. The study provides data for further analyses and further research.
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Affiliation(s)
- Felix Holl
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, Germany; (J.S.); (W.J.S.)
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11
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Mongin D, Bürgisser N, Courvoisier DS. Time trends and modifiable factors of COVID-19 contact tracing coverage, Geneva, Switzerland, June 2020 to February 2022. Euro Surveill 2024; 29:2300228. [PMID: 38240059 PMCID: PMC10797663 DOI: 10.2807/1560-7917.es.2024.29.3.2300228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/19/2023] [Indexed: 01/22/2024] Open
Abstract
BackgroundContact tracing was one of the central non-pharmaceutical interventions implemented worldwide to control the spread of SARS-CoV-2, but its effectiveness depends on its ability to detect contacts.AimEvaluate the proportion of secondary infections captured by the contact tracing system in Geneva.MethodsWe analysed 166,892 concomitant infections occurring at the same given address from June 2020 until February 2022 using an extensive operational database of SARS-CoV-2 tests in Geneva. We used permutation to compare the total number of secondary infections occurring at the same address with that reported through manual contact tracing.ResultsContact tracing captured on average 41% of secondary infections, varying from 23% during epidemic peaks to 60% during low epidemic activity. People living in wealthy neighbourhoods were less likely to report contacts (odds ratio (OR): 1.6). People living in apartment buildings were also less likely to report contacts than those living in a house (OR: 1.1-3.1) depending on the SARS-CoV-2 variant, the building size and the presence of shops. This under-reporting of contacts in apartment buildings decreased during periods of mandatory wearing of face masks and restrictions on private gatherings.ConclusionContact tracing alone did not detect sufficient secondary infections to reduce the spread of SARS-CoV-2. Campaigns targeting specific populations, such as those in wealthy areas or apartment buildings, could enhance coverage. Additionally, measures like wearing face masks, improving ventilation and implementing restrictions on gatherings should also be considered to reduce infections resulting from interactions that may not be perceived as high risk.
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Affiliation(s)
- Denis Mongin
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nils Bürgisser
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- General internal medicine division, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
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Valdano E, Colombi D, Poletto C, Colizza V. Epidemic graph diagrams as analytics for epidemic control in the data-rich era. Nat Commun 2023; 14:8472. [PMID: 38123580 PMCID: PMC10733371 DOI: 10.1038/s41467-023-43856-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007-2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.
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Affiliation(s)
- Eugenio Valdano
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France
| | | | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121, Padova, Italy
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France.
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13
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Masel J, Petrie JIM, Bay J, Ebbers W, Sharan A, Leibrand SM, Gebhard A, Zimmerman S. Combatting SARS-CoV-2 With Digital Contact Tracing and Notification: Navigating Six Points of Failure. JMIR Public Health Surveill 2023; 9:e49560. [PMID: 38048155 PMCID: PMC10728795 DOI: 10.2196/49560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/06/2023] [Accepted: 10/24/2023] [Indexed: 12/05/2023] Open
Abstract
Digital contact tracing and notification were initially hailed as promising strategies to combat SARS-CoV-2; however, in most jurisdictions, they did not live up to their promise. To avert a given transmission event, both parties must have adopted the technology, it must detect the contact, the primary case must be promptly diagnosed, notifications must be triggered, and the secondary case must change their behavior to avoid the focal tertiary transmission event. If we approximate these as independent events, achieving a 26% reduction in the effective reproduction number Rt would require an 80% success rate at each of these 6 points of failure. Here, we review the 6 failure rates experienced by a variety of digital contact tracing and contact notification schemes, including Singapore's TraceTogether, India's Aarogya Setu, and leading implementations of the Google Apple Exposure Notification system. This leads to a number of recommendations, for example, that the narrative be framed in terms of user autonomy rather than user privacy, and that tracing/notification apps be multifunctional and integrated with testing, manual contact tracing, and the gathering of critical scientific data.
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Affiliation(s)
- Joanna Masel
- Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, AZ, United States
| | - James Ian Mackie Petrie
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jason Bay
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Wolfgang Ebbers
- Erasmus School of Social and Behavioural Sciences, Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, Netherlands
| | | | | | - Andreas Gebhard
- Temporary Contact Number Protocol (TCN) Coalition, New York, NY, United States
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14
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Baron R, Hamdiui N, Helms YB, Crutzen R, Götz HM, Stein ML. Evaluating the Added Value of Digital Contact Tracing Support Tools for Citizens: Framework Development. JMIR Res Protoc 2023; 12:e44728. [PMID: 38019583 PMCID: PMC10719815 DOI: 10.2196/44728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic revealed that with high infection rates, health services conducting contact tracing (CT) could become overburdened, leading to limited or incomplete CT. Digital CT support (DCTS) tools are designed to mimic traditional CT, by transferring a part of or all the tasks of CT into the hands of citizens. Besides saving time for health services, these tools may help to increase the number of contacts retrieved during the contact identification process, quantity and quality of contact details, and speed of the contact notification process. The added value of DCTS tools for CT is currently unknown. OBJECTIVE To help determine whether DCTS tools could improve the effectiveness of CT, this study aims to develop a framework for the comprehensive assessment of these tools. METHODS A framework containing evaluation topics, research questions, accompanying study designs, and methods was developed based on consultations with CT experts from municipal public health services and national public health authorities, complemented with scientific literature. RESULTS These efforts resulted in a framework aiming to assist with the assessment of the following aspects of CT: speed; comprehensiveness; effectiveness with regard to contact notification; positive case detection; potential workload reduction of public health professionals; demographics related to adoption and reach; and user experiences of public health professionals, index cases, and contacts. CONCLUSIONS This framework provides guidance for researchers and policy makers in designing their own evaluation studies, the findings of which can help determine how and the extent to which DCTS tools should be implemented as a CT strategy for future infectious disease outbreaks.
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Affiliation(s)
- Ruth Baron
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Nora Hamdiui
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Yannick B Helms
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Rik Crutzen
- Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Hannelore M Götz
- Department of Public Health, Municipal Public Health Service Rotterdam-Rijnmond, Rotterdam, Netherlands
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Mart L Stein
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
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15
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Fosch A, Aleta A, Moreno Y. Characterizing the role of human behavior in the effectiveness of contact-tracing applications. Front Public Health 2023; 11:1266989. [PMID: 38026393 PMCID: PMC10657191 DOI: 10.3389/fpubh.2023.1266989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded. Methods To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence. Results The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers. Discussion The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.
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Affiliation(s)
- Ariadna Fosch
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
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16
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Scholz U, Mundry R, Freund AM. Predicting the use of a COVID-19 contact tracing application: A study across two points of measurements. Appl Psychol Health Well Being 2023; 15:1673-1694. [PMID: 37339769 DOI: 10.1111/aphw.12461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
Contact tracing mobile applications (apps) were important in combating the COVID-19 pandemic. Most previous studies predicting contact tracing app use were cross-sectional and not theory-based. This study aimed at contributing to a better understanding of app use intentions and app use by applying an extended version of the protection motivation theory across two measurement points while accounting for the development of the pandemic. A total of N = 1525 participants from Switzerland (Mage = 53.70, SD = 18.73; 47% female; n = 270 completed both assessments) reported on risk perceptions, response efficacy, self-efficacy, social norms, trust in government, trust in the healthcare system, active search of COVID-19-related information, intentions for and actual (self-reported) app use. Analyses included country-specific incidences and death toll. Increases in response-efficacy, self-efficacy, trust in government, and the active search of COVID-19-related information predicted increased app-use intentions. Increases in self-efficacy, intentions, and the active search of COVID-19-related information predicted increased self-reported app use. Risk perceptions, incidence, and death toll were unrelated to both outcomes. Across an aggravation of the pandemic situation, intentions for and app use were primarily related to response-efficacy, self-efficacy, trust in government, and the active search of COVID-19-related information.
