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Dall’Amico L, Kleynhans J, Gauvin L, Tizzoni M, Ozella L, Makhasi M, Wolter N, Language B, Wagner RG, Cohen C, Tempia S, Cattuto C. Estimating household contact matrices structure from easily collectable metadata. PLoS One 2024; 19:e0296810. [PMID: 38483886 PMCID: PMC10939291 DOI: 10.1371/journal.pone.0296810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/18/2023] [Indexed: 03/17/2024] Open
Abstract
Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure.
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Affiliation(s)
| | - Jackie Kleynhans
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Laetitia Gauvin
- ISI Foundation, Turin, Italy
- Institute for Research on sustainable Development, UMR215 PRODIG, Aubervilliers, France
| | - Michele Tizzoni
- ISI Foundation, Turin, Italy
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | | | - Mvuyo Makhasi
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
| | - Nicole Wolter
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - Brigitte Language
- Unit for Environmental Science and Management, Climatology Research Group, North-West University, Potchefstroom, South Africa
| | - Ryan G. Wagner
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Agincourt, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stefano Tempia
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ciro Cattuto
- ISI Foundation, Turin, Italy
- Department of Informatics, University of Turin, Turin, Italy
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2
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Leung KY, Metting E, Ebbers W, Veldhuijzen I, Andeweg SP, Luijben G, de Bruin M, Wallinga J, Klinkenberg D. Effectiveness of a COVID-19 contact tracing app in a simulation model with indirect and informal contact tracing. Epidemics 2024; 46:100735. [PMID: 38128242 DOI: 10.1016/j.epidem.2023.100735] [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: 05/15/2023] [Revised: 11/17/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023] Open
Abstract
During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app.
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Affiliation(s)
- Ka Yin Leung
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, the Netherlands.
| | - Esther Metting
- University of Groningen, University Medical Center Groningen, Data Science Center in Health, the Netherlands; University of Groningen, University Medical Center Groningen, Department of Primary Care, the Netherlands; University of Groningen, faculty of Economics and Business, Department of Operations, the Netherlands
| | - Wolfgang Ebbers
- Erasmus School of Social and Behavioural Sciences, Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Irene Veldhuijzen
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, the Netherlands
| | - Stijn P Andeweg
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, the Netherlands
| | - Guus Luijben
- National Institute for Public Health and the Environment, Centre for Health and Society, Bilthoven, the Netherlands
| | - Marijn de Bruin
- National Institute for Public Health and the Environment, Centre for Health and Society, Bilthoven, the Netherlands; Radboud University Medical Centre, Radboud Institute of Health Sciences, IQ Healthcare, Nijmegen, the Netherlands
| | - Jacco Wallinga
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, the Netherlands; Leiden University Medical Centre, Department of Biomedical Datasciences, Leiden, the Netherlands
| | - Don Klinkenberg
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, the Netherlands
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3
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Cencetti G, Lucchini L, Santin G, Battiston F, Moro E, Pentland A, Lepri B. Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion. J R Soc Interface 2024; 21:20230471. [PMID: 38166491 PMCID: PMC10761286 DOI: 10.1098/rsif.2023.0471] [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: 08/14/2023] [Accepted: 11/23/2023] [Indexed: 01/04/2024] Open
Abstract
Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectiveness from an epidemic control perspective; however, they have also significant negative consequences on social life and relationships, work routines and community engagement. In particular, complex ideas, work and school collaborations, innovative discoveries and resilient norms formation and maintenance, which often require face-to-face interactions of two or more parties to be developed and synergically coordinated, are particularly affected. In this study, we propose an alternative hybrid solution that balances the slowdown of epidemic diffusion with the preservation of face-to-face interactions, that we test simulating a disease and a knowledge spreading simultaneously on a network of contacts. Our approach involves a two-step partitioning of the population. First, we tune the level of node clustering, creating 'social bubbles' with increased contacts within each bubble and fewer outside, while maintaining the average number of contacts in each network. Second, we tune the level of temporal clustering by pairing, for a certain time interval, nodes from specific social bubbles. Our results demonstrate that a hybrid approach can achieve better trade-offs between epidemic control and complex knowledge diffusion. The versatility of our model enables tuning and refining clustering levels to optimally achieve the desired trade-off, based on the potentially changing characteristics of a disease or knowledge diffusion process.
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Affiliation(s)
- Giulia Cencetti
- Digital Society Center, Fondazione Bruno Kessler, Trento, Italy
- Centre de Physique Théorique, CNRS, Aix-Marseille Univ, Université de Toulon, Marseille, France
| | - Lorenzo Lucchini
- DONDENA and BIDSA Research Centres—Bocconi University, Milan, Italy
| | - Gabriele Santin
- Digital Society Center, Fondazione Bruno Kessler, Trento, Italy
- Department of Environmental Sciences, Informatics and Statistics, University of Venice, Venezia, Italy
| | - Federico Battiston
- Department of Network and Data Science, Central European University, Vienna, Austria
| | - Esteban Moro
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mathematics & GISC, Universidad Carlos III de Madrid, Leganes, Spain
| | - Alex Pentland
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bruno Lepri
- Digital Society Center, Fondazione Bruno Kessler, Trento, Italy
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4
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Chow BWK, Lim YD, Poh RCH, Ko A, Hong GH, Zou SWL, Cheah J, Ho S, Lee VJM, Ho MZJ. Use of a digital contact tracing system in Singapore to mitigate COVID-19 spread. BMC Public Health 2023; 23:2253. [PMID: 37974135 PMCID: PMC10652620 DOI: 10.1186/s12889-023-17150-0] [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: 08/17/2023] [Accepted: 11/04/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Contact tracing has been essential to reducing spread of COVID-19. Singapore leveraged technology to assist with contact tracing efforts using a Bluetooth-based app and token platform called 'TraceTogether'. METHODS We reviewed the impact of this system during the country's Delta and Omicron waves (24 August 2021 to 17 February 2022) to identify differences in number of close contacts and time savings between full automation using TraceTogether alone as compared to manual contact tracing supplemented by TraceTogether. Characteristics of digital contact tracing app or token users were reviewed. Thereafter, the number of close contacts identified by manual and digital contact tracing methods, and the number of confirmed COVID-19 cases among contacts were analysed. The difference in time taken for identification of close contacts was also determined. FINDINGS Adoption rate for TraceTogether was high, with 93.3% of cases having a registered device. There was a 9.8 h (34.9%) reduction in time savings for close contacts to be informed using TraceTogether alone compared to manual contact tracing supplemented by TraceTogether. The proportion of close contacts automatically identified through TraceTogether alone and turned positive was 3.6%. For those identified through manual contact tracing supplemented by TraceTogether, this proportion was 12.5% and 6.2% for those served quarantine orders and health risk warnings respectively. INTERPRETATION The high adoption rate of 'TraceTogether' suggest that digital solutions remain a promising option to improve contact tracing in future epidemics. This may have been through its concurrent use with vaccine differentiated public health measures and policies which engender public trust. There is future potential for utilising such technology in managing communicable diseases to achieve good public health outcomes.
