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Muntoni AP, Mazza F, Braunstein A, Catania G, Dall'Asta L. Effectiveness of probabilistic contact tracing in epidemic containment: The role of superspreaders and transmission path reconstruction. PNAS NEXUS 2024; 3:pgae377. [PMID: 39285934 PMCID: PMC11404514 DOI: 10.1093/pnasnexus/pgae377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Accepted: 08/15/2024] [Indexed: 09/19/2024]
Abstract
The recent COVID-19 pandemic underscores the significance of early stage nonpharmacological intervention strategies. The widespread use of masks and the systematic implementation of contact tracing strategies provide a potentially equally effective and socially less impactful alternative to more conventional approaches, such as large-scale mobility restrictions. However, manual contact tracing faces strong limitations in accessing the network of contacts, and the scalability of currently implemented protocols for smartphone-based digital contact tracing becomes impractical during the rapid expansion phases of the outbreaks, due to the surge in exposure notifications and associated tests. A substantial improvement in digital contact tracing can be obtained through the integration of probabilistic techniques for risk assessment that can more effectively guide the allocation of diagnostic tests. In this study, we first quantitatively analyze the diagnostic and social costs associated with these containment measures based on contact tracing, employing three state-of-the-art models of SARS-CoV-2 spreading. Our results suggest that probabilistic techniques allow for more effective mitigation at a lower cost. Secondly, our findings reveal a remarkable efficacy of probabilistic contact-tracing techniques in performing backward and multistep tracing and capturing superspreading events.
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Affiliation(s)
- Anna Paola Muntoni
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
- Statistical inference and computational biology, Italian Institute for Genomic Medicine, c/o IRCSS, Candiolo 10060, Italy
| | - Fabio Mazza
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, Milano 20133, Italy
| | - Alfredo Braunstein
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
- Statistical inference and computational biology, Italian Institute for Genomic Medicine, c/o IRCSS, Candiolo 10060, Italy
| | - Giovanni Catania
- Departamento de Física Teórica, Universidad Complutense, Madrid 28040, Spain
| | - Luca Dall'Asta
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
- Statistical inference and computational biology, Italian Institute for Genomic Medicine, c/o IRCSS, Candiolo 10060, Italy
- Collegio Carlo Alberto, P.za Arbarello 8, Torino 10122, Italy
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2
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Cori A, Kucharski A. Inference of epidemic dynamics in the COVID-19 era and beyond. Epidemics 2024; 48:100784. [PMID: 39167954 DOI: 10.1016/j.epidem.2024.100784] [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: 03/22/2024] [Revised: 06/25/2024] [Accepted: 07/11/2024] [Indexed: 08/23/2024] Open
Abstract
The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.
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Affiliation(s)
- Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, United Kingdom.
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, United Kingdom.
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Kim J, Jo S, Cho SI. New framework to assess tracing and testing based on South Korea's response to COVID-19. BMC Infect Dis 2024; 24:469. [PMID: 38702610 PMCID: PMC11067276 DOI: 10.1186/s12879-024-09363-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 04/29/2024] [Indexed: 05/06/2024] Open
Abstract
South Korea's remarkable success in controlling the spread of COVID-19 during the pre-Omicron period was based on extensive contact tracing and large-scale testing. Here we suggest a general criterion for tracing and testing based on South Korea's experience, and propose a new framework to assess tracing and testing. We reviewed papers on South Korea's response to COVID-19 to capture its concept of tracing and testing. South Korea expanded its testing capabilities to enable group tracing combined with preemptive testing, and to conduct open testing. According to our proposed model, COVID-19 cases are classified into 4 types: confirmed in quarantine, source known, source unknown, and unidentified. The proportion of the first two case types among confirmed cases is defined as "traced proportion", and used as the indicator of tracing and testing effectiveness. In conclusion, South Korea successfully suppressed COVID-19 transmission by maintaining a high traced proportion (> 60%) using group tracing in conjunction with preemptive testing as a complementary strategy to traditional contact tracing.
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Heidecke J, Fuhrmann J, Barbarossa MV. A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities. PLoS One 2024; 19:e0299880. [PMID: 38470895 DOI: 10.1371/journal.pone.0299880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts-often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy-is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool. In this work, we present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates the assumption of limited TTIQ capacities, providing insights into the reduced effectiveness of testing and tracing in high prevalence scenarios. In addition, we account for potential transmission during the early phase of an infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Our numerical experiments inspired by the early spread of COVID-19 in Germany demonstrate the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent, which is representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses reveal both disease-dependent and disease-independent factors that impede or enhance the success of TTIQ. Studying the diminishing impact of TTIQ along simulations of an epidemic wave, we highlight consequences for intervention strategies.
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Affiliation(s)
- Julian Heidecke
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Jan Fuhrmann
- Institute of Applied Mathematics, Heidelberg University, Heidelberg, Germany
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Bayly H, Stoddard M, Van Egeren D, Murray EJ, Raifman J, Chakravarty A, White LF. Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic. BMC Public Health 2024; 24:595. [PMID: 38395830 PMCID: PMC10893709 DOI: 10.1186/s12889-024-18012-z] [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: 05/18/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.
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Affiliation(s)
- Henry Bayly
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | | | | | - Eleanor J Murray
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Julia Raifman
- Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA, USA
| | | | - Laura F White
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
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Zhao W, Wang X, Tang B. The impacts of spatial-temporal heterogeneity of human-to-human contacts on the extinction probability of infectious disease from branching process model. J Theor Biol 2024; 579:111703. [PMID: 38096979 DOI: 10.1016/j.jtbi.2023.111703] [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: 09/06/2023] [Revised: 11/26/2023] [Accepted: 12/07/2023] [Indexed: 12/18/2023]
Abstract
In this study, we focus on the impacts of spatial-temporal heterogeneity of human-to-human contacts on the spread of infectious diseases and develop a multi-type branching process model by introducing random human-to-human contact mode into a structured population. We provide the general formulas of the generation size, extinction probability, and basic reproduction number of the proposed branching process model. The result shows that the natural temporal heterogeneity (i.e. random contacts over time) can lead to a higher extinction probability while remains the same basic reproduction number and generation size. This is also numerically verified by choosing the real contact distributions from different circumstances of four countries. In addition, we observe a non-monotonic pattern of the differences, against the transmission probability and the mean contact rate, between the extinction probabilities under the constant and random contact patterns. Given the spatial heterogeneity, we show that it can contribute to the increase of basic reproduction number, but also increase the extinction probability of the infectious disease. This study adds novel insights to the course of the impact of heterogeneity on the transmission dynamics and also provides additional evidence for the limited role of reproduction numbers.
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Affiliation(s)
- Wuqiong Zhao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, PR China.
| | - Xia Wang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, PR China.
| | - Biao Tang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, PR China.
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Tóbiás R, Diouf ML, Cozijn FMJ, Ubachs W, Császár AG. All paths lead to hubs in the spectroscopic networks of water isotopologues H 216O and H 218O. Commun Chem 2024; 7:34. [PMID: 38365971 PMCID: PMC10873357 DOI: 10.1038/s42004-024-01103-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024] Open
Abstract
Network theory has fundamentally transformed our comprehension of complex systems, catalyzing significant advances across various domains of science and technology. In spectroscopic networks, hubs are the quantum states involved in the largest number of transitions. Here, utilizing network paths probed via precision metrology, absolute energies have been deduced, with at least 10-digit accuracy, for almost 200 hubs in the experimental spectroscopic networks of H216O and H218O. These hubs, lying on the ground vibrational states of both species and the bending fundamental of H216O, are involved in tens of thousands of observed transitions. Relying on the same hubs and other states, benchmark-quality line lists have been assembled, which supersede and improve, by three orders of magnitude, the accuracy of the massive amount of data reported in hundreds of papers dealing with Doppler-limited spectroscopy. Due to the omnipresence of water, these ultraprecise line lists could be applied to calibrate high-resolution spectra and serve ongoing and upcoming space missions.
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Affiliation(s)
- Roland Tóbiás
- Laboratory of Molecular Structure and Dynamics, Institute of Chemistry, ELTE Eötvös Loránd University and HUN-REN-ELTE Complex Chemical Systems Research Group, H-1117 Budapest, Pázmány Péter sétány 1/A, Hungary
| | - Meissa L Diouf
- Department of Physics and Astronomy, LaserLaB, Vrije Universiteit, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands
| | - Frank M J Cozijn
- Department of Physics and Astronomy, LaserLaB, Vrije Universiteit, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands
| | - Wim Ubachs
- Department of Physics and Astronomy, LaserLaB, Vrije Universiteit, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands.
| | - Attila G Császár
- Laboratory of Molecular Structure and Dynamics, Institute of Chemistry, ELTE Eötvös Loránd University and HUN-REN-ELTE Complex Chemical Systems Research Group, H-1117 Budapest, Pázmány Péter sétány 1/A, Hungary.