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Affiliation(s)
- Urte Scholz
- Department of Psychology, University of Zurich, Zurich, Switzerland
- University Research Priority Program Dynamic of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Roger Mundry
- Cognitive Ethology Laboratory, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
- Department for Primate Cognition, Georg-August-University Göttingen, Göttingen, Germany
- Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Alexandra M Freund
- Department of Psychology, University of Zurich, Zurich, Switzerland
- University Research Priority Program Dynamic of Healthy Aging, University of Zurich, Zurich, Switzerland
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17
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Geenen C, Raymenants J, Gorissen S, Thibaut J, McVernon J, Lorent N, André E. Individual level analysis of digital proximity tracing for COVID-19 in Belgium highlights major bottlenecks. Nat Commun 2023; 14:6717. [PMID: 37872213 PMCID: PMC10593825 DOI: 10.1038/s41467-023-42518-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/12/2023] [Indexed: 10/25/2023] Open
Abstract
To complement labour-intensive conventional contact tracing, digital proximity tracing was implemented widely during the COVID-19 pandemic. However, the privacy-centred design of the dominant Google-Apple exposure notification framework has hindered assessment of its effectiveness. Between October 2021 and January 2022, we systematically collected app use and notification receipt data within a test and trace programme targeting around 50,000 university students in Leuven, Belgium. Due to low success rates in each studied step of the digital notification cascade, only 4.3% of exposed contacts (CI: 2.8-6.1%) received such notifications, resulting in 10 times more cases detected through conventional contact tracing. Moreover, the infection risk of digitally traced contacts (5.0%; CI: 3.0-7.7%) was lower than that of conventionally traced non-app users (9.8%; CI: 8.8-10.7%; p = 0.002). Contrary to common perception as near instantaneous, there was a 1.2-day delay (CI: 0.6-2.2) between case PCR result and digital contact notification. These results highlight major limitations of a digital proximity tracing system based on the dominant framework.
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Affiliation(s)
- Caspar Geenen
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium.
| | - Joren Raymenants
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Sarah Gorissen
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
| | - Jonathan Thibaut
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
| | - Jodie McVernon
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Victorian Infectious Diseases Laboratory Epidemiology Unit, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Natalie Lorent
- University Hospitals Leuven, Respiratory Diseases, Leuven, Belgium
- KU Leuven, Dept of CHROMETA, Laboratory of Thoracic Surgery and Respiratory Diseases (BREATHE), Leuven, Belgium
| | - Emmanuel André
- KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium
- University Hospitals Leuven, Laboratory Medicine, Leuven, Belgium
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18
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Littlecott H, Herd C, O'Rourke J, Chaparro LT, Keeling M, James Rubin G, Fearon E. Effectiveness of testing, contact tracing and isolation interventions among the general population on reducing transmission of SARS-CoV-2: a systematic review. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230131. [PMID: 37611628 PMCID: PMC10446909 DOI: 10.1098/rsta.2023.0131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
We conducted a systematic literature review of general population testing, contact tracing, case isolation and contact quarantine interventions to assess their effectiveness in reducing SARS-CoV-2 transmission, as implemented in real-world settings. We designed a broad search strategy and aimed to identify peer-reviewed studies of any design provided there was a quantitative measure of effectiveness on a transmission outcome. Studies that assessed the effect of testing or diagnosis on disease outcomes via treatment, but did not assess a transmission outcome, were not included. We focused on interventions implemented among the general population rather than in specific settings; these were from anywhere in the world and published any time after 1 January 2020 until the end of 2022. From 26 720 titles and abstracts, 1181 were reviewed as full text, and 25 met our inclusion criteria. These 25 studies included one randomized control trial (RCT) and the remaining 24 analysed empirical data and made some attempt to control for confounding. Studies included were categorized by the type of intervention: contact tracing (seven studies); specific testing strategies (12 studies); strategies for isolating cases/contacts (four studies); and 'test, trace, isolate' (TTI) as a part of a package of interventions (two studies). None of the 25 studies were rated at low risk of bias and many were rated as serious risk of bias, particularly due to the likely presence of uncontrolled confounding factors, which was a major challenge in assessing the independent effects of TTI in observational studies. These confounding factors are to be expected from observational studies during an on-going pandemic, when the emphasis was on reducing the epidemic burden rather than trial design. Findings from these 25 studies suggested an important public health role for testing followed by isolation, especially where mass and serial testing was used to reduce transmission. Some of the most compelling analyses came from examining fine-grained within-country data on contact tracing; while broader studies which compared behaviour between countries also often found TTI led to reduced transmission and mortality, this was not universal. There was limited evidence for the benefit of isolation of cases/contacts away from the home environment. One study, an RCT, showed that daily testing of contacts could be a viable strategy to replace lengthy quarantine of contacts. Based on the scarcity of robust empirical evidence, we were not able to draw any firm quantitative conclusions about the quantitative impact of TTI interventions in different epidemic contexts. While the majority of studies found that testing, tracing and isolation reduced transmission, evidence for the scale of this impact is only available for specific scenarios and hence is not necessarily generalizable. Our review therefore emphasizes the need to conduct robust experimental studies that help inform the likely quantitative impact of different TTI interventions on transmission and their optimal design. Work is needed to support such studies in the context of future emerging epidemics, along with assessments of the cost-effectiveness of TTI interventions, which was beyond the scope of this review but will be critical to decision-making. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Hannah Littlecott
- Institute for Medical Information Processing, Biometry and Epidemiology—IBE, Chair of Public Health and Health Services Research, LMU Munich, Germany
| | - Clare Herd
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
| | - John O'Rourke
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
| | - Lina Toncon Chaparro
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
| | - Matt Keeling
- Zeeman Institute (SBIDER), Mathematics Institute and School of Life Sciences, University of Warwick, Coventry, UK
- JUNIPER consortium, UK
| | - G. James Rubin
- Department of Psychological Medicine, King's College London, London, UK
| | - Elizabeth Fearon
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
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Kleber Cabral Silva H, Silva Cardoso C, Di Lorenzo Oliveira C, Carrilho Menezes A, Avelar Maia Seixas AF, Machado Rocha G. Validation of a Satisfaction Scale with a Telemedicine COVID-19 Service: Satis-COVID. Telemed J E Health 2023; 29:1514-1522. [PMID: 37022788 DOI: 10.1089/tmj.2022.0473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
Objectives: Despite being a widespread tool, telehealth was significantly incorporated during the COVID-19 pandemic period, but it still lacks analysis methodologies, greater digital security, and satisfaction assessment instruments that are still little explored and validated. The objective is to assess user satisfaction through the validation of a satisfaction scale with a telemedicine COVID-19 service (TeleCOVID). Methods: Cross-sectional study of a cohort of confirmed COVID-19 cases evaluated and monitored by the TeleCOVID team. To study the scale's measurement qualities, a factorial analysis was performed to test the validity of the construct. Correlation between items and the global scale was assessed using Spearman's correlation coefficient, and the instrument's internal consistency was assessed using Cronbach's alpha coefficient. Results: There were 1,181 respondents evaluating the care received from the TeleCOVID project. A total of 61.6% were female, and 62.4% aged between 30 and 59 years. The correlation coefficients indicated a good correlation between the items present in the instrument. The internal consistency of the global scale was high (Cronbach's alpha = 0.903) and the item-total correlations for the scale ranged from 0.563 to 0.820. The average overall user satisfaction was 4.58, based upon a 5-point Likert scale where 5 is the highest level of satisfaction. Conclusions: The results presented here show how much telehealth can contribute to improving access, resolutibility, and quality of care to the population in general in Public Health Care. In view of the results found, it can be said that the TeleCOVID team offered excellent care and fulfilled its proposed objectives. The scale fulfills its objective of evaluating the quality of teleservice, bringing good results in terms of validity and reliability, in addition to showing high levels of user satisfaction.