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Affiliation(s)
- Bryan W K Chow
- Preventive Medicine Residency Program, National University Health System, Singapore, Singapore.
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore.
| | - Yi Ding Lim
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
| | - Richard C H Poh
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
| | - Amy Ko
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
| | - Guo Hao Hong
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
| | - Steffen W L Zou
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
| | - Joshua Cheah
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
| | - Shaowei Ho
- Government Technology Agency, Prime Minister's Office, Singapore, Republic of Singapore
| | - Vernon J M Lee
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Marc Z J Ho
- Communicable Diseases Division, Ministry of Health, Singapore, Republic of Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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5
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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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6
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Marmor Y, Abbey A, Shahar Y, Mokryn O. Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model. Sci Rep 2023; 13:12955. [PMID: 37563358 PMCID: PMC10415258 DOI: 10.1038/s41598-023-39817-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 07/31/2023] [Indexed: 08/12/2023] Open
Abstract
Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.
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Affiliation(s)
- Yanir Marmor
- Information Systems, University of Haifa, Haifa, Israel
| | - Alex Abbey
- Information Systems, University of Haifa, Haifa, Israel
| | - Yuval Shahar
- Software and Information Systems Engineering, Ben Gurion University, Beer Sheva, Israel
| | - Osnat Mokryn
- Information Systems, University of Haifa, Haifa, Israel.
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7
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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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8
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Braunstein A, Catania G, Dall'Asta L, Mariani M, Muntoni AP. Inference in conditioned dynamics through causality restoration. Sci Rep 2023; 13:7350. [PMID: 37147382 PMCID: PMC10163042 DOI: 10.1038/s41598-023-33770-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/18/2023] [Indexed: 05/07/2023] Open
Abstract
Estimating observables from conditioned dynamics is typically computationally hard. While obtaining independent samples efficiently from unconditioned dynamics is usually feasible, most of them do not satisfy the imposed conditions and must be discarded. On the other hand, conditioning breaks the causal properties of the dynamics, which ultimately renders the sampling of the conditioned dynamics non-trivial and inefficient. In this work, a Causal Variational Approach is proposed, as an approximate method to generate independent samples from a conditioned distribution. The procedure relies on learning the parameters of a generalized dynamical model that optimally describes the conditioned distribution in a variational sense. The outcome is an effective and unconditioned dynamical model from which one can trivially obtain independent samples, effectively restoring the causality of the conditioned dynamics. The consequences are twofold: the method allows one to efficiently compute observables from the conditioned dynamics by averaging over independent samples; moreover, it provides an effective unconditioned distribution that is easy to interpret. This approximation can be applied virtually to any dynamics. The application of the method to epidemic inference is discussed in detail. The results of direct comparison with state-of-the-art inference methods, including the soft-margin approach and mean-field methods, are promising.
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Affiliation(s)
- Alfredo Braunstein
- DISAT, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129, Turin, Italy
- INFN, Sezione di Torino, Turin, Italy
- Italian Institute for Genomic Medicine, IRCCS Candiolo, SP-142, 10060, Candiolo, TO, Italy
| | - Giovanni Catania
- Departamento de Física Téorica I, Universidad Complutense, 28040, Madrid, Spain
| | - Luca Dall'Asta
- DISAT, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129, Turin, Italy
- INFN, Sezione di Torino, Turin, Italy
- Italian Institute for Genomic Medicine, IRCCS Candiolo, SP-142, 10060, Candiolo, TO, Italy
- Collegio Carlo Alberto, P.za Arbarello 8, 10122, Turin, Italy
| | - Matteo Mariani
- DISAT, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129, Turin, Italy.
| | - Anna Paola Muntoni
- DISAT, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129, Turin, Italy
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9
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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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10
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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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11
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Yang L, Wu J, Mo X, Chen Y, Huang S, Zhou L, Dai J, Xie L, Chen S, Shang H, Rao B, Weng B, Abulimiti A, Wu S, Xie X. Changes in Mobile Health Apps Usage Before and After the COVID-19 Outbreak in China: Semilongitudinal Survey. JMIR Public Health Surveill 2023; 9:e40552. [PMID: 36634256 PMCID: PMC9996426 DOI: 10.2196/40552] [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: 06/27/2022] [Revised: 10/26/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Mobile health (mHealth) apps are rapidly emerging technologies in China due to strictly controlled medical needs during the COVID-19 pandemic while continuing essential services for chronic diseases. However, there have been no large-scale, systematic efforts to evaluate relevant apps. OBJECTIVE We aim to provide a landscape of mHealth apps in China by describing and comparing digital health concerns before and after the COVID-19 outbreak, including mHealth app data flow and user experience, and analyze the impact of COVID-19 on mHealth apps. METHODS We conducted a semilongitudinal survey of 1593 mHealth apps to study the app data flow and clarify usage changes and influencing factors. We selected mHealth apps in app markets, web pages from the Baidu search engine, the 2018 top 100 hospitals with internet hospitals, and online shopping sites with apps that connect to smart devices. For user experience, we recruited residents from a community in southeastern China from October 2019 to November 2019 (before the outbreak) and from June 2020 to August 2020 (after the outbreak) comparing the attention of the population to apps. We also examined associations between app characteristics, functions, and outcomes at specific quantiles of distribution in download changes using quantile regression models. RESULTS Rehabilitation medical support was the top-ranked functionality, with a median 1.44 million downloads per app prepandemic and a median 2.74 million downloads per app postpandemic. Among the top 10 functions postpandemic, 4 were related to maternal and child health: pregnancy preparation (ranked second; fold change 4.13), women's health (ranked fifth; fold change 5.16), pregnancy (ranked sixth; fold change 5.78), and parenting (ranked tenth; fold change 4.03). Quantile regression models showed that rehabilitation (P75, P90), pregnancy preparation (P90), bodybuilding (P50, P90), and vaccination (P75) were positively associated with an increase in downloads after the outbreak. In the user experience survey, the attention given to health information (prepandemic: 249/375, 66.4%; postpandemic: 146/178, 82.0%; P=.006) steadily increased after the outbreak. CONCLUSIONS mHealth apps are an effective health care approach gaining in popularity among the Chinese population following the COVID-19 outbreak. This research provides direction for subsequent mHealth app development and promotion in the postepidemic era, supporting medical model reformation in China as a reference, which may provide new avenues for designing and evaluating indirect public health interventions such as health education and health promotion.