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Hijano DR, Dennis SR, Hoffman JM, Tang L, Hayden RT, Gaur AH, Hakim H. Employee investigation and contact tracing program in a pediatric cancer hospital to mitigate the spread of COVID-19 among the workforce, patients, and caregivers. Front Public Health 2024; 11:1304072. [PMID: 38259752 PMCID: PMC10801179 DOI: 10.3389/fpubh.2023.1304072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Background Case investigations and contact tracing are essential disease control measures used by health departments. Early in the pandemic, they were seen as a key strategy to stop COVID-19 spread. The CDC urged rapid action to scale up and train a large workforce and collaborate across public and private agencies to halt COVID-19 transmission. Methods We developed a program for case investigation and contact tracing that followed CDC and local health guidelines, compliant with the Occupational Safety and Health Administration (OSHA) regulations and tailored to the needs and resources of our institution. Program staff were trained and assessed for competency before joining the program. Results From March 2020 to May 2021, we performed 838 COVID-19 case investigations, which led to 136 contacts. Most employees reported a known SARS-CoV-2 exposure from the community (n = 435) or household (n = 343). Only seven (5.1%) employees were determined as more likely than not to have SARS-CoV-2 infection related to workplace exposure, and when so, lapses in following the masking recommendations were identified. Between June 2021-February 2022, our program adjusted to the demand of the different waves, particularly omicron, by significantly reducing the amount of data collected. No transmission from employees to patients or caregivers was observed during this period. Conclusion Prompt implementation of case investigation and contact tracing is possible, and it effectively reduces workplace exposures. This approach can be adapted to suit the specific needs and requirements of various healthcare settings, particularly those serving the most vulnerable patient populations.
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Affiliation(s)
- Diego R. Hijano
- Departments of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, TN, United States
- Department of Pediatrics, University of Tennessee Health Sciences Center, Memphis, TN, United States
| | - Sandra R. Dennis
- Department of Human Resources, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - James M. Hoffman
- Department of Human Resources, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Li Tang
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Randall T. Hayden
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | | | - Aditya H. Gaur
- Departments of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Hana Hakim
- Office of Quality and Patient Safety, St. Jude Children’s Research Hospital, Memphis, TN, United States
- Department of Preventive Medicine, University of Tennessee Health Sciences Center, Memphis, TN, United States
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Mongin D, Bürgisser N, Courvoisier DS. Time trends and modifiable factors of COVID-19 contact tracing coverage, Geneva, Switzerland, June 2020 to February 2022. Euro Surveill 2024; 29:2300228. [PMID: 38240059 PMCID: PMC10797663 DOI: 10.2807/1560-7917.es.2024.29.3.2300228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/19/2023] [Indexed: 01/22/2024] Open
Abstract
BackgroundContact tracing was one of the central non-pharmaceutical interventions implemented worldwide to control the spread of SARS-CoV-2, but its effectiveness depends on its ability to detect contacts.AimEvaluate the proportion of secondary infections captured by the contact tracing system in Geneva.MethodsWe analysed 166,892 concomitant infections occurring at the same given address from June 2020 until February 2022 using an extensive operational database of SARS-CoV-2 tests in Geneva. We used permutation to compare the total number of secondary infections occurring at the same address with that reported through manual contact tracing.ResultsContact tracing captured on average 41% of secondary infections, varying from 23% during epidemic peaks to 60% during low epidemic activity. People living in wealthy neighbourhoods were less likely to report contacts (odds ratio (OR): 1.6). People living in apartment buildings were also less likely to report contacts than those living in a house (OR: 1.1-3.1) depending on the SARS-CoV-2 variant, the building size and the presence of shops. This under-reporting of contacts in apartment buildings decreased during periods of mandatory wearing of face masks and restrictions on private gatherings.ConclusionContact tracing alone did not detect sufficient secondary infections to reduce the spread of SARS-CoV-2. Campaigns targeting specific populations, such as those in wealthy areas or apartment buildings, could enhance coverage. Additionally, measures like wearing face masks, improving ventilation and implementing restrictions on gatherings should also be considered to reduce infections resulting from interactions that may not be perceived as high risk.
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Affiliation(s)
- Denis Mongin
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nils Bürgisser
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- General internal medicine division, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland
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Ter Haar W, Bosdriesz J, Venekamp RP, Schuit E, van den Hof S, Ebbers W, Kretzschmar M, Kluijtmans J, Moons C, Schim van der Loeff M, Matser A, van de Wijgert JHHM. The epidemiological impact of digital and manual contact tracing on the SARS-CoV-2 epidemic in the Netherlands: Empirical evidence. PLOS DIGITAL HEALTH 2023; 2:e0000396. [PMID: 38157381 PMCID: PMC10756539 DOI: 10.1371/journal.pdig.0000396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 10/23/2023] [Indexed: 01/03/2024]
Abstract
The Dutch government introduced the CoronaMelder smartphone application for digital contact tracing (DCT) to complement manual contact tracing (MCT) by Public Health Services (PHS) during the 2020-2022 SARS-CoV-2 epidemic. Modelling studies showed great potential but empirical evidence of DCT and MCT impact is scarce. We determined reasons for testing, and mean exposure-testing intervals by reason for testing, using routine data from PHS Amsterdam (1 December 2020 to 31 May 2021) and data from two SARS-CoV-2 rapid diagnostic test accuracy studies at other PHS sites in the Netherlands (14 December 2020 to 18 June 2021). Throughout the study periods, notification of DCT-identified contacts was via PHS contact-tracers, and self-testing was not yet widely available. The most commonly reported reason for testing was having symptoms. In asymptomatic individuals, it was having been warned by an index case. Only around 2% and 2-5% of all tests took place after DCT or MCT notification, respectively. About 20-36% of those who had received a DCT or MCT notification had symptoms at the time of test request. Test positivity after a DCT notification was significantly lower, and exposure-test intervals after a DCT or MCT notification were longer, than for the above-mentioned other reasons for testing. Our data suggest that the impact of DCT and MCT on the SARS-CoV-2 epidemic in the Netherlands was limited. However, DCT impact might be enlarged if app use coverage is improved, contact-tracers are eliminated from the digital notification process to minimise delays, and DCT is combined with self-testing.
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Affiliation(s)
- Wianne Ter Haar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Public Health Service (GGD) of Amsterdam, Amsterdam, Netherlands
| | - Jizzo Bosdriesz
- Public Health Service (GGD) of Amsterdam, Amsterdam, Netherlands
- Department of Internal Medicine, Division of Infectious Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Roderick P. Venekamp
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Susan van den Hof
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Wolfgang Ebbers
- Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Mirjam Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Jan Kluijtmans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Carl Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten Schim van der Loeff
- Public Health Service (GGD) of Amsterdam, Amsterdam, Netherlands
- Department of Internal Medicine, Division of Infectious Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Amy Matser
- Public Health Service (GGD) of Amsterdam, Amsterdam, Netherlands
- Department of Internal Medicine, Division of Infectious Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Janneke H. H. M. van de Wijgert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
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Rudin C, Bollen N, Hong SL, Wegner F, Politi L, Mellou K, Geenen C, Gorissen S, Verhasselt B, Durkin K, Henin C, Logist AS, Dellicour S, Resa T, Stadler T, Maes P, Cuypers L, André E, Egli A, Baele G. Investigation of an international water polo tournament in Czechia as a potential source for early introduction of the SARS-CoV-2 Omicron variant into Belgium, Switzerland and Germany, November 2021. Euro Surveill 2023; 28:2300018. [PMID: 37943503 PMCID: PMC10636743 DOI: 10.2807/1560-7917.es.2023.28.45.2300018] [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: 12/29/2022] [Accepted: 06/28/2023] [Indexed: 11/10/2023] Open
Abstract
BackgroundThe earliest recognised infections by the SARS-CoV-2 Omicron variant (Pango lineage B.1.1.529) in Belgium and Switzerland suggested a connection to an international water polo tournament, held 12-14 November 2021 in Brno, Czechia.AimTo study the arrival and subsequent spread of the Omicron variant in Belgium and Switzerland, and understand the overall importance of this international sporting event on the number of infections in the two countries.MethodsWe performed intensive forward and backward contact tracing in both countries, supplemented by phylogenetic investigations using virus sequences of the suspected infection chain archived in public databases.ResultsThrough contact tracing, we identified two and one infected athletes of the Belgian and Swiss water polo teams, respectively, and subsequently also three athletes from Germany. In Belgium and Switzerland, four and three secondary infections, and three and one confirmed tertiary infections were identified. Phylogenetic investigation demonstrated that this sporting event played a role as the source of infection, but without a direct link with infections from South Africa and not as a superspreading event; the virus was found to already be circulating at that time in the countries involved.ConclusionThe SARS-CoV-2 Omicron variant started to circulate in Europe several weeks before its identification in South Africa on 24 November 2021. Accordingly, it can be assumed that travel restrictions are usually implemented too late to prevent the spread of newly detected SARS-CoV-2 variants to other regions. Phylogenetic analysis may modify the perception of an apparently clear result of intensive contact tracing.