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20
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Daniore P, Moser A, Höglinger M, Probst Hensch N, Imboden M, Vermes T, Keidel D, Bochud M, Ortega Herrero N, Baggio S, Chocano-Bedoya P, Rodondi N, Tancredi S, Wagner C, Cullati S, Stringhini S, Gonseth Nusslé S, Veys-Takeuchi C, Zuppinger C, Harju E, Michel G, Frank I, Kahlert CR, Albanese E, Crivelli L, Levati S, Amati R, Kaufmann M, Geigges M, Ballouz T, Frei A, Fehr J, von Wyl V. Interplay of Digital Proximity App Use and SARS-CoV-2 Vaccine Uptake in Switzerland: Analysis of Two Population-Based Cohort Studies. Int J Public Health 2023; 68:1605812. [PMID: 37799349 PMCID: PMC10549773 DOI: 10.3389/ijph.2023.1605812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 08/18/2023] [Indexed: 10/07/2023] Open
Abstract
Objectives: Our study aims to evaluate developments in vaccine uptake and digital proximity tracing app use in a localized context of the SARS-CoV-2 pandemic. Methods: We report findings from two population-based longitudinal cohorts in Switzerland from January to December 2021. Failure time analyses and Cox proportional hazards regression models were conducted to assess vaccine uptake and digital proximity tracing app (SwissCovid) uninstalling outcomes. Results: We observed a dichotomy of individuals who did not use the SwissCovid app and did not get vaccinated, and who used the SwissCovid app and got vaccinated during the study period. Increased vaccine uptake was observed with SwissCovid app use (aHR, 1.51; 95% CI: 1.40-1.62 [CI-DFU]; aHR, 1.79; 95% CI: 1.62-1.99 [CSM]) compared to SwissCovid app non-use. Decreased SwissCovid uninstallation risk was observed for participants who got vaccinated (aHR, 0.55; 95% CI: 0.38-0.81 [CI-DFU]; aHR, 0.45; 95% CI: 0.27-0.78 [CSM]) compared to participants who did not get vaccinated. Conclusion: In evolving epidemic contexts, these findings underscore the need for communication strategies as well as flexible digital proximity tracing app adjustments that accommodate different preventive measures and their anticipated interactions.
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Affiliation(s)
- Paola Daniore
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - André Moser
- Clinical Trials Unit Bern, University of Bern, Bern, Switzerland
| | - Marc Höglinger
- Winterthur Institute of Health Economics, Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Nicole Probst Hensch
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Medea Imboden
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Thomas Vermes
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Dirk Keidel
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Murielle Bochud
- Unisanté, University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Natalia Ortega Herrero
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
| | - Stéphanie Baggio
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
| | - Patricia Chocano-Bedoya
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
| | - Nicolas Rodondi
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stefano Tancredi
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
| | - Cornelia Wagner
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
| | - Stéphane Cullati
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
- Department of Readaptation and Geriatrics, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Semira Gonseth Nusslé
- Unisanté, University Center for Primary Care and Public Health, Lausanne, Switzerland
| | | | - Claire Zuppinger
- Unisanté, University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Erika Harju
- Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
- Clinical Trial Unit, Lucerne Cantonal Hospital, Lucerne, Switzerland
- School of Health Sciences, ZHAW Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Gisela Michel
- Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
| | - Irène Frank
- Clinical Trial Unit, Lucerne Cantonal Hospital, Lucerne, Switzerland
| | - Christian R. Kahlert
- Department of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
- Infectious Diseases and Hospital Epidemiology, Children’s Hospital of Eastern Switzerland, St. Gallen, Switzerland
| | - Emiliano Albanese
- Institute of Public Health, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Luca Crivelli
- Institute of Public Health, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
- Department Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland
| | - Sara Levati
- Department Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland
| | - Rebecca Amati
- Institute of Public Health, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Marco Kaufmann
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Marco Geigges
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Tala Ballouz
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Jan Fehr
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
- Division of Infectious Disease and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland
| | - Viktor von Wyl
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Division of Infectious Disease and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland
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21
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Mellor J, Overton CE, Fyles M, Chawner L, Baxter J, Baird T, Ward T. Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK. Epidemiol Infect 2023; 151:e172. [PMID: 37664991 PMCID: PMC10600913 DOI: 10.1017/s0950268823001449] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/20/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Following the end of universal testing in the UK, hospital admissions are a key measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at the National Health Service (NHS) Trust, regional and national geographies help health services plan for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospitalisations across SARS-CoV-2 waves in England. This analysis includes an evaluation of internet search volumes from Google Trends, NHS triage calls and online queries, the NHS COVID-19 app, lateral flow devices (LFDs), and the ZOE app. Data sources were analysed for their feasibility as leading indicators using Granger causality, cross-correlation, and dynamic time warping at fine spatial scales. Google Trends and NHS triages consistently temporally led admissions in most locations, with lead times ranging from 5 to 20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 app, and LFD testing, which diminished with spatial resolution, showing cross-correlation of leads between -7 and 7 days. The results indicate that novel surveillance sources can be used effectively to understand the expected healthcare burden within hospital administrative areas though the temporal and spatial heterogeneity of these relationships is a key determinant of their operational public health utility.
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Affiliation(s)
- Jonathon Mellor
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
| | - Christopher E Overton
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Martyn Fyles
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Liam Chawner
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
| | - James Baxter
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
| | - Tarrion Baird
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Thomas Ward
- UK Health Security Agency, Data, Analytics and Surveillance, Nobel House, London, UK
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22
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Aronoff-Spencer E, Mazrouee S, Graham R, Handcock MS, Nguyen K, Nebeker C, Malekinejad M, Longhurst CA. Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting. PLoS One 2023; 18:e0287368. [PMID: 37594936 PMCID: PMC10437830 DOI: 10.1371/journal.pone.0287368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 05/29/2023] [Indexed: 08/20/2023] Open
Abstract
PURPOSE Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These "Exposure Notification (EN)" systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. METHODS We investigated the potential to short-term forecast COVID-19 caseloads using data from California's implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident's smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1-7 days in the future. RESULTS Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. CONCLUSIONS This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.
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Affiliation(s)
- Eliah Aronoff-Spencer
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Sepideh Mazrouee
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Rishi Graham
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Mark S. Handcock
- University of California Los Angeles, Los Angeles, CA, United States of America
| | - Kevin Nguyen
- Herbert Wertheim School of Public Health and Longevity Sciences, University of California San Diego, La Jolla, CA, United States of America
- University of California San Diego Health, San Diego, CA, United States of America
| | - Camille Nebeker
- Herbert Wertheim School of Public Health and Longevity Sciences, University of California San Diego, La Jolla, CA, United States of America
| | - Mohsen Malekinejad
- California Department of Public Health, Sacramento, CA, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States of America
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23
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Sarhan AY. An agent-based secure privacy-preserving decentralized protocol for sharing and managing digital health passport information during crises. PeerJ Comput Sci 2023; 9:e1458. [PMID: 37547404 PMCID: PMC10403165 DOI: 10.7717/peerj-cs.1458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 06/06/2023] [Indexed: 08/08/2023]
Abstract
The aim of this article is to identify a range of changes and challenges that present-day technologies often present to contemporary societies, particularly in the context of smart city logistics, especially during crises. For example, the long-term consequences of the COVID-19 pandemic, such as life losses, economic damages, and privacy and security violations, demonstrate the extent to which the existing designs and deployments of technological means are inadequate. The article proposes a privacy-preserving, decentralized, secure protocol to safeguard individual boundaries and supply governments and public health organizations with cost-effective information, particularly regarding vaccination. The contribution of this article is threefold: (i) conducting a systematic review of most of the privacy-preserving apps and their protocols created during pandemics, and we found that most apps pose security and privacy violations. (ii) Proposing an agent-based, decentralized private set intersection (PSI) protocol for securely sharing individual digital personal and health passport information. The proposed scheme is called secure mobile digital passport agent (SMDPA). (iii) Providing a simulation measurement of the proposed protocol to assess performance. The performance result proves that SMDPA is a practical solution and better than the proposed active data bundles using secure multi-party computation (ADB-SMC), as the average CPU load for SMDPA is approximately 775 milliseconds (ms) compared to about 900 ms for ADB-SMC.