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Affiliation(s)
- Le Yang
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Jiadong Wu
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiaoxiao Mo
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Yaqin Chen
- School of Nursing, Fujian Medical University, Fuzhou, China
| | - Shanshan Huang
- School of Nursing, Fujian Medical University, Fuzhou, China
| | - Linlin Zhou
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Jiaqi Dai
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Linna Xie
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Siyu Chen
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Hao Shang
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Beibei Rao
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Bingtao Weng
- School of Public Health, Fujian Medical University, Fuzhou, China
| | | | - Siying Wu
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiaoxu Xie
- School of Public Health, Fujian Medical University, Fuzhou, China
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12
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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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13
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Digitalization impacts the COVID-19 pandemic and the stringency of government measures. Sci Rep 2022; 12:21628. [PMID: 36517489 PMCID: PMC9749635 DOI: 10.1038/s41598-022-24726-0] [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: 07/07/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 poses a significant burden to populations worldwide. Although the pandemic has accelerated digital transformation, little is known about the influence of digitalization on pandemic developments. Therefore, this country-level study aims to explore the impact of pre-pandemic digital adoption on COVID-19 outcomes and government measures. Using the Digital Adoption Index (DAI), we examined the association between countries' digital preparedness levels and COVID-19 cases, deaths, and stringency indices (SI) of government measures until March 2021. Gradient Tree Boosting based algorithm pinpointed essential features related to COVID-19 trends, such as digital adoption, populations' smoker fraction, age, and poverty. Subsequently, regression analyses indicated that higher DAI was associated with significant declines in new cases (β = - 362.25/pm; p < 0.001) and attributed deaths (β = - 5.53/pm; p < 0.001) months after the peak. When plotting DAI against the SI normalized for the starting day, countries with higher DAI adopted slightly more stringent government measures (β = 4.86; p < 0.01). Finally, a scoping review identified 70 publications providing valuable arguments for our findings. Countries with higher DAI before the pandemic show a positive trend in handling the pandemic and facilitate the implementation of more decisive governmental measures. Further distribution of digital adoption may have the potential to attenuate the impact of COVID-19 cases and deaths.
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14
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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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15
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Azzolina D, Comoretto R, Lanera C, Berchialla P, Baldi I, Gregori D. COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies. Front Public Health 2022; 10:1002232. [PMID: 36530678 PMCID: PMC9748343 DOI: 10.3389/fpubh.2022.1002232] [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: 07/24/2022] [Accepted: 11/09/2022] [Indexed: 12/05/2022] Open
Abstract
Introduction An excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered. Methods An inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies. Results This study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations. Discussion This study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the containment policies. However, all these interventions represent strategies designed to contain a mechanism that has already been triggered. Further efforts should be directed toward preventive policies aimed at protecting the most fragile subjects, such as the elderly. Therefore, it is essential to implement containment strategies before the occurrence of potentially out-of-control situations, resulting in congestion in hospitals and health services.
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Affiliation(s)
- Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy,Department of Environmental and Preventive Science, University of Ferrara, Ferrara, Italy
| | - Rosanna Comoretto
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy,Department of Public Health and Pediatrics, University of Turin, Turin, Italy
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
| | - Paola Berchialla
- Department of Clinical and Biological Science, University of Torino, Torino, Italy
| | - Ileana Baldi
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy,*Correspondence: Dario Gregori
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16
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Peel L, Peixoto TP, De Domenico M. Statistical inference links data and theory in network science. Nat Commun 2022; 13:6794. [PMID: 36357376 PMCID: PMC9649740 DOI: 10.1038/s41467-022-34267-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications. Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.
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17
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Chacoma A, Billoni OV, Kuperman MN. Complexity emerges in measures of the marking dynamics in football games. Phys Rev E 2022; 106:044308. [PMID: 36397551 DOI: 10.1103/physreve.106.044308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
In this article, we study the dynamics of marking in football matches. To do this, we survey and analyze a database containing the trajectories of players from both teams on the field of play during three professional games. We describe the dynamics through the construction of temporal bipartite networks of proximity. Based on the introduced concept of proximity, the nodes are the players, and the links are defined between opponents that are close enough to each other at a given moment. By studying the evolution of the heterogeneity parameter of the networks during the game, we characterize a scaling law for the average shape of the fluctuations, unveiling the emergence of complexity in the system. Moreover, we propose a simple model to simulate the players' motion in the field from where we obtained the evolution of a synthetic proximity network. We show that the model captures with a remarkable agreement the complexity of the empirical case, hence it proves to be helpful to elucidate the underlying mechanisms responsible for the observed phenomena.
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Affiliation(s)
- A Chacoma
- Instituto de Física Enrique Gaviola (IFEG-CONICET), Ciudad Universitaria, 5000 Córdoba, Argentina and Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000 Córdoba, Argentina
| | - O V Billoni
- Instituto de Física Enrique Gaviola (IFEG-CONICET), Ciudad Universitaria, 5000 Córdoba, Argentina and Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000 Córdoba, Argentina
| | - M N Kuperman
- Instituto Balseiro, Universidad Nacional de Cuyo, R8402AGP Bariloche, Argentina and Centro Atómico Bariloche and CONICET, R8402AGP Bariloche, Argentina
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18
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Smart Building Technologies in Response to COVID-19. ENERGIES 2022. [DOI: 10.3390/en15155488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
The COVID-19 pandemic has had a huge impact on society. Scientists are working to mitigate the impact in many ways. As a field closely related to human life, building engineering can make a great contribution. In this article, we started with the concept of the smart building as our guide. The impact of COVID-19 on daily energy consumption, information and communication technology, the ventilation of the interior environment of buildings, and the higher demand for new energy technologies such as electric vehicles is an entry point. We discuss how the concept of the smart building and related technologies (refrigeration, measurement, sensor networks, robotics, local energy generation, and storage) could help human society respond to the pandemic. We also analyze the current problems and difficulties that smart buildings face and the possible future directions of this technology.
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19
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Shrivastava SR, Shrivastava PS. Exploring the scope and utility of digital proximity tracing in the effective containment of COVID-19 infection: A narrative review. Germs 2022; 12:276-282. [PMID: 36504605 PMCID: PMC9719377 DOI: 10.18683/germs.2022.1329] [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: 12/04/2021] [Revised: 04/10/2022] [Accepted: 04/16/2022] [Indexed: 12/15/2022]
Abstract
The ongoing coronavirus disease-2019 (COVID-19) pandemic can be acknowledged as one of the most significant public health emergencies the world has encountered in the last few decades. The purpose of the current review is to understand the significance of contact tracing and explore the pros and cons of digital contact tracing in ensuring better containment of the COVID-19 outbreaks. A widespread search of published articles pertaining to the topic was done in the PubMed search engine and a total of 46 articles matching the objectives of the present review were identified. However, four articles were discarded because of the non-availability of the free full text, and thus 42 research papers were finally included. Digital contact tracing bridges the gap wherein we aim to expedite the process of contact tracing to identify the potential contacts of the confirmed cases. These applications are designed in such a way that they send a notification on the smartphone of a person, once the user is exposed to one or more confirmed cases of COVID-19. To conclude, in the battle against the COVID-19 infection, the international welfare agencies and national policy makers have been looking forward to the employment of digital technologies to support the ongoing public health measures for contact tracing. The approach of digital contact/proximity tracing should be considered as a supplement to conventional manual tracing. The need of the hour is to take specific measures to improve the inherent design of these apps, their implementation and demonstration of their effectiveness, which in turn will play a part in enhancing their acceptance and usability among the general population.