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Affiliation(s)
| | - Nena Bollen
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Samuel L Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Fanny Wegner
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
- Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Lida Politi
- Department of Microbial Resistance and Infections in Health Care Settings, Directorate of Surveillance and Prevention of Infectious Diseases, Hellenic National Public Health Organization (EODY), Athens, Greece
- European Programme for Intervention Epidemiology Training (EPIET), European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Kassiani Mellou
- Directorate of Epidemiological Surveillance and Intervention for Infectious Diseases, Hellenic National Public Health Organization (EODY), Athens, Greece
| | - Caspar Geenen
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, KU Leuven, Leuven, Belgium
| | - Sarah Gorissen
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, KU Leuven, Leuven, Belgium
| | - Bruno Verhasselt
- Department of Diagnostic Sciences, Ghent University Hospital, Ghent University, Ghent, Belgium
| | - Keith Durkin
- Laboratory of Human Genetics, GIGA Research Institute, Liège, Belgium
| | - Coralie Henin
- Federal testing platform COVID-19, Université libre de Bruxelles, Bruxelles, Belgium
| | - Anne-Sophie Logist
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Tobias Resa
- Cantonal Office of Public Health Basel-Landschaft, Liestal, Switzerland
| | - Tanja Stadler
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Piet Maes
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Lize Cuypers
- Department of Laboratory Medicine, National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Leuven, Belgium
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, KU Leuven, Leuven, Belgium
| | - Emmanuel André
- Department of Laboratory Medicine, National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Leuven, Belgium
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, KU Leuven, Leuven, Belgium
| | - Adrian Egli
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
- Swiss Pathogen Surveillance Platform (https://spsp.ch)
- Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
- Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
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de Meijere G, Castellano C. Limited efficacy of forward contact tracing in epidemics. Phys Rev E 2023; 108:054305. [PMID: 38115421 DOI: 10.1103/physreve.108.054305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/16/2023] [Indexed: 12/21/2023]
Abstract
Infectious diseases that spread silently through asymptomatic or pre-symptomatic infections represent a challenge for policy makers. A traditional way of achieving isolation of silent infectors from the community is through forward contact tracing, aimed at identifying individuals that might have been infected by a known infected person. In this work we investigate how efficient this measure is in preventing a disease from becoming endemic. We introduce an SIS-based compartmental model where symptomatic individuals may self-isolate and trigger a contact tracing process aimed at quarantining asymptomatic infected individuals. Imperfect adherence and delays affect both measures. We derive the epidemic threshold analytically and find that contact tracing alone can only lead to a very limited increase of the threshold. We quantify the effect of imperfect adherence and the impact of incentivizing asymptomatic and symptomatic populations to adhere to isolation. Our analytical results are confirmed by simulations on complex networks and by the numerical analysis of a much more complex model incorporating more realistic in-host disease progression.
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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 Roma, Italy
| | - Claudio Castellano
- Istituto dei Sistemi Complessi (ISC-CNR), Via dei Taurini 19, I-00185 Roma, Italy
- Centro Ricerche Enrico Fermi, Piazza del Viminale, 1, I-00184 Rome, Italy
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13
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Heron L, Mugglin C, Zürcher K, Brumann E, Keune-Dübi B, Low N, Fenner L. Contact tracing for COVID-19 in a Swiss canton: analysis of key performance indicators. Swiss Med Wkly 2023; 153:40112. [PMID: 37955850 DOI: 10.57187/smw.2023.40112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Contact tracing (CT) has played an important role in strategies to control COVID-19. However, there is limited evidence on the performance of digital tools for CT and no consensus on which indicators to use to monitor their performance. We aimed to describe the system and analyse outcomes of CT with a partially automated workflow in the Swiss canton of Solothurn, using key performance indicators (KPIs). METHODS We describe the process of CT used in the canton of Solothurn between November 2020 and February 2022, including forward and backward CT. We developed 16 KPIs representing CT structure (S1-2), process (P1-11) and outcome (O1-3) based on previous literature to analyse the relative performance of CT. We report the changes in the indicators over waves of SARS-CoV-2 infections caused by several viral variants. RESULTS The CT team in Solothurn processed 57,363 index cases and 71,809 contacts over a 15-month period. The CT team successfully contacted 99% of positive cases within 24 hours (KPI P7) throughout the pandemic and returned almost all test results on the same or next day (KPI P6), before the delta variant emerged. Three-quarters of contacts were notified within 24 hours of the CT interview with the index (KPI P8) before the emergence of the alpha, delta and omicron variants, when the proportions decreased to 64%, 36% and 54%, respectively. The percentage of new symptomatic cases tested and interviewed within 3 days of symptom onset was high at >70% (KPI P10) and contacts started quarantine within a median of 3 days of index case symptom onset (KPI P3). About a fifth of new index cases had already been in quarantine by the time of their positive test (KPI O1), before the delta variant emerged. The percentage of index cases in isolation by day of testing remained at almost 100% throughout the period of analysis (KPI O2). CONCLUSIONS The CT in Solothurn used a partially automated workflow and continued to perform well throughout the pandemic, although the relative performance of the CT system declined at higher caseloads. CT remains an important tool for controlling the spread of infectious diseases, but clearer standards should improve the performance, comparability and monitoring of infection in real time as part of pandemic preparedness efforts.
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Affiliation(s)
- Leonie Heron
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Catrina Mugglin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Kathrin Zürcher
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Erich Brumann
- Cantonal Physician's Office, Canton of Solothurn, Solothurn, Switzerland
| | - Bettina Keune-Dübi
- Cantonal Physician's Office, Canton of Solothurn, Solothurn, Switzerland
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Lukas Fenner
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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14
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Boudreau MC, Allen AJ, Roberts NJ, Allard A, Hébert-Dufresne L. Temporal and Probabilistic Comparisons of Epidemic Interventions. Bull Math Biol 2023; 85:118. [PMID: 37857996 PMCID: PMC11216031 DOI: 10.1007/s11538-023-01220-w] [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: 03/08/2023] [Accepted: 09/26/2023] [Indexed: 10/21/2023]
Abstract
Forecasting disease spread is a critical tool to help public health officials design and plan public health interventions. However, the expected future state of an epidemic is not necessarily well defined as disease spread is inherently stochastic, contact patterns within a population are heterogeneous, and behaviors change. In this work, we use time-dependent probability generating functions (PGFs) to capture these characteristics by modeling a stochastic branching process of the spread of a disease over a network of contacts in which public health interventions are introduced over time. To achieve this, we define a general transmissibility equation to account for varying transmission rates (e.g. masking), recovery rates (e.g. treatment), contact patterns (e.g. social distancing) and percentage of the population immunized (e.g. vaccination). The resulting framework allows for a temporal and probabilistic analysis of an intervention's impact on disease spread, which match continuous-time stochastic simulations that are much more computationally expensive. To aid policy making, we then define several metrics over which temporal and probabilistic intervention forecasts can be compared: Looking at the expected number of cases and the worst-case scenario over time, as well as the probability of reaching a critical level of cases and of not seeing any improvement following an intervention. Given that epidemics do not always follow their average expected trajectories and that the underlying dynamics can change over time, our work paves the way for more detailed short-term forecasts of disease spread and more informed comparison of intervention strategies.
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Affiliation(s)
- Mariah C Boudreau
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA.
- Department of Mathematics & Statistics, University of Vermont, Burlington, VT, USA.