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24
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Salathé M. COVID-19 digital contact tracing worked - heed the lessons for future pandemics. Nature 2023; 619:31-33. [PMID: 37400650 DOI: 10.1038/d41586-023-02130-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
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25
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Garry M, Zajac R, Hope L, Salathé M, Levine L, Merritt TA. Hits and Misses: Digital Contact Tracing in a Pandemic. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2023:17456916231179365. [PMID: 37390338 DOI: 10.1177/17456916231179365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2023]
Abstract
Traditional contact tracing is one of the most powerful weapons people have in the battle against a pandemic, especially when vaccines do not yet exist or do not afford complete protection from infection. But the effectiveness of contact tracing hinges on its ability to find infected people quickly and obtain accurate information from them. Therefore, contact tracing inherits the challenges associated with the fallibilities of memory. Against this backdrop, digital contact tracing is the "dream scenario"-an unobtrusive, vigilant, and accurate recorder of danger that should outperform manual contact tracing on every dimension. There is reason to celebrate the success of digital contact tracing. Indeed, epidemiologists report that digital contact tracing probably reduced the incidence of COVID-19 cases by at least 25% in many countries, a feat that would have been hard to match with its manual counterpart. Yet there is also reason to speculate that digital contact tracing delivered on only a fraction of its potential because it almost completely ignored the relevant psychological science. We discuss the strengths and weaknesses of digital contact tracing, its hits and misses in the COVID-19 pandemic, and its need to be integrated with the science of human behavior.
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Affiliation(s)
| | | | - Lorraine Hope
- Department of Psychology, The University of Portsmouth
| | | | - Linda Levine
- School of Social Ecology, University California, Irvine
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26
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Renedo A, Stuart R, Kühlbrandt C, Grenfell P, McGowan CR, Miles S, Farrow S, Marston C. Community-led responses to COVID-19 within Gypsy and Traveller communities in England: A participatory qualitative research study. SSM. QUALITATIVE RESEARCH IN HEALTH 2023; 3:100280. [PMID: 37200551 PMCID: PMC10156409 DOI: 10.1016/j.ssmqr.2023.100280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/31/2023] [Accepted: 05/01/2023] [Indexed: 05/20/2023]
Abstract
Individuals were asked to play an active role in infection control in the COVID-19 pandemic. Yet while government messages emphasised taking responsibility for the public good (e.g. to protect the National Health Service), they appeared to overlook social, economic and political factors affecting the ways that people were able to respond. We co-produced participatory qualitative research with members of Gypsy and Traveller communities in England between October 2021 and February 2022 to explore how they had responded to COVID-19, its containment (test, trace, isolate) and the contextual factors affecting COVID-19 risks and responses within the communities. Gypsies and Travellers reported experiencing poor treatment from health services, police harassment, surveillance, and constrained living conditions. For these communities, claiming the right to health in an emergency required them to rely on community networks and resources. They organised collective actions to contain COVID-19 in the face of this ongoing marginalisation, such as using free government COVID-19 tests to support self-designed protective measures including community-facilitated testing and community-led contact tracing. This helped keep families and others safe while minimising engagement with formal institutions. In future emergencies, communities must be given better material, political and technical support to help them to design and implement effective community-led solutions, particularly where government institutions are untrusted or untrustworthy.
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Affiliation(s)
- Alicia Renedo
- Faculty of Public Health & Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Rachel Stuart
- College of Business, Arts and Social Sciences, Brunel University London, Kingston Lane, Middlesex, UB8 3PH, UK
| | - Charlotte Kühlbrandt
- Faculty of Public Health & Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Pippa Grenfell
- Faculty of Public Health & Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Catherine R. McGowan
- Faculty of Public Health & Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | - Sam Miles
- Faculty of Public Health & Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
| | | | - Cicely Marston
- Faculty of Public Health & Policy, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK
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27
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Murphy C, Wong JY, Cowling BJ. Nonpharmaceutical interventions for managing SARS-CoV-2. Curr Opin Pulm Med 2023; 29:184-190. [PMID: 36856551 PMCID: PMC10090342 DOI: 10.1097/mcp.0000000000000949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
PURPOSE OF REVIEW Initial response strategies to the COVID-19 pandemic were heavily reliant on nonpharmaceutical interventions (NPIs), a set of measures implemented to slow or even stop the spread of infection. Here, we reviewed key measures used during the COVID-19 pandemic. RECENT FINDINGS Some NPIs were successful in reducing the transmission of SARS-CoV-2. Personal protective measures such as face masks were widely used, and likely had some effect on transmission. The development and production of rapid antigen tests allowed self-diagnosis in the community, informing isolation and quarantine measures. Community-wide measures such as school closures, workplace closures and complete stay-at-home orders were able to reduce contacts and prevent transmission. They were widely used in the pandemic and contributed to reduce transmission in the community; however, there were also negative unintended consequences in the society and economy. SUMMARY NPIs slowed the spread of SARS-CoV-2 and are essential for pandemic preparedness and response. Understanding which measures are more effective at reducing transmission with lower costs is imperative.
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Affiliation(s)
- Caitriona Murphy
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam
| | - Jessica Y. Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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28
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Shausan A, Nazarathy Y, Dyda A. Emerging data inputs for infectious diseases surveillance and decision making. Front Digit Health 2023; 5:1131731. [PMID: 37082524 PMCID: PMC10111015 DOI: 10.3389/fdgth.2023.1131731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.
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Affiliation(s)
- Aminath Shausan
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Yoni Nazarathy
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Amalie Dyda
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
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29
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Chong KC, Li K, Guo Z, Jia KM, Leung EYM, Zhao S, Hung CT, Yam CHK, Chow TY, Dong D, Wang H, Wei Y, Yeoh EK. Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis. JMIR Public Health Surveill 2023; 9:e44251. [PMID: 36811849 PMCID: PMC9994464 DOI: 10.2196/44251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/01/2023] [Accepted: 02/14/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19. OBJECTIVE We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events. METHODS We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. RESULTS A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). CONCLUSIONS We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events.
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Affiliation(s)
- Ka Chun Chong
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Kehang Li
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Zihao Guo
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Katherine Min Jia
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Eman Yee Man Leung
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Shi Zhao
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Chi Tim Hung
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Carrie Ho Kwan Yam
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Tsz Yu Chow
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Dong Dong
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Huwen Wang
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Yuchen Wei
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
| | - Eng Kiong Yeoh
- Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, New Territories, Hong Kong
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30
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Gupta P, Maharaj T, Weiss M, Rahaman N, Alsdurf H, Minoyan N, Harnois-Leblanc S, Merckx J, Williams A, Schmidt V, St-Charles PL, Patel A, Zhang Y, Buckeridge DL, Pal C, Schölkopf B, Bengio Y. Proactive Contact Tracing. PLOS DIGITAL HEALTH 2023; 2:e0000199. [PMID: 36913342 PMCID: PMC10010527 DOI: 10.1371/journal.pdig.0000199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 01/25/2023] [Indexed: 03/14/2023]
Abstract
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting daily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as "pingdemic," may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users' infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
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Affiliation(s)
- Prateek Gupta
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- The Alan Turing Institute, London, United Kingdom
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Tegan Maharaj
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Martin Weiss
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Nasim Rahaman
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Hannah Alsdurf
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Nanor Minoyan
- Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Canada
| | - Soren Harnois-Leblanc
- Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Canada
| | - Joanna Merckx
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Canada
| | - Andrew Williams
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Victor Schmidt
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | | | - Akshay Patel
- Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Yang Zhang
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
| | - David L. Buckeridge
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Canada
| | - Christopher Pal
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
| | - Bernhard Schölkopf
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
- Fellow of the Canadian Institute for Advanced Research (CIFAR), Canada
| | - Yoshua Bengio
- Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montréal, Québec, Canada
- Fellow of the Canadian Institute for Advanced Research (CIFAR), Canada
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31
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Pozo-Martin F, Beltran Sanchez MA, Müller SA, Diaconu V, Weil K, El Bcheraoui C. Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review. Eur J Epidemiol 2023; 38:243-266. [PMID: 36795349 PMCID: PMC9932408 DOI: 10.1007/s10654-023-00963-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 12/31/2022] [Indexed: 02/17/2023]
Abstract
Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.