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Affiliation(s)
- Saurabh RamBihariLal Shrivastava
- MD, FAIMER, PGDHHM, DHRM, FCS, ACME, M. Phil. (HPE), Deputy Director – Academics, Sri Balaji Vidyapeeth – Deemed to be University, Medical Education Unit Coordinator and Member of the Institute Research Council, Department of Community Medicine, Shri Sathya Sai Medical College and Research Institute, Thiruporur – Guduvancherry Main Road, Ammapettai, Nellikuppam, Chengalpet District – 603108, Tamil Nadu, India,Corresponding author: Saurabh RamBihariLal Shrivastava,
| | - Prateek Saurabh Shrivastava
- MD, Department of Community Medicine, Shri Sathya Sai Medical College and Research Institute, Sri Balaji Vidyapeeth – Deemed to be University, Thiruporur – Guduvancherry Main Road, Ammapettai, Nellikuppam, Chengalpet District - 603108, Tamil Nadu, India
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20
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Schneider T, Dunbar ORA, Wu J, Böttcher L, Burov D, Garbuno-Inigo A, Wagner GL, Pei S, Daraio C, Ferrari R, Shaman J. Epidemic management and control through risk-dependent individual contact interventions. PLoS Comput Biol 2022; 18:e1010171. [PMID: 35737648 PMCID: PMC9223336 DOI: 10.1371/journal.pcbi.1010171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 05/05/2022] [Indexed: 12/12/2022] Open
Abstract
Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption. During the ongoing COVID-19 pandemic, exposure notification apps have been developed to scale up manual contact tracing. The apps use proximity data from mobile devices to automate notifying direct contacts of an infection source. The information they provide is limited because users receive only rare and binary alerts. Here we present network data assimilation (DA) as a new digital approach to epidemic management and control. Network DA uses the same data as exposure notification apps but uses it more effectively to provide frequently updated individual risk assessments to users. Network DA is based on automated learning about individuals’ risk of exposure and infection from crowd-sourced health data and proximity data. The data are aggregated with models of disease transmission to produce statistical assessments of users’ risks. In an extensive simulation study of the COVID-19 epidemic in New York City (NYC), we show that network DA with diagnostic testing achieves epidemic control with fewer than half the deaths that occurred during NYC’s lockdown, while isolating a far smaller fraction of the population (typically only 5–10% of the population at any given time). Implemented at scale, then, network DA has the potential to effectively control epidemics while minimizing economic and social disruption.
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Affiliation(s)
- Tapio Schneider
- California Institute of Technology, Pasadena, California, United States of America
- * E-mail:
| | - Oliver R. A. Dunbar
- California Institute of Technology, Pasadena, California, United States of America
| | - Jinlong Wu
- California Institute of Technology, Pasadena, California, United States of America
| | - Lucas Böttcher
- Computational Social Science, Frankfurt School of Finance and Management, Frankfurt a. M., Germany
- Department of Computational Medicine, University of California, Los Angeles, California, United States of America
| | - Dmitry Burov
- California Institute of Technology, Pasadena, California, United States of America
| | - Alfredo Garbuno-Inigo
- Departamento de Estadística, Instituto Tecnológico Autónomo de México, Ciudad de México, México
| | - Gregory L. Wagner
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States of America
| | - Chiara Daraio
- California Institute of Technology, Pasadena, California, United States of America
| | - Raffaele Ferrari
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States of America
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21
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Rannikko J, Tamminen P, Hellsten R, Nuorti JP, Syrjänen J. Effectiveness of COVID-19 digital proximity tracing app in Finland. Clin Microbiol Infect 2022; 28:903-904. [PMID: 35283308 PMCID: PMC8913429 DOI: 10.1016/j.cmi.2022.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 01/14/2023]
Affiliation(s)
- Juha Rannikko
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland; Faculty of Medicine and Health Technology, Tampere University, Finland.
| | - Pekka Tamminen
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland
| | - Roosa Hellsten
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland
| | - J Pekka Nuorti
- Health Sciences Unit, Faculty of Social Sciences, Tampere University, Finland.
| | - Jaana Syrjänen
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland
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22
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Petrizzelli F, Guzzi PH, Mazza T. Beyond COVID-19 Pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading. Comput Struct Biotechnol J 2022; 20:2664-2671. [PMID: 35664237 PMCID: PMC9135485 DOI: 10.1016/j.csbj.2022.05.040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022] Open
Abstract
Paper discusses the relevance of the adoption of ad-hoc vaccination strategies. Paper shows how to evaluate the impact of different vaccination strategy by considering network-based models. Tailored interventions, e.g., vaccination, applied on central nodes of these networks may efficiently stop the propagation of an infection. The way node "centrality" is defined is the key to curb infection spreading.
The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks. We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus’s spreading. It is known that tailored interventions (e.g., vaccination) on central nodes may efficiently stop the propagation, thereby eliminating the “bridge edges.” We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available at https://github.com/mazzalab/playgrounds.
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Affiliation(s)
- Francesco Petrizzelli
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Campus S Venuta, 88100, Italy
- Corresponding authors.
| | - Tommaso Mazza
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, Italy
- Corresponding authors.
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23
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Rizi AK, Faqeeh A, Badie-Modiri A, Kivelä M. Epidemic spreading and digital contact tracing: Effects of heterogeneous mixing and quarantine failures. Phys Rev E 2022; 105:044313. [PMID: 35590624 DOI: 10.1103/physreve.105.044313] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 03/22/2022] [Indexed: 06/15/2023]
Abstract
Contact tracing via digital tracking applications installed on mobile phones is an important tool for controlling epidemic spreading. Its effectivity can be quantified by modifying the standard methodology for analyzing percolation and connectivity of contact networks. We apply this framework to networks with varying degree distributions, numbers of application users, and probabilities of quarantine failures. Further, we study structured populations with homophily and heterophily and the possibility of degree-targeted application distribution. Our results are based on a combination of explicit simulations and mean-field analysis. They indicate that there can be major differences in the epidemic size and epidemic probabilities which are equivalent in the normal susceptible-infectious-recovered (SIR) processes. Further, degree heterogeneity is seen to be especially important for the epidemic threshold but not as much for the epidemic size. The probability that tracing leads to quarantines is not as important as the application adoption rate. Finally, both strong homophily and especially heterophily with regard to application adoption can be detrimental. Overall, epidemic dynamics are very sensitive to all of the parameter values we tested out, which makes the problem of estimating the effect of digital contact tracing an inherently multidimensional problem.