| | - Andrea J Allen
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA
- Children's Hospital of Philadelphia, Applied Clinical Research Center, Philadelphia, PA, USA
| | - Nicholas J Roberts
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA
| | - Antoine Allard
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA
- Départment de Physique, de génie physique et d'optique, Université Laval, Québec, Québec, G1V 0A6, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, Québec, G1V 0A6, Canada
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA
- Department of Mathematics & Statistics, University of Vermont, Burlington, VT, USA
- Départment de Physique, de génie physique et d'optique, Université Laval, Québec, Québec, G1V 0A6, Canada
- Department of Computer Science, University of Vermont, Burlington, VT, USA
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15
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Littlecott H, Herd C, O'Rourke J, Chaparro LT, Keeling M, James Rubin G, Fearon E. Effectiveness of testing, contact tracing and isolation interventions among the general population on reducing transmission of SARS-CoV-2: a systematic review. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230131. [PMID: 37611628 PMCID: PMC10446909 DOI: 10.1098/rsta.2023.0131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
We conducted a systematic literature review of general population testing, contact tracing, case isolation and contact quarantine interventions to assess their effectiveness in reducing SARS-CoV-2 transmission, as implemented in real-world settings. We designed a broad search strategy and aimed to identify peer-reviewed studies of any design provided there was a quantitative measure of effectiveness on a transmission outcome. Studies that assessed the effect of testing or diagnosis on disease outcomes via treatment, but did not assess a transmission outcome, were not included. We focused on interventions implemented among the general population rather than in specific settings; these were from anywhere in the world and published any time after 1 January 2020 until the end of 2022. From 26 720 titles and abstracts, 1181 were reviewed as full text, and 25 met our inclusion criteria. These 25 studies included one randomized control trial (RCT) and the remaining 24 analysed empirical data and made some attempt to control for confounding. Studies included were categorized by the type of intervention: contact tracing (seven studies); specific testing strategies (12 studies); strategies for isolating cases/contacts (four studies); and 'test, trace, isolate' (TTI) as a part of a package of interventions (two studies). None of the 25 studies were rated at low risk of bias and many were rated as serious risk of bias, particularly due to the likely presence of uncontrolled confounding factors, which was a major challenge in assessing the independent effects of TTI in observational studies. These confounding factors are to be expected from observational studies during an on-going pandemic, when the emphasis was on reducing the epidemic burden rather than trial design. Findings from these 25 studies suggested an important public health role for testing followed by isolation, especially where mass and serial testing was used to reduce transmission. Some of the most compelling analyses came from examining fine-grained within-country data on contact tracing; while broader studies which compared behaviour between countries also often found TTI led to reduced transmission and mortality, this was not universal. There was limited evidence for the benefit of isolation of cases/contacts away from the home environment. One study, an RCT, showed that daily testing of contacts could be a viable strategy to replace lengthy quarantine of contacts. Based on the scarcity of robust empirical evidence, we were not able to draw any firm quantitative conclusions about the quantitative impact of TTI interventions in different epidemic contexts. While the majority of studies found that testing, tracing and isolation reduced transmission, evidence for the scale of this impact is only available for specific scenarios and hence is not necessarily generalizable. Our review therefore emphasizes the need to conduct robust experimental studies that help inform the likely quantitative impact of different TTI interventions on transmission and their optimal design. Work is needed to support such studies in the context of future emerging epidemics, along with assessments of the cost-effectiveness of TTI interventions, which was beyond the scope of this review but will be critical to decision-making. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Hannah Littlecott
- Institute for Medical Information Processing, Biometry and Epidemiology-IBE, Chair of Public Health and Health Services Research, LMU Munich, Germany
| | - Clare Herd
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
| | - John O'Rourke
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
| | - Lina Toncon Chaparro
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
| | - Matt Keeling
- Zeeman Institute (SBIDER), Mathematics Institute and School of Life Sciences, University of Warwick, Coventry, UK
- JUNIPER consortium, UK
| | - G James Rubin
- Department of Psychological Medicine, King's College London, London, UK
| | - Elizabeth Fearon
- Institute for Global Health, Faculty of Population Health Sciences, University College London, London, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
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16
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Boelsums TL, van de Luitgaarden IAT, Whelan J, Poell H, Hoffman CM, Fanoy E, Buskermolen M, Richardus JH. The value of manual backward contact tracing to control COVID-19 in practice, the Netherlands, February to March 2021: a pilot study. Euro Surveill 2023; 28:2200916. [PMID: 37824253 PMCID: PMC10571494 DOI: 10.2807/1560-7917.es.2023.28.41.2200916] [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: 11/18/2022] [Accepted: 06/20/2023] [Indexed: 10/14/2023] Open
Abstract
BackgroundContact tracing has been a key component of COVID-19 outbreak control. Backward contact tracing (BCT) aims to trace the source that infected the index case and, thereafter, the cases infected by the source. Modelling studies have suggested BCT will substantially reduce SARS-CoV-2 transmission in addition to forward contact tracing.AimTo assess the feasibility and impact of adding BCT in practice.MethodsWe identified COVID-19 cases who were already registered in the electronic database between 19 February and 10 March 2021 for routine contact tracing at the Public Health Service (PHS) of Rotterdam-Rijnmond, the Netherlands (pop. 1.3 million). We investigated if, through a structured questionnaire by dedicated contact tracers, we could trace additional sources and cases infected by these sources. Potential sources identified by the index were approached to trace the source's contacts. We evaluated the number of source contacts that could be additionally quarantined.ResultsOf 7,448 COVID-19 cases interviewed in the study period, 47% (n = 3,497) indicated a source that was already registered as a case in the PHS electronic database. A potential, not yet registered source was traced in 13% (n = 979). Backward contact tracing was possible in 62 of 979 cases, from whom an additional 133 potential sources were traced, and four were eligible for tracing of source contacts. Two additional contacts traced had to stay in quarantine for 1 day. No new COVID-19 cases were confirmed.ConclusionsThe addition of manual BCT to control the COVID-19 pandemic did not provide added value in our study setting.
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Affiliation(s)
- Timo Louis Boelsums
- Department of Infectious Disease Control, Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
| | | | - Jane Whelan
- Department of Infectious Disease Control, Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
| | - Hanna Poell
- Department of Infectious Disease Control, Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
| | - Charlotte Maria Hoffman
- Department of Infectious Disease Control, Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
| | - Ewout Fanoy
- Department of Infectious Disease Control, Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
- Department of Infectious Disease Control, Public Health Service Amsterdam-Amstelland, Amsterdam, the Netherlands
| | - Maaike Buskermolen
- Department of Infectious Disease Control, Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
| | - Jan Hendrik Richardus
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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17
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Valgañón P, Useche AF, Soriano-Paños D, Ghoshal G, Gómez-Gardeñes J. Quantifying the heterogeneous impact of lockdown policies on different socioeconomic classes during the first COVID-19 wave in Colombia. Sci Rep 2023; 13:16481. [PMID: 37777581 PMCID: PMC10542364 DOI: 10.1038/s41598-023-43685-8] [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: 02/27/2023] [Accepted: 09/27/2023] [Indexed: 10/02/2023] Open
Abstract
In the absence of vaccines, the most widespread reaction to curb the COVID-19 pandemic worldwide was the implementation of lockdowns or stay-at-home policies. Despite the reported usefulness of such policies, their efficiency was highly constrained by socioeconomic factors determining their feasibility and their associated outcome in terms of mobility reduction and the subsequent limitation of social activity. Here we investigate the impact of lockdown policies on the mobility patterns of different socioeconomic classes in the three major cities of Colombia during the first wave of the COVID-19 pandemic. In global terms, we find a consistent positive correlation between the reduction in mobility levels and the socioeconomic stratum of the population in the three cities, implying that those with lower incomes were less capable of adopting the aforementioned policies. Our analysis also suggests a strong restructuring of the mobility network of lowest socioeconomic strata during COVID-19 lockdown, increasing their endogenous mixing while hampering their connections with wealthiest areas due to a sharp reduction in long-distance trips.
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Affiliation(s)
- Pablo Valgañón
- Departament of Condensed Matter Physics, University of Zaragoza, 50009, Zaragoza, Spain
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018, Zaragoza, Spain
| | - Andrés F Useche
- Department of Industrial Engineering, School of Engineering, Universidad de Los Andes, 111711, Bogotá, Colombia
| | - David Soriano-Paños
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018, Zaragoza, Spain.
- Instituto Gulbenkian de Ciência, 2780-156, Oeiras, Portugal.
| | - Gourab Ghoshal
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, 14627, USA
| | - Jesús Gómez-Gardeñes
- Departament of Condensed Matter Physics, University of Zaragoza, 50009, Zaragoza, Spain
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018, Zaragoza, Spain
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18
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Juul JL, Strogatz SH. Comparing the efficiency of forward and backward contact tracing. Phys Rev E 2023; 108:034308. [PMID: 37849148 DOI: 10.1103/physreve.108.034308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 06/28/2023] [Indexed: 10/19/2023]
Abstract
Tracing potentially infected contacts of confirmed cases is important when fighting outbreaks of many infectious diseases. The COVID-19 pandemic has motivated researchers to examine how different contact tracing strategies compare in terms of effectiveness (ability to mitigate infections) and cost efficiency (number of prevented infections per isolation). Two important strategies are so-called forward contact tracing (tracing to whom disease spreads) and backward contact tracing (tracing from whom disease spreads). Recently, Kojaku and colleagues reported that backward contact tracing was "profoundly more effective" than forward contact tracing, that contact tracing effectiveness "hinges on reaching the 'source' of infection," and that contact tracing outperformed case isolation in terms of cost efficiency. Here we show that these conclusions are not true in general. They were based in part on simulations that vastly overestimated the effectiveness and efficiency of contact tracing. Our results show that the efficiency of contact tracing strategies is highly contextual; faced with a disease outbreak, the disease dynamics determine whether tracing infection sources or new cases is more impactful. Our results also demonstrate the importance of simulating disease spread and mitigation measures in parallel rather than sequentially.