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Affiliation(s)
- Francisco Pozo-Martin
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany.
| | | | - Sophie Alice Müller
- Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Viorela Diaconu
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Kilian Weil
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Charbel El Bcheraoui
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
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Yoo S, Gulbransen-Diaz N, Parker C, Wang AP. Designing Digital COVID-19 Screening: Insights and Deliberations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3899. [PMID: 36900909 PMCID: PMC10001447 DOI: 10.3390/ijerph20053899] [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: 11/26/2022] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Due to the global COVID-19 pandemic, public health control and screening measures have been introduced at healthcare facilities, including those housing our most vulnerable populations. These warning measures situated at hospital entrances are presently labour-intensive, requiring additional staff to conduct manual temperature checks and risk-assessment questionnaires of every individual entering the premises. To make this process more efficient, we present eGate, a digital COVID-19 health-screening smart Internet of Things system deployed at multiple entry points around a children's hospital. This paper reports on design insights based on the experiences of concierge screening staff stationed alongside the eGate system. Our work contributes towards social-technical deliberations on how to improve design and deploy of digital health-screening systems in hospitals. It specifically outlines a series of design recommendations for future health screening interventions, key considerations relevant to digital screening control systems and their implementation, and the plausible effects on the staff who work alongside them.
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Affiliation(s)
- Soojeong Yoo
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London W1W 7TY, UK
| | - Natalia Gulbransen-Diaz
- School of Architecture, Planning and Design, The University of Sydney, Sydney, NSW 2006, Australia
| | - Callum Parker
- School of Architecture, Planning and Design, The University of Sydney, Sydney, NSW 2006, Australia
| | - Audrey P. Wang
- Biomedical Informatics and Digital Health, School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
- DHI Laboratory, Research Education Network, Western Sydney Local Health District, Westmead Health Precinct, Westmead, NSW 2145, Australia
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Epidemiological impacts of the NHS COVID-19 app in England and Wales throughout its first year. Nat Commun 2023; 14:858. [PMID: 36813770 PMCID: PMC9947127 DOI: 10.1038/s41467-023-36495-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
The NHS COVID-19 app was launched in England and Wales in September 2020, with a Bluetooth-based contact tracing functionality designed to reduce transmission of SARS-CoV-2. We show that user engagement and the app's epidemiological impacts varied according to changing social and epidemic characteristics throughout the app's first year. We describe the interaction and complementarity of manual and digital contact tracing approaches. Results of our statistical analyses of anonymised, aggregated app data include that app users who were recently notified were more likely to test positive than app users who were not recently notified, by a factor that varied considerably over time. We estimate that the app's contact tracing function alone averted about 1 million cases (sensitivity analysis 450,000-1,400,000) during its first year, corresponding to 44,000 hospital cases (SA 20,000-60,000) and 9,600 deaths (SA 4600-13,000).
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Qian W, Stanley KG, Osgood ND. Impacts of observation frequency on proximity contact data and modeled transmission dynamics. PLoS Comput Biol 2023; 19:e1010917. [PMID: 36848398 PMCID: PMC9997969 DOI: 10.1371/journal.pcbi.1010917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 03/09/2023] [Accepted: 02/03/2023] [Indexed: 03/01/2023] Open
Abstract
Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.
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Affiliation(s)
- Weicheng Qian
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- * E-mail:
| | - Kevin Gordon Stanley
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Nathaniel David Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada
- Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada
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Bannister-Tyrrell M, Chen M, Choi V, Miglietta A, Galea G. Systematic scoping review of the implementation, adoption, use, and effectiveness of digital contact tracing interventions for COVID-19 in the Western Pacific Region. THE LANCET REGIONAL HEALTH - WESTERN PACIFIC 2023; 34:100647. [DOI: 10.1016/j.lanwpc.2022.100647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/24/2022] [Accepted: 11/01/2022] [Indexed: 02/27/2023]
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Handmann E, Camanor SW, Fallah MP, Candy N, Parker D, Gries A, Grünewald T. Feasibility of digital contact tracing in low-income settings - pilot trial for a location-based DCT app. BMC Public Health 2023; 23:146. [PMID: 36670358 PMCID: PMC9859743 DOI: 10.1186/s12889-022-14888-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/16/2022] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Data about the effectiveness of digital contact tracing are based on studies conducted in countries with predominantly high- or middle-income settings. Up to now, little research is done to identify specific problems for the implementation of such technique in low-income countries. METHODS A Bluetooth-assisted GPS location-based digital contact tracing (DCT) app was tested by 141 participants during 14 days in a hospital in Monrovia, Liberia in February 2020. The DCT app was compared to a paper-based reference system. Hits between participants and 10 designated infected participants were recorded simultaneously by both methods. Additional data about GPS and Bluetooth adherence were gathered and surveys to estimate battery consumption and app adherence were conducted. DCT apps accuracy was evaluated in different settings. RESULTS GPS coordinates from 101/141 (71.6%) participants were received. The number of hours recorded by the participants during the study period, true Hours Recorded (tHR), was 496.3 h (1.1% of maximum Hours recordable) during the study period. With the paper-based method 1075 hits and with the DCT app five hits of designated infected participants with other participants have been listed. Differences between true and maximum recording times were due to failed permission settings (45%), data transmission issues (11.3%), of the participants 10.1% switched off GPS and 32.5% experienced other technical or compliance problems. In buildings, use of Bluetooth increased the accuracy of the DCT app (GPS + BT 22.9 m ± 21.6 SD vs. GPS 60.9 m ± 34.7 SD; p = 0.004). GPS accuracy in public transportation was 10.3 m ± 10.05 SD with a significant (p = 0.007) correlation between precision and phone brand. GPS resolution outdoors was 10.4 m ± 4.2 SD. CONCLUSION In our study several limitations of the DCT together with the impairment of GPS accuracy in urban settings impede the solely use of a DCT app. It could be feasible as a supplement to traditional manual contact tracing. DKRS, DRKS00029327 . Registered 20 June 2020 - Retrospectively registered.
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Affiliation(s)
- Eric Handmann
- Department for Emergency Medicine, University Hospital Leipzig, Leipzig, Germany.
| | | | - Mosoka P. Fallah
- grid.512250.1National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia
| | - Neima Candy
- grid.512250.1National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia
| | | | - André Gries
- grid.411339.d0000 0000 8517 9062Department for Emergency Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Thomas Grünewald
- grid.459629.50000 0004 0389 4214Clinic for Infectious Diseases and Tropical Medicine and Department for Hospital and Environmental Hygiene, Klinikum Chemnitz, Chemnitz, Germany
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Sun Y, Koo JR, Park M, Yi H, Dickens BL, Cook AR. Use of Bluetooth contact tracing technology to model COVID-19 quarantine policies in high-risk closed populations. Digit Health 2023; 9:20552076231178418. [PMID: 37312947 PMCID: PMC10259105 DOI: 10.1177/20552076231178418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 05/10/2023] [Indexed: 06/15/2023] Open
Abstract
Containment measures in high-risk closed settings, like migrant worker (MW) dormitories, are critical for mitigating emerging infectious disease outbreaks and protecting potentially vulnerable populations in outbreaks such as coronavirus disease 2019 (COVID-19). The direct impact of social distancing measures can be assessed through wearable contact tracing devices. Here, we developed an individual-based model using data collected through a Bluetooth wearable device that collected 33.6M and 52.8M contact events in two dormitories in Singapore, one apartment style and the other a barrack style, to assess the impact of measures to reduce the social contact of cases and their contacts. The simulation of highly detailed contact networks accounts for different infrastructural levels, including room, floor, block, and dormitory, and intensity in terms of being regular or transient. Via a branching process model, we then simulated outbreaks that matched the prevalence during the COVID-19 outbreak in the two dormitories and explored alternative scenarios for control. We found that strict isolation of all cases and quarantine of all contacts would lead to very low prevalence but that quarantining only regular contacts would lead to only marginally higher prevalence but substantially fewer total man-hours lost in quarantine. Reducing the density of contacts by 30% through the construction of additional dormitories was modelled to reduce the prevalence by 14 and 9% under smaller and larger outbreaks, respectively. Wearable contact tracing devices may be used not just for contact tracing efforts but also to inform alternative containment measures in high-risk closed settings.