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Affiliation(s)
- Abbas K Rizi
- Department of Computer Science, School of Science, Aalto University, FI-00076, Finland
| | - Ali Faqeeh
- Department of Computer Science, School of Science, Aalto University, FI-00076, Finland
- Mathematics Applications Consortium for Science & Industry, University of Limerick, Limerick V94 T9PX, Ireland
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - Arash Badie-Modiri
- Department of Computer Science, School of Science, Aalto University, FI-00076, Finland
| | - Mikko Kivelä
- Department of Computer Science, School of Science, Aalto University, FI-00076, Finland
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Poletto C, Boëlle PY. Learning from the initial deployment of digital contact tracing apps. THE LANCET PUBLIC HEALTH 2022; 7:e206-e207. [PMID: 35131044 PMCID: PMC8816385 DOI: 10.1016/s2468-2667(22)00035-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 12/19/2022] Open
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d’Andrea V, Gallotti R, Castaldo N, De Domenico M. Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks. PLoS Comput Biol 2022; 18:e1009760. [PMID: 35171901 PMCID: PMC8849607 DOI: 10.1371/journal.pcbi.1009760] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/15/2021] [Indexed: 12/20/2022] Open
Abstract
The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook—involving more than 500,000 respondents from 64 countries—showing that there is a “one-to-one” relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease—sharing epidemiological features with COVID-19—that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks. The spreading of a disease across a population is affected by the compliance with behavioral restrictions, enforced by governments to slow the diffusion of an epidemic. In this study, we use a large-scale survey to relate compliance with behavioral rules to individual level of disease risk perception. We asses that absence of risk awareness is associated with a set of harmful behaviors (namely, non-compliance with: social distancing, use of facial masks and adoption of any prevention measures) that can accelerate the diffusion of an epidemic. Through a mathematical model, we study how epidemic dynamics, and in particular hospitalization burden, is affected by the presence of different fractions of the total population who do not correctly perceive the disease risk and, accordingly, adopt harmful behaviors. Moreover, we study how different social contact structures among individuals modulate the effect on epidemic spreading of a fixed population fraction with null risk perception. Our findings highlight that a fixed percentage of people with null risk awareness has a lower impact on epidemic size in social structures characterized by communities and heterogeneity in contacts among individuals. The spreading of a disease across a population is affected by the compliance with behavioral restrictions, enforced by governments to slow the diffusion of an epidemic. In this study, we use a large-scale survey to relate compliance with behavioral rules to individual level of disease risk perception. We asses that absence of risk awareness is associated with a set of harmful behaviors (namely, non-compliance with: social distancing, use of facial masks and adoption of any prevention measures) that can accelerate the diffusion of an epidemic. Through a mathematical model, we study how epidemic dynamics, and in particular hospitalization burden, is affected by the presence of different fractions of the total population who do not correctly perceive the disease risk and, accordingly, adopt harmful behaviors. Moreover, we study how different social contact structures among individuals modulate the effect on epidemic spreading of a fixed population fraction with null risk perception. Our findings highlight that a fixed percentage of people with null risk awareness has a lower effectiveness on epidemic size in social structures characterized by communities and heterogeneity in contacts among individuals. However, in these same social structures, larger fractions of risk-denying population cause an enhanced effect on epidemic size.
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Affiliation(s)
- Valeria d’Andrea
- CoMuNe Lab, Fondazione Bruno Kessler, Trento, Italy
- * E-mail: (VdA); (MDD)
| | | | | | - Manlio De Domenico
- CoMuNe Lab, Fondazione Bruno Kessler, Trento, Italy
- Department of Physics and Astronomy “G. Galilei”, University of Padova, Padova, Italy
- * E-mail: (VdA); (MDD)
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Leoni E, Cencetti G, Santin G, Istomin T, Molteni D, Picco GP, Farella E, Lepri B, Murphy AL. Measuring close proximity interactions in summer camps during the COVID-19 pandemic. EPJ DATA SCIENCE 2022; 11:5. [PMID: 35127327 PMCID: PMC8802275 DOI: 10.1140/epjds/s13688-022-00316-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
Policy makers have implemented multiple non-pharmaceutical strategies to mitigate the COVID-19 worldwide crisis. Interventions had the aim of reducing close proximity interactions, which drive the spread of the disease. A deeper knowledge of human physical interactions has revealed necessary, especially in all settings involving children, whose education and gathering activities should be preserved. Despite their relevance, almost no data are available on close proximity contacts among children in schools or other educational settings during the pandemic. Contact data are usually gathered via Bluetooth, which nonetheless offers a low temporal and spatial resolution. Recently, ultra-wideband (UWB) radios emerged as a more accurate alternative that nonetheless exhibits a significantly higher energy consumption, limiting in-field studies. In this paper, we leverage a novel approach, embodied by the Janus system that combines these radios by exploiting their complementary benefits. The very accurate proximity data gathered in-field by Janus, once augmented with several metadata, unlocks unprecedented levels of information, enabling the development of novel multi-level risk analyses. By means of this technology, we have collected real contact data of children and educators in three summer camps during summer 2020 in the province of Trento, Italy. The wide variety of performed daily activities induced multiple individual behaviors, allowing a rich investigation of social environments from the contagion risk perspective. We consider risk based on duration and proximity of contacts and classify interactions according to different risk levels. We can then evaluate the summer camps' organization, observe the effect of partition in small groups, or social bubbles, and identify the organized activities that mitigate the riskier behaviors. Overall, we offer an insight into the educator-child and child-child social interactions during the pandemic, thus providing a valuable tool for schools, summer camps, and policy makers to (re)structure educational activities safely.
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Affiliation(s)
- Elia Leoni
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
- DEI, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
| | - Giulia Cencetti
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
| | - Gabriele Santin
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
| | - Timofei Istomin
- DISI, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| | - Davide Molteni
- DISI, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| | | | | | - Bruno Lepri
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
| | - Amy L. Murphy
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
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The impact of control and mitigation strategies during the second wave of coronavirus infections in Spain and Italy. Sci Rep 2022; 12:1073. [PMID: 35058522 PMCID: PMC8776768 DOI: 10.1038/s41598-022-05041-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 12/30/2021] [Indexed: 12/22/2022] Open
Abstract
European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants’ perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus.
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Caserotti M, Girardi P, Tasso A, Rubaltelli E, Lotto L, Gavaruzzi T. Joint analysis of the intention to vaccinate and to use contact tracing app during the COVID-19 pandemic. Sci Rep 2022; 12:793. [PMID: 35039550 PMCID: PMC8764077 DOI: 10.1038/s41598-021-04765-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 12/31/2021] [Indexed: 01/01/2023] Open
Abstract
Pharmacological and non-pharmacological measures will overlap for a period after the onset of the pandemic, playing a strong role in virus containment. We explored which factors influence the likelihood to adopt two different preventive measures against the COVID-19 pandemic. An online snowball sampling (May-June 2020) collected a total of 448 questionnaires in Italy. A Bayesian bivariate Gaussian regression model jointly investigated the willingness to get vaccinated against COVID-19 and to download the national contact tracing app. A mixed-effects cumulative logistic model explored which factors affected the motivation to adopt one of the two preventive measures. Despite both COVID-19 vaccines and tracing apps being indispensable tools to contain the spread of SARS-CoV-2, our results suggest that adherence to the vaccine or to the national contact tracing app is not predicted by the same factors. Therefore, public communication on these measures needs to take in consideration not only the perceived risk associated with COVID-19, but also the trust people place in politics and science, their concerns and doubts about vaccinations, and their employment status. Further, the results suggest that the motivation to comply with these measurements was predominantly to protect others rather than self-protection.
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Affiliation(s)
- Marta Caserotti
- Department of Developmental Psychology and Socialization, University of Padova, Padua, Italy
| | - Paolo Girardi
- Department of Developmental Psychology and Socialization, University of Padova, Padua, Italy.