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Affiliation(s)
- Jonas L Juul
- Center for Applied Mathematics, Cornell University, Ithaca, New York 14853, USA
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Steven H Strogatz
- Center for Applied Mathematics, Cornell University, Ithaca, New York 14853, USA
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19
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Lamata-Otín S, Reyna-Lara A, Soriano-Paños D, Latora V, Gómez-Gardeñes J. Collapse transition in epidemic spreading subject to detection with limited resources. Phys Rev E 2023; 108:024305. [PMID: 37723687 DOI: 10.1103/physreve.108.024305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/21/2023] [Indexed: 09/20/2023]
Abstract
Compartmental models are the most widely used framework for modeling infectious diseases. These models have been continuously refined to incorporate all the realistic mechanisms that can shape the course of an epidemic outbreak. Building on a compartmental model that accounts for early detection and isolation of infectious individuals through testing, in this article we focus on the viability of detection processes under limited availability of testing resources, and we study how the latter impacts on the detection rate. Our results show that, in addition to the well-known epidemic transition at R_{0}=1, a second transition occurs at R_{0}^{★}>1 pinpointing the collapse of the detection system and, as a consequence, the switch from a regime of mitigation to a regime in which the pathogen spreads freely. We characterize the epidemic phase diagram of the model as a function of the relevant control parameters: the basic reproduction number, the maximum detection capacity of the system, and the fraction of individuals in shelter. Our analysis thus provides a valuable tool for estimating the detection resources and the level of confinement needed to face epidemic outbreaks.
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Affiliation(s)
- Santiago Lamata-Otín
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
- GOTHAM lab, Institute of Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
| | - Adriana Reyna-Lara
- Instituto Tecnológico y de Estudios Superiores de Monterrey, 64849 Monterrey, Nuevo León, México
| | - David Soriano-Paños
- GOTHAM lab, Institute of Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
- Institute Gulbenkian of Science (IGC), 2780-156 Oeiras, Portugal
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
- Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, I-95123 Catania, Italy
- Complexity Science Hub Vienna, A-1080 Vienna, Austria
| | - Jesús Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
- GOTHAM lab, Institute of Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
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20
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Kanté DSI, Jebrane A, Hakim A, Boukamel A. Characterization of superspreaders movement in a bidirectional corridor using a social force model. Front Public Health 2023; 11:1188732. [PMID: 37575110 PMCID: PMC10416642 DOI: 10.3389/fpubh.2023.1188732] [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: 03/17/2023] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
During infectious disease outbreaks, some infected individuals may spread the disease widely and amplify risks in the community. People whose daily activities bring them in close proximity to many others can unknowingly become superspreaders. The use of contact tracking based on social networks, GPS, or mobile tracking data can help to identify superspreaders and break the chain of transmission. We propose a model that aims at providing insight into risk factors of superspreading events. Here, we use a social force model to estimate the superspreading potential of individuals walking in a bidirectional corridor. First, we applied the model to identify parameters that favor exposure to an infectious person in scattered crowds. We find that low walking speed and high body mass both increase the expected number of close exposures. Panic events exacerbate the risks while social distancing reduces both the number and duration of close encounters. Further, in dense crowds, pedestrians interact more and cannot easily maintain the social distance between them. The number of exposures increases with the density of person in the corridor. The study of movements reveals that individuals walking toward the center of the corridor tend to rotate and zigzag more than those walking along the edges, and thus have higher risks of superspreading. The corridor model can be applied to designing risk reduction measures for specific high volume venues, including transit stations, stadiums, and schools.
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Affiliation(s)
- Dramane Sam Idris Kanté
- LAMAI, Department of Mathematics, Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakesh, Morocco
- Centrale Casablanca, Complex Systems and Interactions Research Center, Ville Verte, Bouskoura, Morocco
| | - Aissam Jebrane
- Centrale Casablanca, Complex Systems and Interactions Research Center, Ville Verte, Bouskoura, Morocco
| | - Abdelilah Hakim
- LAMAI, Department of Mathematics, Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakesh, Morocco
| | - Adnane Boukamel
- Centrale Casablanca, Complex Systems and Interactions Research Center, Ville Verte, Bouskoura, Morocco
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21
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Oeltmann JE, Vohra D, Matulewicz HH, DeLuca N, Smith JP, Couzens C, Lash RR, Harvey B, Boyette M, Edwards A, Talboy PM, Dubose O, Regan P, Loosier P, Caruso E, Katz DJ, Taylor MM, Moonan PK. Isolation and Quarantine for Coronavirus Disease 2019 in the United States, 2020-2022. Clin Infect Dis 2023; 77:212-219. [PMID: 36947142 PMCID: PMC11094624 DOI: 10.1093/cid/ciad163] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/21/2023] [Accepted: 03/17/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Public health programs varied in ability to reach people with coronavirus disease 2019 (COVID-19) and their contacts to encourage separation from others. For both adult case patients with COVID-19 and their contacts, we estimated the impact of contact tracing activities on separation behaviors from January 2020 until March 2022. METHODS We used a probability-based panel survey of a nationally representative sample to gather data for estimates and comparisons. RESULTS An estimated 64 255 351 adults reported a positive severe acute respiratory syndrome coronavirus 2 test result; 79.6% isolated for ≥5 days, 60.2% isolated for ≥10 days, and 79.2% self-notified contacts. A total of, 24 057 139 (37.7%) completed a case investigation, and 46.2% of them reported contacts to health officials. More adults who completed a case investigation isolated than those who did not complete a case investigation (≥5 days, 82.6% vs 78.2%, respectively; ≥10 days, 69.8% vs 54.8%; both P < .05). A total of 84 946 636 adults were contacts of a COVID-19 case patient. Of these, 73.1% learned of their exposure directly from a case patient; 49.4% quarantined for ≥5 days, 18.7% quarantined for ≥14 days, and 13.5% completed a contact tracing call. More quarantined among those who completed a contact tracing call than among those who did not complete a tracing call (≥5 days, 61.2% vs 48.5%, respectively; ≥14 days, 25.2% vs 18.0%; both P < .05). CONCLUSIONS Engagement in contact tracing was positively correlated with isolation and quarantine. However, most adults with COVID-19 isolated and self-notified contacts regardless of whether the public health workforce was able to reach them. Identifying and reaching contacts was challenging and limited the ability to promote quarantining, and testing.
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Affiliation(s)
- John E Oeltmann
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Divya Vohra
- Health Division, Mathematica, Princeton, New Jersey, USA
| | | | - Nickolas DeLuca
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Jonathan P Smith
- School of Public Health, Yale University, New Haven, Connecticut, USA
| | | | - R Ryan Lash
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Barrington Harvey
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Melissa Boyette
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Alicia Edwards
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Philip M Talboy
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Odessa Dubose
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Paul Regan
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Penny Loosier
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Elise Caruso
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Dolores J Katz
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Melanie M Taylor
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
| | - Patrick K Moonan
- US Centers for Disease Control and Prevention, COVID-19 Response Team, Atlanta, Georgia, USA
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22
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Bayly H, Stoddard M, Egeren DV, Murray EJ, Raifman J, Chakravarty A, White LF. Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic. RESEARCH SQUARE 2023:rs.3.rs-2953875. [PMID: 37333276 PMCID: PMC10274953 DOI: 10.21203/rs.3.rs-2953875/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests (with a high false negative rate) due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62%-1.68%) of transmission events with PCR testing and 0.88% (95% uncertainty interval 0.86%-0.89%) with rapid antigen testing. When considering an optimal scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6%-62.8%). These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.
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23
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Nie Y, Zhong M, Li R, Zhao D, Peng H, Zhong X, Lin T, Wang W. Digital contact tracing on hypergraphs. CHAOS (WOODBURY, N.Y.) 2023; 33:063146. [PMID: 37347642 DOI: 10.1063/5.0149384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/05/2023] [Indexed: 06/24/2023]
Abstract
The higher-order interactions emerging in the network topology affect the effectiveness of digital contact tracing (DCT). In this paper, we propose a mathematical model in which we use the hypergraph to describe the gathering events. In our model, the role of DCT is modeled as individuals carrying the app. When the individuals in the hyperedge all carry the app, epidemics cannot spread through this hyperedge. We develop a generalized percolation theory to investigate the epidemic outbreak size and threshold. We find that DCT can effectively suppress the epidemic spreading, i.e., decreasing the outbreak size and enlarging the threshold. DCT limits the spread of the epidemic to larger cardinality of hyperedges. On real-world networks, the inhibitory effect of DCT on the spread of epidemics is evident when the spread of epidemics is small.