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Affiliation(s)
| | | | - Minah Park
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Huso Yi
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Borame L Dickens
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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Al-Haboubi M, Exley J, Allel K, Erens B, Mays N. One year of digital contact tracing: Who was more likely to install the NHS COVID-19 app? Results from a tracker survey in England and Wales. Digit Health 2023. [DOI: 10.1177/20552076231159449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023] Open
Abstract
Objective To examine changes in the uptake of the National Health Service (NHS) COVID-19 proximity (contact) tracing application (‘app’) over one year, amongst smartphone users in England and Wales. Methods We conducted a longitudinal survey between October 2020 and September 2021, amongst an online panel representative of smartphone users aged 18–79 and a purposeful sample from six of the largest minority ethnic groups. We fitted pooled logistic regression models to examine factors associated with app installation and a longitudinal logistic regression model to estimate factors associated with installing/uninstalling the app over time. Results Around 50% of respondents had the app installed at each time point. The majority of installations took place soon after its launch. The key reason for installing at launch was ‘civic, public or social responsibility’. Amongst those who installed the app later, it was ‘needed to scan NHS QR code’. Uptake was higher amongst individuals who considered themselves vulnerable to COVID-19 or were concerned about the risk COVID-19 posed, were more highly educated, of White ethnicity, and who reported higher levels of trust in government information. Factors associated with installing the app over time included becoming more concerned about the risk COVID-19 poses to the country, or perceiving that the crisis in their local area had worsened. Conclusions Despite changes in pandemic response and case numbers, app installation in England and Wales remained relatively stable after launch. If governments wish to increase app installation and use rates in future pandemics, they need to highlight those app features that encourage engagement, and take related action to allay privacy concerns and improve trust in government information sharing.
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Affiliation(s)
- Mustafa Al-Haboubi
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Josephine Exley
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Kasim Allel
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
- Department of Disease Control, Faculty of Infectious & Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Bob Erens
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Nicholas Mays
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
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Harish V, Samson TG, Diemert L, Tuite A, Mamdani M, Khan K, McGahan A, Shaw JA, Das S, Rosella LC. Governing partnerships with technology companies as part of the COVID-19 response in Canada: A qualitative case study. PLOS DIGITAL HEALTH 2022; 1:e0000164. [PMID: 36812643 PMCID: PMC9931354 DOI: 10.1371/journal.pdig.0000164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022]
Abstract
Cross-sector partnerships are vital for maintaining resilient health systems; however, few studies have sought to empirically assess the barriers and enablers of effective and responsible partnerships during public health emergencies. Through a qualitative, multiple case study, we analyzed 210 documents and conducted 26 interviews with stakeholders in three real-world partnerships between Canadian health organizations and private technology startups during the COVID-19 pandemic. The three partnerships involved: 1) deploying a virtual care platform to care for COVID-19 patients at one hospital, 2) deploying a secure messaging platform for physicians at another hospital, and 3) using data science to support a public health organization. Our results demonstrate that a public health emergency created time and resource pressures throughout a partnership. Given these constraints, early and sustained alignment on the core problem was critical for success. Moreover, governance processes designed for normal operations, such as procurement, were triaged and streamlined. Social learning, or the process of learning from observing others, offset some time and resource pressures. Social learning took many forms ranging from informal conversations between individuals at peer organisations (e.g., hospital chief information officers) to standing meetings at the local university's city-wide COVID-19 response table. We also found that startups' flexibility and understanding of the local context enabled them to play a highly valuable role in emergency response. However, pandemic fueled "hypergrowth" created risks for startups, such as introducing opportunities for deviation away from their core value proposition. Finally, we found each partnership navigated intense workloads, burnout, and personnel turnover through the pandemic. Strong partnerships required healthy, motivated teams. Visibility into and engagement in partnership governance, belief in partnership impact, and strong emotional intelligence in managers promoted team well-being. Taken together, these findings can help to bridge the theory-to-practice gap and guide effective cross-sector partnerships during public health emergencies.
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Affiliation(s)
- Vinyas Harish
- MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Schwartz Reisman Institute for Technology and Society, Toronto, Canada
- Ethics of AI Lab, Centre for Ethics, University of Toronto, Toronto, Canada
| | - Thomas G. Samson
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Lori Diemert
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Ashleigh Tuite
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Muhammad Mamdani
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Canada
| | - Kamran Khan
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Canada
- Division of Infectious Diseases, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Anita McGahan
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Rotman School of Management, University of Toronto, Toronto, Canada
- Munk School of Global Affairs and Public Policy, University of Toronto, Toronto, Canada
| | - James A. Shaw
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Joint Centre for Bioethics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Institute for Health Systems Solutions and Virtual Care, Women’s College Hospital, Toronto, Canada
| | - Sunit Das
- Ethics of AI Lab, Centre for Ethics, University of Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, Temerty Faculty of Medicine, Toronto, Canada
| | - Laura C. Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Schwartz Reisman Institute for Technology and Society, Toronto, Canada
- Institute for Better Health, Trillium Health Partners, Mississauga, Canada
- * E-mail:
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Understanding the impact of digital contact tracing during the COVID-19 pandemic. PLOS DIGITAL HEALTH 2022; 1:e0000149. [PMID: 36812611 PMCID: PMC9931320 DOI: 10.1371/journal.pdig.0000149] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 10/23/2022] [Indexed: 12/12/2022]
Abstract
Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention's efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation.
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Pratt B, Parker M, Bull S. Equitable Design and Use of Digital Surveillance Technologies During COVID-19: Norms and Concerns. J Empir Res Hum Res Ethics 2022; 17:573-586. [PMID: 36069118 PMCID: PMC9676107 DOI: 10.1177/15562646221118127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Given the unprecedented scale of digital surveillance in the COVID-19 pandemic, designing and implementing digital technologies in ways that are equitable is critical now and in future epidemics and pandemics. Yet to date there has been very limited consideration about what is necessary to promote their equitable design and implementation. In this study, literature relating to the use of digital surveillance technologies during epidemics and pandemics was collected and thematically analyzed for ethical norms and concerns related to equity and social justice. Eleven norms are reported, including procedural fairness and inclusive approaches to design and implementation, designing to rectify or avoid exacerbating inequities, and fair access. Identified concerns relate to digital divides, stigma and discrimination, disparate risk of harm, and unfair design processes. We conclude by considering what dimensions of social justice the norms promote and whether identified concerns can be addressed by building the identified norms into technology design and implementation practice.