- Department of Statistical Sciences, University of Padova, Padua, Italy.
| | | | - Enrico Rubaltelli
- Department of Developmental Psychology and Socialization, University of Padova, Padua, Italy
| | - Lorella Lotto
- Department of Developmental Psychology and Socialization, University of Padova, Padua, Italy
| | - Teresa Gavaruzzi
- Department of Developmental Psychology and Socialization, University of Padova, Padua, Italy
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Gomes BM, Rebelo CB, Alves de Sousa L. Public health, surveillance systems and preventive medicine in an interconnected world. One Health 2022. [DOI: 10.1016/b978-0-12-822794-7.00006-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Durazzi F, Pichard F, Remondini D, Salathé M. Dynamics of social media behavior before and after SARS-CoV-2 infection. Front Public Health 2022; 10:1069931. [PMID: 36911211 PMCID: PMC9995964 DOI: 10.3389/fpubh.2022.1069931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/30/2022] [Indexed: 02/25/2023] Open
Abstract
Introduction Online social media have been both a field of research and a source of data for research since the beginning of the COVID-19 pandemic. In this study, we aimed to determine how and whether the content of tweets by Twitter users reporting SARS-CoV-2 infections changed over time. Methods We built a regular expression to detect users reporting being infected, and we applied several Natural Language Processing methods to assess the emotions, topics, and self-reports of symptoms present in the timelines of the users. Results Twelve thousand one hundred and twenty-one twitter users matched the regular expression and were considered in the study. We found that the proportions of health-related, symptom-containing, and emotionally non-neutral tweets increased after users had reported their SARS-CoV-2 infection on Twitter. Our results also show that the number of weeks accounting for the increased proportion of symptoms was consistent with the duration of the symptoms in clinically confirmed COVID-19 cases. Furthermore, we observed a high temporal correlation between self-reports of SARS-CoV-2 infection and officially reported cases of the disease in the largest English-speaking countries. Discussion This study confirms that automated methods can be used to find digital users publicly sharing information about their health status on social media, and that the associated data analysis may supplement clinical assessments made in the early phases of the spread of emerging diseases. Such automated methods may prove particularly useful for newly emerging health conditions that are not rapidly captured in the traditional health systems, such as the long term sequalae of SARS-CoV-2 infections.
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Affiliation(s)
- Francesco Durazzi
- Department of Physics and Astronomy (DIFA), University of Bologna, Bologna, Italy
| | - François Pichard
- Digital Epidemiology Lab, School of Life Sciences, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Global Health Institute, Geneva, Switzerland
| | - Daniel Remondini
- Department of Physics and Astronomy (DIFA), University of Bologna, Bologna, Italy
| | - Marcel Salathé
- Digital Epidemiology Lab, School of Life Sciences, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Global Health Institute, Geneva, Switzerland
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Daniore P, Nittas V, Moser A, Höglinger M, von Wyl V. Using Venn Diagrams to Evaluate Digital Contact Tracing: Panel Survey Analysis. JMIR Public Health Surveill 2021; 7:e30004. [PMID: 34874890 PMCID: PMC8658229 DOI: 10.2196/30004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/22/2021] [Accepted: 10/26/2021] [Indexed: 01/11/2023] Open
Abstract
Background Mitigation of the spread of infection relies on targeted approaches aimed at preventing nonhousehold interactions. Contact tracing in the form of digital proximity tracing apps has been widely adopted in multiple countries due to its perceived added benefits of tracing speed and breadth in comparison to traditional manual contact tracing. Assessments of user responses to exposure notifications through a guided approach can provide insights into the effect of digital proximity tracing app use on managing the spread of SARS-CoV-2. Objective The aim of this study was to demonstrate the use of Venn diagrams to investigate the contributions of digital proximity tracing app exposure notifications and subsequent mitigative actions in curbing the spread of SARS-CoV-2 in Switzerland. Methods We assessed data from 4 survey waves (December 2020 to March 2021) from a nationwide panel study (COVID-19 Social Monitor) of Swiss residents who were (1) nonusers of the SwissCovid app, (2) users of the SwissCovid app, or (3) users of the SwissCovid app who received exposure notifications. A Venn diagram approach was applied to describe the overlap or nonoverlap of these subpopulations and to assess digital proximity tracing app use and its associated key performance indicators, including actions taken to prevent SARS-CoV-2 transmission. Results We included 12,525 assessments from 2403 participants, of whom 50.9% (1222/2403) reported not using the SwissCovid digital proximity tracing app, 49.1% (1181/2403) reported using the SwissCovid digital proximity tracing app and 2.5% (29/1181) of the digital proximity tracing app users reported having received an exposure notification. Most digital proximity tracing app users (75.9%, 22/29) revealed taking at least one recommended action after receiving an exposure notification, such as seeking SARS-CoV-2 testing (17/29, 58.6%) or calling a federal information hotline (7/29, 24.1%). An assessment of key indicators of mitigative actions through a Venn diagram approach reveals that 30% of digital proximity tracing app users (95% CI 11.9%-54.3%) also tested positive for SARS-CoV-2 after having received exposure notifications, which is more than 3 times that of digital proximity tracing app users who did not receive exposure notifications (8%, 95% CI 5%-11.9%). Conclusions Responses in the form of mitigative actions taken by 3 out of 4 individuals who received exposure notifications reveal a possible contribution of digital proximity tracing apps in mitigating the spread of SARS-CoV-2. The application of a Venn diagram approach demonstrates its value as a foundation for researchers and health authorities to assess population-level digital proximity tracing app effectiveness by providing an intuitive approach for calculating key performance indicators.
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Affiliation(s)
- Paola Daniore
- Institute for Implementation Science in Healthcare, University of Zurich, Zurich, Switzerland
| | - Vasileios Nittas
- Epidemiology, Biostatistics and Prevention Institute, 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
| | - Viktor von Wyl
- Institute for Implementation Science in Healthcare, University of Zurich, Zurich, Switzerland.,Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Hambridge HL, Kahn R, Onnela JP. Examining SARS-CoV-2 Interventions in Residential Colleges Using an Empirical Network. Int J Infect Dis 2021; 113:325-330. [PMID: 34624516 PMCID: PMC8492892 DOI: 10.1016/j.ijid.2021.10.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/17/2021] [Accepted: 10/02/2021] [Indexed: 01/11/2023] Open
Abstract
Objectives Universities have turned to SARS-CoV-2 models to examine campus reopening strategies. While these studies have explored a variety of modeling techniques, none have used empirical data. Methods In this study, we use an empirical proximity network of college freshmen obtained using smartphone Bluetooth to simulate the spread of the virus. We investigate the role of immunization, testing, isolation, mask wearing, and social distancing in the presence of implementation challenges and imperfect compliance. Results We show that frequent testing could drastically reduce the spread of the virus if levels of immunity are low, but its effects are limited if immunity is more ubiquitous. Furthermore, moderate levels of mask wearing and social distancing could lead to additional reductions in cumulative incidence, but their benefit decreases rapidly as immunity and testing frequency increase. However, if immunity from vaccination is imperfect or declines over time, scenarios not studied here, frequent testing and other interventions may play more central roles. Conclusions Our findings suggest that although regular testing and isolation are powerful tools, they have limited benefit if immunity is high or other interventions are widely adopted. If universities can attain even moderate levels of vaccination, masking, and social distancing, they may be able to relax the frequency of testing to once every four weeks.