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Affiliation(s)
- Yanyi Nie
- School of Public Health, Chongqing Medical University, Chongqing 400016, China
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Ming Zhong
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Runchao Li
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Dandan Zhao
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Hao Peng
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Xiaoni Zhong
- School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Tao Lin
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Wei Wang
- School of Public Health, Chongqing Medical University, Chongqing 400016, China
- Research Center of Public Health Security, Chongqing Medical University, Chongqing 400016, China
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24
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Valdez LD, Vassallo L, Braunstein LA. Epidemic control in networks with cliques. Phys Rev E 2023; 107:054304. [PMID: 37329038 DOI: 10.1103/physreve.107.054304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/13/2023] [Indexed: 06/18/2023]
Abstract
Social units, such as households and schools, can play an important role in controlling epidemic outbreaks. In this work, we study an epidemic model with a prompt quarantine measure on networks with cliques (a clique is a fully connected subgraph representing a social unit). According to this strategy, newly infected individuals are detected and quarantined (along with their close contacts) with probability f. Numerical simulations reveal that epidemic outbreaks in networks with cliques are abruptly suppressed at a transition point f_{c}. However, small outbreaks show features of a second-order phase transition around f_{c}. Therefore, our model can exhibit properties of both discontinuous and continuous phase transitions. Next, we show analytically that the probability of small outbreaks goes continuously to 1 at f_{c} in the thermodynamic limit. Finally, we find that our model exhibits a backward bifurcation phenomenon.
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Affiliation(s)
- L D Valdez
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), Departamento de Física, FCEyN, Universidad Nacional de Mar del Plata-CONICET, Mar del Plata 7600, Argentina
| | - L Vassallo
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), Departamento de Física, FCEyN, Universidad Nacional de Mar del Plata-CONICET, Mar del Plata 7600, Argentina
| | - L A Braunstein
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), Departamento de Física, FCEyN, Universidad Nacional de Mar del Plata-CONICET, Mar del Plata 7600, Argentina
- Physics Department, Boston University, Boston, Massachusetts 02215, USA
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25
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Patterson K, Chalifoux M, Gad R, Leblanc S, Paulsen P, Boudreau L, Mazerolle T, Pâquet M. Demographic patterns of exposure and transmission for a rural Canadian community outbreak of COVID-19, 2020. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2022; 48:465-472. [PMID: 38169870 PMCID: PMC10760791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Background A coronavirus disease 2019 (COVID-19) community outbreak was declared October 5-December 3, 2020, in the Restigouche region of New Brunswick, Canada. This article describes the epidemiological characteristics of the outbreak and assesses factors associated with its transmission in rural communities, informing public health measures and programming. Methods A provincial line list was developed from case and contact interviews. Descriptive epidemiological methods were used to characterize the outbreak. Incidence rates among contacts, and by gender for the regional population were estimated. Results There were 83 laboratory-confirmed cases of COVID-19 identified during the observation period. The case ages ranged from 10-89 years of age (median age group was 40-59 years of age) and 51.2% of the cases were male. Symptom onset dates ranged from September 27-October 27, 2020, with 83% of cases being symptomatic. A cluster of early cases at a social event led to multiple workplace outbreaks, though the majority of cases were linked to household transmission. Complex and overlapping social networks resulted in multiple exposure events and that obscured transmission pathways. The incidence rate among men was higher than women, men were significantly more likely to have transmission exposure at their workplace than women, and men were the most common index cases within a household. No transmission in school settings among children was documented despite multiple exposures. Conclusion This investigation highlighted the gendered nature and complexity of a COVID-19 outbreak in a rural Canadian community. Targeted action at workplaces and strategic messaging towards men are likely required to increase awareness and adherence to public health measures to reduce transmission in these settings.
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Affiliation(s)
| | - Mathieu Chalifoux
- Canadian Field Epidemiology Program, Public Health Agency of Canada, Ottawa, ON
- New Brunswick Department of Health, Fredericton, NB
- Vitalité Health Network, Bathurst, NB
- Vitalité Health Network, Richibucto, NB
| | - Rita Gad
- Canadian Field Epidemiology Program, Public Health Agency of Canada, Ottawa, ON
- New Brunswick Department of Health, Fredericton, NB
- Vitalité Health Network, Bathurst, NB
- Vitalité Health Network, Richibucto, NB
| | - Shannon Leblanc
- Canadian Field Epidemiology Program, Public Health Agency of Canada, Ottawa, ON
- New Brunswick Department of Health, Fredericton, NB
- Vitalité Health Network, Bathurst, NB
- Vitalité Health Network, Richibucto, NB
| | - Paige Paulsen
- Canadian Field Epidemiology Program, Public Health Agency of Canada, Ottawa, ON
- New Brunswick Department of Health, Fredericton, NB
- Vitalité Health Network, Bathurst, NB
- Vitalité Health Network, Richibucto, NB
| | - Louise Boudreau
- Canadian Field Epidemiology Program, Public Health Agency of Canada, Ottawa, ON
- New Brunswick Department of Health, Fredericton, NB
- Vitalité Health Network, Bathurst, NB
- Vitalité Health Network, Richibucto, NB
| | - Theresa Mazerolle
- Canadian Field Epidemiology Program, Public Health Agency of Canada, Ottawa, ON
- New Brunswick Department of Health, Fredericton, NB
- Vitalité Health Network, Bathurst, NB
- Vitalité Health Network, Richibucto, NB
| | - Mariane Pâquet
- Canadian Field Epidemiology Program, Public Health Agency of Canada, Ottawa, ON
- New Brunswick Department of Health, Fredericton, NB
- Vitalité Health Network, Bathurst, NB
- Vitalité Health Network, Richibucto, NB
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26
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Wang X, Du Z, James E, Fox SJ, Lachmann M, Meyers LA, Bhavnani D. The effectiveness of COVID-19 testing and contact tracing in a US city. Proc Natl Acad Sci U S A 2022; 119:e2200652119. [PMID: 35969766 PMCID: PMC9407477 DOI: 10.1073/pnas.2200652119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 07/07/2022] [Indexed: 11/18/2022] Open
Abstract
Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.
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Affiliation(s)
- Xutong Wang
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712
| | - Zhanwei Du
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Emily James
- Information Technology Project Management Office, Dell Medical School, The University of Texas at Austin, Austin, TX 78712
| | - Spencer J. Fox
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712
| | | | - Lauren Ancel Meyers
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712
- Santa Fe Institute, Santa Fe, NM 87501
| | - Darlene Bhavnani
- Department of Population Health, Dell Medical School, The University of Texas at Austin, Austin, TX 78712
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27
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Raymenants J, Geenen C, Thibaut J, Nelissen K, Gorissen S, Andre E. Empirical evidence on the efficiency of backward contact tracing in COVID-19. Nat Commun 2022; 13:4750. [PMID: 35963872 PMCID: PMC9375086 DOI: 10.1038/s41467-022-32531-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 08/03/2022] [Indexed: 11/09/2022] Open
Abstract
Standard contact tracing practice for COVID-19 is to identify persons exposed to an infected person during the contagious period, assumed to start two days before symptom onset or diagnosis. In the first large cohort study on backward contact tracing for COVID-19, we extended the contact tracing window by 5 days, aiming to identify the source of the infection and persons infected by the same source. The risk of infection amongst these additional contacts was similar to contacts exposed during the standard tracing window and significantly higher than symptomatic individuals in a control group, leading to 42% more cases identified as direct contacts of an index case. Compared to standard practice, backward traced contacts required fewer tests and shorter quarantine. However, they were identified later in their infectious cycle if infected. Our results support implementing backward contact tracing when rigorous suppression of viral transmission is warranted.
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Affiliation(s)
- Joren Raymenants
- KU Leuven, Laboratory of Clinical Microbiology, Herestraat 49, box 6711, 3000, Leuven, Belgium.
- Algemene Interne Geneeskunde, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - Caspar Geenen
- KU Leuven, Laboratory of Clinical Microbiology, Herestraat 49, box 6711, 3000, Leuven, Belgium
| | - Jonathan Thibaut
- KU Leuven, Laboratory of Clinical Microbiology, Herestraat 49, box 6711, 3000, Leuven, Belgium
| | - Klaas Nelissen
- KU Leuven, Laboratory of Clinical Microbiology, Herestraat 49, box 6711, 3000, Leuven, Belgium
| | - Sarah Gorissen
- KU Leuven, Laboratory of Clinical Microbiology, Herestraat 49, box 6711, 3000, Leuven, Belgium
| | - Emmanuel Andre
- KU Leuven, Laboratory of Clinical Microbiology, Herestraat 49, box 6711, 3000, Leuven, Belgium
- Laboratoriumgeneeskunde, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium
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28
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Fromberg D, Ank N, Nielsen HL. COVID-19 contact tracing in the hospitals located in the North Denmark region: A retrospective review. J Infect Prev 2022; 23:228-234. [PMID: 36003129 PMCID: PMC9207588 DOI: 10.1177/17571774221107754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 05/30/2022] [Indexed: 11/16/2022] Open
Abstract
Background The Department of Infection Control, at our University Hospital conducted contact
tracing of COVID-19 positive patients and staff members at all hospitals in the North
Denmark Region. Aim To describe the contact tracing performed during the COVID-19 pandemic in the Region
and its outcomes. Methods Data from each contact tracing were collected prospectively during 14 May 2020–26 May
2021. Data included information about the index case (patient or hospital staff member),
presentation (asymptomatic vs symptomatic), probable source of transmission
(community-acquired or hospital-acquired), number of close contacts and if any of these
were SARS-CoV-2 PCR-test positive. Findings 362 contact tracing were performed. A total of 573 COVID-19 positive cases were
identified among 171 (30%) patients and 402 (70%) staff members. 192 (34%) of all cases
were tested due to symptoms of COVID-19, whereas two-third were tested for other reasons
including outbreak and systematic screening tests. A total of 1575 close contacts were
identified, including 225 (14%) patients and 1350 (86%) staff members. 100 (6%) close
contacts, including 24 patients and 76 staff members, were infected with SARS-CoV-2, of
which 33 (43%) staff members was positive at day 0 i.e. the same day as being identified
as close contacts. Discussion We found a three to one of close contacts to each index case, but only 6% became
SARS-CoV-2 positive, with a surprisingly high number of those identified at day 0. Our
data confirm that regular testing of patients and staff will identify asymptomatic
carriers and thereby prevent new cases.