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Affiliation(s)
- Bridget Pratt
- Queensland Bioethics Centre, Australian Catholic University, Brisbane, Australia,School of Population and Global Health, University of Melbourne, Melbourne, Australia,Bridget Pratt, Queensland Bioethics Centre, Australian Catholic University, 1100 Nudgee Rd, Brisbane, Australia.
| | - Michael Parker
- The Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Susan Bull
- The Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK,Department of Psychological Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
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Chen GJ, Palmer JR, Bartumeus F, Alba-Casals A. Modeling the impact of surveillance activities combined with physical distancing interventions on COVID-19 epidemics at a local level. Infect Dis Model 2022; 7:811-822. [PMID: 36411772 PMCID: PMC9670679 DOI: 10.1016/j.idm.2022.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/26/2022] [Accepted: 11/03/2022] [Indexed: 11/18/2022] Open
Abstract
Physical distancing and contact tracing are two key components in controlling the COVID-19 epidemics. Understanding their interaction at local level is important for policymakers. We propose a flexible modeling framework to assess the effect of combining contact tracing with different physical distancing strategies. Using scenario tree analyses, we compute the probability of COVID-19 detection using passive surveillance, with and without contact tracing, in metropolitan Barcelona. The estimates of detection probability and the frequency of daily social contacts are fitted into an age-structured susceptible-exposed-infectious-recovered compartmental model to simulate the epidemics considering different physical distancing scenarios over a period of 26 weeks. With the original Wuhan strain, the probability of detecting an infected individual without implementing physical distancing would have been 0.465, 0.515, 0.617, and 0.665 in designated age groups (0-14, 15-49, 50-64, and >65), respectively. As the physical distancing measures were reinforced and the disease circulation decreased, the interaction between the two interventions resulted in a reduction of the detection probabilities; however, despite this reduction, active contact tracing and isolation remained an effective supplement to physical distancing. If we relied solely on passive surveillance for diagnosing COVID-19, the model required a minimal 50% (95% credible interval, 39-69%) reduction of daily social contacts to keep the infected population under 5%, as compared to the 36% (95% credible interval, 22-56%) reduction with contact tracing systems. The simulation with the B.1.1.7 and B.1.167.2 strains shows similar results. Our simulations showed that a functioning contact tracing program would reduce the need for physical distancing and mitigate the COVID-19 epidemics.
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Affiliation(s)
- Guan-Jhou Chen
- College of Medicine, National Taiwan University, Taipei, Taiwan
- Min-Sheng General Hospital, Taoyuan, Taiwan
| | - John R.B. Palmer
- Department of Political and Social Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Frederic Bartumeus
- Centre d’Estudis Avançats de Blanes (CEAB-CSIC), Blanes, 17300, Spain
- Centre de Recerca Ecològica i Aplicacions Forestals (CREAF), Cerdanyola del Vallès, 08193, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, 08010, Spain
| | - Ana Alba-Casals
- Centre de Recerca en Sanitat Animal (CReSA), Institut de Recerca i Tecnologia Agroalimentàries, Spain
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A Bayesian generative neural network framework for epidemic inference problems. Sci Rep 2022; 12:19673. [PMID: 36385141 PMCID: PMC9667449 DOI: 10.1038/s41598-022-20898-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/20/2022] [Indexed: 11/17/2022] Open
Abstract
The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals (i.e., contact tracing), the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations (e.g., medical tests), epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better (in some cases, significantly better) or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals.
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Hohmuth N, Khanyaree I, Lang AL, Duering O, Konigorski S, Višković V, Heising T, Egender F, Remschmidt C, Leistner R. Participatory disease surveillance for a mass gathering — a prospective cohort study on COVID-19, Germany 2021. BMC Public Health 2022; 22:2074. [PMID: 36376856 PMCID: PMC9660174 DOI: 10.1186/s12889-022-14505-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/31/2022] [Indexed: 11/16/2022] Open
Abstract
Background Mass gatherings (MGs) such as music festivals and sports events have been associated with a high risk of SARS-CoV-2 transmission. On-site research can foster knowledge of risk factors for infections and improve risk assessments and precautionary measures at future MGs. We tested a web-based participatory disease surveillance tool to detect COVID-19 infections at and after an outdoor MG by collecting self-reported COVID-19 symptoms and tests. Methods We conducted a digital prospective observational cohort study among fully immunized attendees of a sports festival that took place from September 2 to 5, 2021 in Saxony-Anhalt, Germany. Participants used our study app to report demographic data, COVID-19 tests, symptoms, and their contact behavior. This self-reported data was used to define probable and confirmed COVID-19 cases for the full “study period” (08/12/2021 – 10/31/2021) and within the 14-day “surveillance period” during and after the MG, with the highest likelihood of an MG-related COVID-19 outbreak (09/04/2021 – 09/17/2021). Results A total of 2,808 of 9,242 (30.4%) event attendees participated in the study. Within the study period, 776 individual symptoms and 5,255 COVID-19 tests were reported. During the 14-day surveillance period around and after the MG, seven probable and seven PCR-confirmed COVID-19 cases were detected. The confirmed cases translated to an estimated seven-day incidence of 125 per 100,000 participants (95% CI [67.7/100,000, 223/100,000]), which was comparable to the average age-matched incidence in Germany during this time. Overall, weekly numbers of COVID-19 cases were fluctuating over the study period, with another increase at the end of the study period. Conclusion COVID-19 cases attributable to the mass gathering were comparable to the Germany-wide age-matched incidence, implicating that our active participatory disease surveillance tool was able to detect MG-related infections. Further studies are needed to evaluate and apply our participatory disease surveillance tool in other mass gathering settings. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-14505-x.
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Affiliation(s)
- Nils Hohmuth
- Data4Life gGmbH, Charlottenstraße 13, 10969 Berlin, Germany ,grid.6363.00000 0001 2218 4662Medizinische Klinik für Gastroenterologie-, Infektiologie-, und Rheumatologie, Charité University Medicine Berlin, Campus Benjamin Franklin, Berlin, Germany
| | | | - Anna-Lena Lang
- Data4Life gGmbH, Charlottenstraße 13, 10969 Berlin, Germany
| | - Ohad Duering
- Data4Life gGmbH, Charlottenstraße 13, 10969 Berlin, Germany
| | - Stefan Konigorski
- grid.11348.3f0000 0001 0942 1117Digital Health Center, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany ,grid.59734.3c0000 0001 0670 2351Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Tobias Heising
- Medizinisches Versorgungszentrum Bohmte, Bremer Str. 37, 49163 Bohmte, Germany
| | - Friedemann Egender
- grid.6363.00000 0001 2218 4662Medizinische Klinik für Nephrologie und Intensivmedizin, Charité University Medicine Berlin, Campus Virchow, Berlin, Germany
| | | | - Rasmus Leistner
- grid.6363.00000 0001 2218 4662Medizinische Klinik für Gastroenterologie-, Infektiologie-, und Rheumatologie, Charité University Medicine Berlin, Campus Benjamin Franklin, Berlin, Germany
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Daniore P, Nittas V, Ballouz T, Menges D, Moser A, Höglinger M, Villiger P, Schmitz-Grosz K, Von Wyl V. Performance of the Swiss Digital Contact-Tracing App Over Various SARS-CoV-2 Pandemic Waves: Repeated Cross-sectional Analyses. JMIR Public Health Surveill 2022; 8:e41004. [PMID: 36219833 PMCID: PMC9700234 DOI: 10.2196/41004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/28/2022] [Accepted: 10/09/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Digital proximity-tracing apps have been deployed in multiple countries to assist with SARS-CoV-2 pandemic mitigation efforts. However, it is unclear how their performance and effectiveness were affected by changing pandemic contexts and new viral variants of concern. OBJECTIVE The aim of this study is to bridge these knowledge gaps through a countrywide digital proximity-tracing app effectiveness assessment, as guided by the World Health Organization/European Center for Prevention and Disease Control (WHO/ECDC) indicator framework to evaluate the public health effectiveness of digital proximity-tracing solutions. METHODS We performed a descriptive analysis of the digital proximity-tracing app SwissCovid in Switzerland for 3 different periods where different SARS-CoV-2 variants of concern (ie, Alpha, Delta, and Omicron, respectively) were most prevalent. In our study, we refer to the indicator framework for the evaluation of public health effectiveness of digital proximity-tracing apps of the WHO/ECDC. We applied this framework to compare the performance and effectiveness indicators of the SwissCovid app. RESULTS Average daily registered SARS-CoV-2 case rates during our assessment period from January 25, 2021, to March 19, 2022, were 20 (Alpha), 54 (Delta), and 350 (Omicron) per 100,000 inhabitants. The percentages of overall entered authentication codes from positive tests into the SwissCovid app were 9.9% (20,273/204,741), 3.9% (14,372/365,846), and 4.6% (72,324/1,581,506) during the Alpha, Delta, and Omicron variant phases, respectively. Following receipt of an exposure notification from the SwissCovid app, 58% (37/64, Alpha), 44% (7/16, Delta), and 73% (27/37, Omicron) of app users sought testing or performed self-tests. Test positivity among these exposure-notified individuals was 19% (7/37) in the Alpha variant phase, 29% (2/7) in the Delta variant phase, and 41% (11/27) in the Omicron variant phase compared to 6.1% (228,103/3,755,205), 12% (413,685/3,443,364), and 41.7% (1,784,951/4,285,549) in the general population, respectively. In addition, 31% (20/64, Alpha), 19% (3/16, Delta), and 30% (11/37, Omicron) of exposure-notified app users reported receiving mandatory quarantine orders by manual contact tracing or through a recommendation by a health care professional. CONCLUSIONS In constantly evolving pandemic contexts, the effectiveness of digital proximity-tracing apps in contributing to mitigating pandemic spread should be reviewed regularly and adapted based on changing requirements. The WHO/ECDC framework allowed us to assess relevant domains of digital proximity tracing in a holistic and systematic approach. Although the Swisscovid app mostly worked, as reasonably expected, our analysis revealed room for optimizations and further performance improvements. Future implementation of digital proximity-tracing apps should place more emphasis on social, psychological, and organizational aspects to reduce bottlenecks and facilitate their use in pandemic contexts.