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Affiliation(s)
- Hali L Hambridge
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Rebecca Kahn
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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De Meijere G, Colizza V, Valdano E, Castellano C. Effect of delayed awareness and fatigue on the efficacy of self-isolation in epidemic control. Phys Rev E 2021; 104:044316. [PMID: 34781485 DOI: 10.1103/physreve.104.044316] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/26/2021] [Indexed: 12/12/2022]
Abstract
The isolation of infectious individuals is a key measure of public health for the control of communicable diseases. However, involving a strong perturbation of daily life, it often causes psychosocial distress, and severe financial and social costs. These may act as mechanisms limiting the adoption of the measure in the first place or the adherence throughout its full duration. In addition, difficulty of recognizing mild symptoms or lack of symptoms may impact awareness of the infection and further limit adoption. Here we study an epidemic model on a network of contacts accounting for limited adherence and delayed awareness to self-isolation, along with fatigue causing overhasty termination. The model allows us to estimate the role of each ingredient and analyze the tradeoff between adherence and duration of self-isolation. We find that the epidemic threshold is very sensitive to an effective compliance that combines the effects of imperfect adherence, delayed awareness and fatigue. If adherence improves for shorter quarantine periods, there exists an optimal duration of isolation, shorter than the infectious period. However, heterogeneities in the connectivity pattern, coupled to a reduced compliance for highly active individuals, may almost completely offset the effectiveness of self-isolation measures on the control of the epidemic.
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Affiliation(s)
- Giulia De Meijere
- Gran Sasso Science Institute, Viale F. Crispi 7, 67100 L'Aquila, Italy.,Istituto dei Sistemi Complessi (ISC-CNR), Via dei Taurini 19, I-00185 Rome, Italy
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, 27, rue Chaligny, 75012 Paris, France.,Tokyo Tech World Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori Ward, Yokohama, Kanagawa 226-0026, Japan
| | - Eugenio Valdano
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, 27, rue Chaligny, 75012 Paris, France
| | - Claudio Castellano
- Istituto dei Sistemi Complessi (ISC-CNR), Via dei Taurini 19, I-00185 Rome, Italy
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Lueks W, Benzler J, Bogdanov D, Kirchner G, Lucas R, Oliveira R, Preneel B, Salathé M, Troncoso C, von Wyl V. Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications. Front Digit Health 2021; 3:677929. [PMID: 34713149 PMCID: PMC8521913 DOI: 10.3389/fdgth.2021.677929] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/08/2021] [Indexed: 11/27/2022] Open
Abstract
Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. DPT not only relies on the technical functioning of the proximity tracing application and its backend server, but also on seamless integration of health system processes such as laboratory testing, communication of results (and their validation), generation of notification codes, manual contact tracing, and management of app-notified users. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. For example, comparative effectiveness measures may require information generated outside the DPT system, e.g., from manual contact tracing. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps may facilitate understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification system may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT. Ultimately, the integration of further information (e.g., regarding exact exposure time) into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. More data may lead to better epidemiological information but may also increase the privacy risks associated with the system, and thus decrease public DPT acceptance. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT systems or intending to assess the added value of DPT relative to the existing contact tracing systems.
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Affiliation(s)
- Wouter Lueks
- Security and Privacy Engineering Laboratory, School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | | | | | - Raquel Lucas
- Medical School and Institute of Public Health (EPIUnit), Universidade Do Porto, Porto, Portugal
| | - Rui Oliveira
- Institute for Systems and Computer Engineering, Technology and Science & University of Minho, Porto, Portugal
| | - Bart Preneel
- Department of Electrical Engineering, Katholieke Universiteit Leuven and IMEC, Leuven, Belgium
| | - Marcel Salathé
- Digital Epidemiology Laboratory, School of Life Sciences, School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Global Health Institute, Geneva, Switzerland
| | - Carmela Troncoso
- Security and Privacy Engineering Laboratory, School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Viktor von Wyl
- Digital and Mobile Health Group, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.,Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
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Leite GS, Albuquerque AB, Pinheiro PR. Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:10765. [PMID: 34682511 PMCID: PMC8535524 DOI: 10.3390/ijerph182010765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/09/2021] [Accepted: 10/11/2021] [Indexed: 12/23/2022]
Abstract
With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.
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Affiliation(s)
- Gleidson Sobreira Leite
- UNIFOR, Department of Computer Science, University of Fortaleza, Fortaleza 60811-905, Ceará, Brazil; (A.B.A.); (P.R.P.)
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36
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Hogan K, Macedo B, Macha V, Barman A, Jiang X. Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation. JMIR Med Inform 2021; 9:e27449. [PMID: 34254937 PMCID: PMC8291141 DOI: 10.2196/27449] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/03/2021] [Accepted: 04/14/2021] [Indexed: 02/06/2023] Open
Abstract
The global and national response to the COVID-19 pandemic has been inadequate due to a collective lack of preparation and a shortage of available tools for responding to a large-scale pandemic. By applying lessons learned to create better preventative methods and speedier interventions, the harm of a future pandemic may be dramatically reduced. One potential measure is the widespread use of contact tracing apps. While such apps were designed to combat the COVID-19 pandemic, the time scale in which these apps were deployed proved a significant barrier to efficacy. Many companies and governments sprinted to deploy contact tracing apps that were not properly vetted for performance, privacy, or security issues. The hasty development of incomplete contact tracing apps undermined public trust and negatively influenced perceptions of app efficacy. As a result, many of these apps had poor voluntary public uptake, which greatly decreased the apps' efficacy. Now, with lessons learned from this pandemic, groups can better design and test apps in preparation for the future. In this viewpoint, we outline common strategies employed for contact tracing apps, detail the successes and shortcomings of several prominent apps, and describe lessons learned that may be used to shape effective contact tracing apps for the present and future. Future app designers can keep these lessons in mind to create a version that is suitable for their local culture, especially with regard to local attitudes toward privacy-utility tradeoffs during public health crises.
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Affiliation(s)
- Katie Hogan
- Department of Bioengineering, Rice University, Houston, TX, United States
| | - Briana Macedo
- School of Engineering, Princeton University, Princeton, NJ, United States
| | - Venkata Macha
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Arko Barman
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, United States
- Data to Knowledge Lab, Rice University, Houston, TX, United States
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
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37
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Wymant C, Ferretti L, Tsallis D, Charalambides M, Abeler-Dörner L, Bonsall D, Hinch R, Kendall M, Milsom L, Ayres M, Holmes C, Briers M, Fraser C. The epidemiological impact of the NHS COVID-19 app. Nature 2021; 594:408-412. [PMID: 33979832 DOI: 10.1038/s41586-021-03606-z] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/03/2021] [Indexed: 11/09/2022]
Abstract
The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention1-6, but its epidemiological impact has remained uncertain7. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.