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Affiliation(s)
- Dorte Fromberg
- Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark
| | - Nina Ank
- Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark
| | - Hans L Nielsen
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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29
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Hu Y, Subagdja B, Tan AH, Quek C, Yin Q. Who are the 'silent spreaders'?: contact tracing in spatio-temporal memory models. Neural Comput Appl 2022; 34:14859-14879. [PMID: 35599972 PMCID: PMC9107326 DOI: 10.1007/s00521-022-07210-8] [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: 10/26/2021] [Accepted: 03/29/2022] [Indexed: 11/25/2022]
Abstract
The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.
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Affiliation(s)
- Yue Hu
- College of Systems Engineering, National University of Defense Technology, Changsha, Hunan 410073 China
| | - Budhitama Subagdja
- School of Computing and Information Systems, Singapore Management University, 178902 Singapore, Singapore
| | - Ah-Hwee Tan
- School of Computing and Information Systems, Singapore Management University, 178902 Singapore, Singapore
| | - Chai Quek
- School of Computer Science and Engineering, Nanyang Technological University, 639798 Singapore, Singapore
| | - Quanjun Yin
- College of Systems Engineering, National University of Defense Technology, Changsha, Hunan 410073 China
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30
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Mancastroppa M, Guizzo A, Castellano C, Vezzani A, Burioni R. Sideward contact tracing and the control of epidemics in large gatherings. J R Soc Interface 2022; 19:20220048. [PMID: 35537473 PMCID: PMC9090492 DOI: 10.1098/rsif.2022.0048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Effective contact tracing is crucial to containing epidemic spreading without disrupting societal activities, especially during a pandemic. Large gatherings play a key role, potentially favouring superspreading events. However, the effects of tracing in large groups have not been fully assessed so far. We show that in addition to forward tracing, which reconstructs to whom the disease spreads, and backward tracing, which searches from whom the disease spreads, a third 'sideward' tracing is always present, when tracing gatherings. This is an indirect tracing that detects infected asymptomatic individuals, even if they have been neither directly infected by nor directly transmitted the infection to the index case. We analyse this effect in a model of epidemic spreading for SARS-CoV-2, within the framework of simplicial activity-driven temporal networks. We determine the contribution of the three tracing mechanisms to the suppression of epidemic spreading, showing that sideward tracing induces a non-monotonic behaviour in the tracing efficiency, as a function of the size of the gatherings. Based on our results, we suggest an optimal choice for the sizes of the gatherings to be traced and we test the strategy on an empirical dataset of gatherings on a university campus.
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Affiliation(s)
- Marco Mancastroppa
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy.,INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy
| | - Andrea Guizzo
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy.,INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy
| | - Claudio Castellano
- Istituto dei Sistemi Complessi (ISC-CNR), Via dei Taurini 19, I-00185 Roma, Italy
| | - Alessandro Vezzani
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy.,INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy.,Istituto dei Materiali per l'Elettronica ed il Magnetismo (IMEM-CNR), Parco Area delle Scienze, 37/A 43124 Parma, Italy
| | - Raffaella Burioni
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy.,INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy
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31
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Gunaratne C, Reyes R, Hemberg E, O'Reilly UM. Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework. Sci Rep 2022; 12:6202. [PMID: 35418652 PMCID: PMC9007058 DOI: 10.1038/s41598-022-09942-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 03/24/2022] [Indexed: 12/24/2022] Open
Abstract
Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.
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Affiliation(s)
- Chathika Gunaratne
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
- Oak Ridge National Laboratory, Oak Ridge, TN, USA.
| | - Rene Reyes
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
| | - Erik Hemberg
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
| | - Una-May O'Reilly
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
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32
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Chen T, Zhang Y, Qian X, Li J. A knowledge graph-based method for epidemic contact tracing in public transportation. TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES 2022; 137:103587. [PMID: 35153392 PMCID: PMC8818383 DOI: 10.1016/j.trc.2022.103587] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 01/14/2022] [Accepted: 01/25/2022] [Indexed: 06/01/2023]
Abstract
Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth-first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
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Affiliation(s)
- Tian Chen
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China
| | - Yimu Zhang
- Urban Mobility Institute, Tongji University, 4800 Cao'an Road, Shanghai 201804, China
| | - Xinwu Qian
- The University of Alabama, Tuscaloosa, AL 35487, United States
| | - Jian Li
- Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China
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33
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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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Kretzschmar ME, Ashby B, Fearon E, Overton CE, Panovska-Griffiths J, Pellis L, Quaife M, Rozhnova G, Scarabel F, Stage HB, Swallow B, Thompson RN, Tildesley MJ, Villela D. Challenges for modelling interventions for future pandemics. Epidemics 2022; 38:100546. [PMID: 35183834 PMCID: PMC8830929 DOI: 10.1016/j.epidem.2022.100546] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 12/16/2022] Open
Abstract
Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.
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Affiliation(s)
- Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Ben Ashby
- Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK
| | - Elizabeth Fearon
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, UK
| | - Christopher E Overton
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; Clinical Data Science Unit, Manchester University NHS Foundation Trust, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; The Alan Turing Institute, London, UK
| | - Matthew Quaife
- TB Modelling Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Francesca Scarabel
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy
| | - Helena B Stage
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; University of Potsdam, Germany; Humboldt University of Berlin, Germany
| | - Ben Swallow
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK; Scottish Covid-19 Response Consortium, UK
| | - Robin N Thompson
- Joint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
| | - Michael J Tildesley
- Joint UNIversities Pandemic and Epidemiological Research, UK; Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
| | - Daniel Villela
- Program of Scientific Computing, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Rosenkrantz DJ, Vullikanti A, Ravi SS, Stearns RE, Levin S, Poor HV, Marathe MV. Fundamental limitations on efficiently forecasting certain epidemic measures in network models. Proc Natl Acad Sci U S A 2022; 119:e2109228119. [PMID: 35046025 PMCID: PMC8794801 DOI: 10.1073/pnas.2109228119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/05/2021] [Indexed: 11/18/2022] Open
Abstract
The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given number of people will get infected at a given time and the probability that the number of infections will reach a peak at a given time) are computationally intractable. For instance, efficient solvability of such problems would imply that the number of satisfying assignments of an arbitrary Boolean formula in conjunctive normal form can be computed efficiently, violating a widely believed hypothesis in computational complexity. This intractability result holds even under the ideal situation, where all the disease parameters are known and are assumed to be insensitive to changes in the environment. From a computational complexity viewpoint, our results, which show that contagion dynamics become unpredictable for both macroscopic and individual properties, bring out some fundamental difficulties of predicting disease parameters. On the positive side, we develop efficient algorithms or approximation algorithms for restricted versions of forecasting problems.
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Affiliation(s)
- Daniel J Rosenkrantz
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University at Albany-State University of New York, Albany, NY 12222
| | - Anil Vullikanti
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University of Virginia, Charlottesville, VA 22904
| | - S S Ravi
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University at Albany-State University of New York, Albany, NY 12222
| | - Richard E Stearns
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University at Albany-State University of New York, Albany, NY 12222
| | - Simon Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544
| | - H Vincent Poor
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544
| | - Madhav V Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904;
- Department of Computer Science, University of Virginia, Charlottesville, VA 22904
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36
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Baumgarten L, Bornholdt S. Epidemics with asymptomatic transmission: Subcritical phase from recursive contact tracing. Phys Rev E 2021; 104:054310. [PMID: 34942758 DOI: 10.1103/physreve.104.054310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/16/2021] [Indexed: 11/07/2022]
Abstract
The challenges presented by the COVID-19 epidemic have created a renewed interest in the development of new methods to combat infectious diseases, and it has shown the importance of preparedness for possible future diseases. A prominent property of the SARS-CoV-2 transmission is the significant fraction of asymptomatic transmission. This may influence the effectiveness of the standard contact tracing procedure for quarantining potentially infected individuals. However, the effects of asymptomatic transmission on the epidemic threshold of epidemic spreading on networks have rarely been studied explicitly. Here we study the critical percolation transition for an arbitrary disease with a nonzero asymptomatic rate in a simple epidemic network model in the presence of a recursive contact tracing algorithm for instant quarantining. We find that, above a certain fraction of asymptomatic transmission, standard contact tracing loses its ability to suppress spreading below the epidemic threshold. However, we also find that recursive contact tracing opens a possibility to contain epidemics with a large fraction of asymptomatic or presymptomatic transmission. In particular, we calculate the required fraction of network nodes participating in the contact tracing for networks with arbitrary degree distributions and for varying recursion depths and discuss the influence of recursion depth and asymptomatic rate on the epidemic percolation phase transition. We anticipate recursive contact tracing to provide a basis for digital, app-based contact tracing tools that extend the efficiency of contact tracing to diseases with a large fraction of asymptomatic transmission.