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Affiliation(s)
- Paola Daniore
- Institute for Implementation Science in Healthcare, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - Vasileios Nittas
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Tala Ballouz
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Dominik Menges
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - André Moser
- Clinical Trials Unit, University of Bern, Bern, Switzerland
| | - Marc Höglinger
- Winterthur Institute of Health Economics, Zurich University of Applied Sciences, Winterthur, Switzerland
| | | | | | - Viktor Von Wyl
- Institute for Implementation Science in Healthcare, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach. Inf Process Manag 2022; 59:103095. [PMID: 36119754 PMCID: PMC9464588 DOI: 10.1016/j.ipm.2022.103095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/09/2022] [Accepted: 09/04/2022] [Indexed: 01/17/2023]
Abstract
Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information.
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Aronoff-Spencer E, Nebeker C, Wenzel AT, Nguyen K, Kunowski R, Zhu M, Adamos G, Goyal R, Mazrouee S, Reyes A, May N, Howard H, Longhurst CA, Malekinejad M. Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify). Public Health Rep 2022; 137:67S-75S. [PMID: 36314660 PMCID: PMC9678789 DOI: 10.1177/00333549221129354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic. MATERIALS AND METHODS California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy. RESULTS We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2. PRACTICE IMPLICATIONS Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.
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Affiliation(s)
- Eliah Aronoff-Spencer
- Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego, La Jolla, CA, USA
- University of California San Diego Health, La Jolla, CA, USA
- The Design Lab, University of California San Diego, La Jolla, CA, USA
| | - Camille Nebeker
- The Design Lab, University of California San Diego, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Alexander T. Wenzel
- Department of Biomedical Informatics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kevin Nguyen
- University of California San Diego Health, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Rachel Kunowski
- University of California San Diego Health, La Jolla, CA, USA
| | - Mingjia Zhu
- University of California San Diego Health, La Jolla, CA, USA
| | - Gary Adamos
- University of California San Diego Health, La Jolla, CA, USA
| | - Ravi Goyal
- Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sepideh Mazrouee
- Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Aaron Reyes
- University of California San Diego Health, La Jolla, CA, USA
| | - Nicole May
- University of California San Diego Health, La Jolla, CA, USA
| | - Holly Howard
- California Connected, Center for Infectious Diseases, California Department of Public Health, Richmond, CA, USA
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Christopher A. Longhurst
- Department of Biomedical Informatics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Mohsen Malekinejad
- California Connected, Center for Infectious Diseases, California Department of Public Health, Richmond, CA, USA
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
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Aringhieri R, Bigharaz S, Druetto A, Duma D, Grosso A, Guastalla A. The daily swab test collection problem. ANNALS OF OPERATIONS RESEARCH 2022:1-22. [PMID: 36320865 PMCID: PMC9612627 DOI: 10.1007/s10479-022-05019-1] [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: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science 10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT.
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Affiliation(s)
- Roberto Aringhieri
- Dipartimento di Informatica, Università degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Sara Bigharaz
- Department of Industrial Economics and Technology Management, Faculty of Economics and Management, NTNU, 7491 Trondheim, Norway
| | - Alessandro Druetto
- Dipartimento di Informatica, Università degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Davide Duma
- Dipartimento di Matematica “Felice Casorati”, Università degli Studi di Pavia, via Adolfo Ferrata, 5, 27100 Pavia, Italy
| | - Andrea Grosso
- Dipartimento di Informatica, Università degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
| | - Alberto Guastalla
- Dipartimento di Informatica, Università degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy
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49
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Segal CD, Lober WB, Revere D, Lorigan D, Karras BT, Baseman JG. Trading-off privacy and utility: the Washington State experience assessing the performance of a public health digital exposure notification system for coronavirus disease 2019. J Am Med Inform Assoc 2022; 29:2050-2056. [PMID: 36206130 DOI: 10.1093/jamia/ocac178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/16/2022] [Accepted: 08/28/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Digital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State's experience utilizing these constructs to design data-driven evaluation approaches. METHODS We conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs. RESULTS We identified 8 useful performance indicators within reach, engagement, and effectiveness constructs. DISCUSSION We use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure. CONCLUSION Continued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies.
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Affiliation(s)
- Courtney D Segal
- Department of Health Systems and Population Health, University of Washington, Seattle, Washington DC, USA
| | - William B Lober
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, Washington DC, USA.,Clinical Informatics Research Group, University of Washington, Seattle, Washington DC, USA
| | - Debra Revere
- Department of Health Systems and Population Health, University of Washington, Seattle, Washington DC, USA
| | - Daniel Lorigan
- Clinical Informatics Research Group, University of Washington, Seattle, Washington DC, USA
| | - Bryant T Karras
- Washington State Department of Health, Olympia, Washington DC, USA
| | - Janet G Baseman
- Department of Epidemiology, University of Washington, Seattle, Washington DC, USA
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50
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Panovska-Griffiths J, Swallow B, Hinch R, Cohen J, Rosenfeld K, Stuart RM, Ferretti L, Di Lauro F, Wymant C, Izzo A, Waites W, Viner R, Bonell C, Fraser C, Klein D, Kerr CC. Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022. [PMID: 35965458 DOI: 10.6084/m9.figshare.c.6070427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The English SARS-CoV-2 epidemic has been affected by the emergence of new viral variants such as B.1.177, Alpha and Delta, and changing restrictions. We used statistical models and the agent-based model Covasim, in June 2021, to estimate B.1.177 to be 20% more transmissible than the wild type, Alpha to be 50-80% more transmissible than B.1.177 and Delta to be 65-90% more transmissible than Alpha. Using these estimates in Covasim (calibrated 1 September 2020 to 20 June 2021), in June 2021, we found that due to the high transmissibility of Delta, resurgence in infections driven by the Delta variant would not be prevented, but would be strongly reduced by delaying the relaxation of restrictions by one month and with continued vaccination. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- J Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
- The Queen's College, University of Oxford, Oxford
| | - B Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - R Hinch
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - J Cohen
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - K Rosenfeld
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - R M Stuart
- University of Copenhagen, Copenhagen, Denmark
| | - L Ferretti
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - F Di Lauro
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - C Wymant
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - A Izzo
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - W Waites
- Department of Public Health, Environments & Society, London School of Hygiene and Tropical Medicine, London, UK
- Department of Computer and Information Sciences, University of Strathclyde, G1 1XH Glasgow, UK
| | - R Viner
- UCL Great Ormond St. Institute of Child Health, University College London, London, UK
| | - C Bonell
- Department of Public Health, Environments & Society, London School of Hygiene and Tropical Medicine, London, UK
| | - C Fraser
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford
| | - D Klein
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - C C Kerr
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
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