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Affiliation(s)
- Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | | | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David Bonsall
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Robert Hinch
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Michelle Kendall
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Department of Statistics, University of Warwick, Coventry, UK
| | - Luke Milsom
- Department of Economics, University of Oxford, Oxford, UK
| | | | - Chris Holmes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,The Alan Turing Institute, London, UK.,Department of Statistics, University of Oxford, Oxford, UK
| | | | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
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38
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Barrat A, Cattuto C, Kivelä M, Lehmann S, Saramäki J. Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data. J R Soc Interface 2021. [PMID: 33947224 DOI: 10.1101/2020.07.24.20159947] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023] Open
Abstract
Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.
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Affiliation(s)
- A Barrat
- Aix Marseille Univ., CNRS, CPT, Turing Center for Living Systems, Université de Toulon, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - C Cattuto
- Computer Science Department, University of Turin, Turin, Italy
- ISI Foundation, Turin, Italy
| | - M Kivelä
- Department of Computer Science, Aalto University, Aalto, Finland
| | - S Lehmann
- Technical University of Denmark, Copenhagen, Denmark
| | - J Saramäki
- Department of Computer Science, Aalto University, Aalto, Finland
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39
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Barrat A, Cattuto C, Kivelä M, Lehmann S, Saramäki J. Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data. J R Soc Interface 2021; 18:20201000. [PMID: 33947224 PMCID: PMC8097511 DOI: 10.1098/rsif.2020.1000] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 04/13/2021] [Indexed: 12/25/2022] Open
Abstract
Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.
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Affiliation(s)
- A. Barrat
- Aix Marseille Univ., CNRS, CPT, Turing Center for Living Systems, Université de Toulon, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - C. Cattuto
- Computer Science Department, University of Turin, Turin, Italy
- ISI Foundation, Turin, Italy
| | - M. Kivelä
- Department of Computer Science, Aalto University, Aalto, Finland
| | - S. Lehmann
- Technical University of Denmark, Copenhagen, Denmark
| | - J. Saramäki
- Department of Computer Science, Aalto University, Aalto, Finland
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40
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Barrat A, Cattuto C, Kivelä M, Lehmann S, Saramäki J. Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data. J R Soc Interface 2021. [PMID: 33947224 DOI: 10.1101/2020.07.24.20159947v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023] Open
Abstract
Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.
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Affiliation(s)
- A Barrat
- Aix Marseille Univ., CNRS, CPT, Turing Center for Living Systems, Université de Toulon, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - C Cattuto
- Computer Science Department, University of Turin, Turin, Italy
- ISI Foundation, Turin, Italy
| | - M Kivelä
- Department of Computer Science, Aalto University, Aalto, Finland
| | - S Lehmann
- Technical University of Denmark, Copenhagen, Denmark
| | - J Saramäki
- Department of Computer Science, Aalto University, Aalto, Finland
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41
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Moreno López JA, Arregui García B, Bentkowski P, Bioglio L, Pinotti F, Boëlle PY, Barrat A, Colizza V, Poletto C. Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection. SCIENCE ADVANCES 2021; 7:eabd8750. [PMID: 33712416 PMCID: PMC8034853 DOI: 10.1126/sciadv.abd8750] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 02/22/2021] [Indexed: 05/12/2023]
Abstract
The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact and epidemiological information to describe exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity, and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for the elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.
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Affiliation(s)
- Jesús A Moreno López
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
| | - Beatriz Arregui García
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
| | - Piotr Bentkowski
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Livio Bioglio
- Department of Computer Science, University of Turin, Turin, Italy
| | - Francesco Pinotti
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Pierre-Yves Boëlle
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Chiara Poletto
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France.
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42
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Moreno López JA, Arregui García B, Bentkowski P, Bioglio L, Pinotti F, Boëlle PY, Barrat A, Colizza V, Poletto C. Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection. SCIENCE ADVANCES 2021; 7:sciadv.abd8750. [PMID: 33712416 DOI: 10.1101/2020.07.22.20158352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 02/22/2021] [Indexed: 05/21/2023]
Abstract
The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact and epidemiological information to describe exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity, and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for the elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.
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Affiliation(s)
- Jesús A Moreno López
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
| | - Beatriz Arregui García
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
| | - Piotr Bentkowski
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Livio Bioglio
- Department of Computer Science, University of Turin, Turin, Italy
| | - Francesco Pinotti
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Pierre-Yves Boëlle
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Chiara Poletto
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France.
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43
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Mancastroppa M, Castellano C, Vezzani A, Burioni R. Stochastic sampling effects favor manual over digital contact tracing. Nat Commun 2021; 12:1919. [PMID: 33772002 PMCID: PMC7997996 DOI: 10.1038/s41467-021-22082-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 02/25/2021] [Indexed: 02/01/2023] Open
Abstract
Isolation of symptomatic individuals, tracing and testing of their nonsymptomatic contacts are fundamental strategies for mitigating the current COVID-19 pandemic. The breaking of contagion chains relies on two complementary strategies: manual reconstruction of contacts based on interviews and a digital (app-based) privacy-preserving contact tracing. We compare their effectiveness using model parameters tailored to describe SARS-CoV-2 diffusion within the activity-driven model, a general empirically validated framework for network dynamics. We show that, even for equal probability of tracing a contact, manual tracing robustly performs better than the digital protocol, also taking into account the intrinsic delay and limited scalability of the manual procedure. This result is explained in terms of the stochastic sampling occurring during the case-by-case manual reconstruction of contacts, contrasted with the intrinsically prearranged nature of digital tracing, determined by the decision to adopt the app or not by each individual. The better performance of manual tracing is enhanced by heterogeneity in agent behavior: superspreaders not adopting the app are completely invisible to digital contact tracing, while they can be easily traced manually, due to their multiple contacts. We show that this intrinsic difference makes the manual procedure dominant in realistic hybrid protocols.
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Affiliation(s)
- Marco Mancastroppa
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, Parma, Italy
- INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, Parco Area delle Scienze, Parma, Italy
| | | | - Alessandro Vezzani
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, Parma, Italy
- Istituto dei Materiali per l'Elettronica ed il Magnetismo (IMEM-CNR), Parco Area delle Scienze, Parma, Italy
| | - Raffaella Burioni
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, Parma, Italy.
- INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, Parco Area delle Scienze, Parma, Italy.
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Cencetti G, Santin G, Longa A, Pigani E, Barrat A, Cattuto C, Lehmann S, Salathé M, Lepri B. Digital proximity tracing on empirical contact networks for pandemic control. Nat Commun 2021; 12:1655. [PMID: 33712583 PMCID: PMC7955065 DOI: 10.1038/s41467-021-21809-w] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/10/2021] [Indexed: 01/05/2023] Open
Abstract
Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15-20 minutes and closer than 2-3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.
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Affiliation(s)
| | - G Santin
- Fondazione Bruno Kessler, Trento, Italy
| | - A Longa
- Fondazione Bruno Kessler, Trento, Italy
- University of Trento, Trento, Italy
| | - E Pigani
- Fondazione Bruno Kessler, Trento, Italy
- University of Padua, Padua, Italy
| | - A Barrat
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - C Cattuto
- University of Turin, Turin, Italy
- ISI Foundation, Turin, Italy
| | - S Lehmann
- Technical University of Denmark, Copenhagen, Denmark
| | - M Salathé
- École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - B Lepri
- Fondazione Bruno Kessler, Trento, Italy.
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