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Affiliation(s)
- Lorenz Baumgarten
- Institut für Theoretische Physik, Universität Bremen, 28759 Bremen, Germany
| | - Stefan Bornholdt
- Institut für Theoretische Physik, Universität Bremen, 28759 Bremen, Germany
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37
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Ganegoda NC, Wijaya KP, Páez Chávez J, Aldila D, Erandi KKWH, Amadi M. Reassessment of contact restrictions and testing campaigns against COVID-19 via spatio-temporal modeling. NONLINEAR DYNAMICS 2021; 107:3085-3109. [PMID: 34955605 PMCID: PMC8686823 DOI: 10.1007/s11071-021-07111-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 11/28/2021] [Indexed: 06/14/2023]
Abstract
Since the earliest outbreak of COVID-19, the disease continues to obstruct life normalcy in many parts of the world. The present work proposes a mathematical framework to improve non-pharmaceutical interventions during the new normal before vaccination settles herd immunity. The considered approach is built from the viewpoint of decision makers in developing countries where resources to tackle the disease from both a medical and an economic perspective are scarce. Spatial auto-correlation analysis via global Moran's index and Moran's scatter is presented to help modulate decisions on hierarchical-based priority for healthcare capacity and interventions (including possible vaccination), finding a route for the corresponding deployment as well as landmarks for appropriate border controls. These clustering tools are applied to sample data from Sri Lanka to classify the 26 Regional Director of Health Services (RDHS) divisions into four clusters by introducing convenient classification criteria. A metapopulation model is then used to evaluate the intra- and inter-cluster contact restrictions as well as testing campaigns under the absence of confounding factors. Furthermore, we investigate the role of the basic reproduction number to determine the long-term trend of the regressing solution around disease-free and endemic equilibria. This includes an analytical bifurcation study around the basic reproduction number using Brouwer Degree Theory and asymptotic expansions as well as related numerical investigations based on path-following techniques. We also introduce the notion of average policy effect to assess the effectivity of contact restrictions and testing campaigns based on the proposed model's transient behavior within a fixed time window of interest.
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Affiliation(s)
| | | | - Joseph Páez Chávez
- Center for Applied Dynamical Systems and Computational Methods (CADSCOM), Faculty of Natural Sciences and Mathematics, Escuela Superior Politécnica del Litoral, P.O. Box 09-01-5863, Guayaquil, Ecuador
- Center for Dynamics, Department of Mathematics, TU Dresden, D–01062 Dresden, Germany
| | - Dipo Aldila
- Department of Mathematics, University of Indonesia, Depok, 16424 Indonesia
| | | | - Miracle Amadi
- Department of Mathematics and Physics, Lappeenranta University of Technology, FI–53851 Lappeenranta, Finland
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38
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Rusu AC, Emonet R, Farrahi K. Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers? PLoS One 2021; 16:e0259969. [PMID: 34793526 PMCID: PMC8601513 DOI: 10.1371/journal.pone.0259969] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 10/30/2021] [Indexed: 12/23/2022] Open
Abstract
Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.
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Affiliation(s)
- Andrei C. Rusu
- Vision, Learning and Control Research Group, University of Southampton, Southampton, United Kingdom
| | - Rémi Emonet
- Department of Machine Learning, Laboratoire Hubert Curien, Saint-Etienne, France
| | - Katayoun Farrahi
- Vision, Learning and Control Research Group, University of Southampton, Southampton, United Kingdom
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Population-based analysis of the epidemiological features of COVID-19 epidemics in Victoria, Australia, January 2020 - March 2021, and their suppression through comprehensive control strategies. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2021; 17:100297. [PMID: 34723232 PMCID: PMC8547897 DOI: 10.1016/j.lanwpc.2021.100297] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/05/2021] [Accepted: 09/19/2021] [Indexed: 12/23/2022]
Abstract
Background Victoria experienced the greatest burden of COVID-19 in Australia in 2020. This report describes key epidemiological characteristics and corresponding control measures between 17 January 2020 and 26 March 2021. Methods COVID-19 notifications made to the State Government Department of Health were used in this analysis. Epidemiological features are described over 4 phases, including enhancements to testing, contact tracing and public health interventions. Demographic and clinical features of cases are described. Findings Victoria recorded 20,483 cases of COVID-19, of which 1073 (5•2%) were acquired overseas and 19,360 (95%) were locally acquired. The initial epidemic (Phase I) was well-contained through public health interventions and was followed by relaxation of restrictions and low-level community transmission (Phase II). However, an outbreak in a hotel used to quarantine returned travellers led to wide-scale community transmission accounting for a majority (91%) of cases (Phase III). Outbreaks occurred in vulnerable settings including aged care and hospitals, contributing to high hospitalisation (12%) and case fatality rates (3•7%). Aggressive restrictions ultimately led to local elimination, and subsequent outbreaks have been swiftly managed with improved processes (Phase IV). The demographic composition of cases evolved across phases from an older, wealthier population to a less advantaged younger population, with many from culturally and linguistically diverse backgrounds. Interpretation Over time, adaptations to the public health response have strengthened capacity to respond to new cases and outbreaks in a more effective manner. The Victorian experience underscores the importance of authentic engagement with diverse communities and balancing restrictions with livelihoods.
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Nielsen BF, Sneppen K, Simonsen L, Mathiesen J. Differences in social activity increase efficiency of contact tracing. THE EUROPEAN PHYSICAL JOURNAL. B 2021; 94:209. [PMID: 34690541 PMCID: PMC8523203 DOI: 10.1140/epjb/s10051-021-00222-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/02/2021] [Indexed: 05/07/2023]
Abstract
ABSTRACT Digital contact tracing has been suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we use smartphone proximity data to explore how social structure affects contact tracing of COVID-19. We model the spread of COVID-19 and find that the effectiveness of contact tracing depends strongly on social network structure and heterogeneous social activity. Contact tracing is shown to be remarkably effective in a workplace environment and the effectiveness depends strongly on the minimum duration of contact required to initiate quarantine. In a realistic social network, we find that forward contact tracing with immediate isolation can reduce an epidemic by more than 70%. In perspective, our findings highlight the necessity of incorporating social heterogeneity into models of mitigation strategies. GRAPHIC ABSTRACT SUPPLEMENTARY INFORMATION The online version supplementary material available at 10.1140/epjb/s10051-021-00222-8.
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Affiliation(s)
- Bjarke Frost Nielsen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, 4000 Roskilde, Denmark
| | - Joachim Mathiesen
- Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
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41
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Contreras S, Dehning J, Mohr SB, Bauer S, Spitzner FP, Priesemann V. Low case numbers enable long-term stable pandemic control without lockdowns. SCIENCE ADVANCES 2021; 7:eabg2243. [PMID: 34623913 PMCID: PMC8500516 DOI: 10.1126/sciadv.abg2243] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The traditional long-term solutions for epidemic control involve eradication or population immunity. Here, we analytically derive the existence of a third viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events and only moderate restrictions remain necessary. In this equilibrium, daily cases stabilize around ten or fewer new infections per million people. However, stability is endangered if restrictions are relaxed or case numbers grow too high. The latter destabilization marks a tipping point beyond which the spread self-accelerates. We show that a lockdown can reestablish control and that recurring lockdowns are not necessary given sustained, moderate contact reduction. We illustrate how this strategy profits from vaccination and helps mitigate variants of concern. This strategy reduces cumulative cases (and fatalities) four times more than strategies that only avoid hospital collapse. In the long term, immunization, large-scale testing, and international coordination will further facilitate control.
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Affiliation(s)
- Sebastian Contreras
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Beauchef 851, 8370456 Santiago, Chile
| | - Jonas Dehning
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - Sebastian B. Mohr
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - Simon Bauer
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - F. Paul Spitzner
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
- Department of Physics, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany
- Corresponding author.
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42
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Donges JF, Lochner JH, Kitzmann NH, Heitzig J, Lehmann S, Wiedermann M, Vollmer J. Dose-response functions and surrogate models for exploring social contagion in the Copenhagen Networks Study. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2021; 230:3311-3334. [PMID: 34611486 PMCID: PMC8484857 DOI: 10.1140/epjs/s11734-021-00279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Spreading dynamics and complex contagion processes on networks are important mechanisms underlying the emergence of critical transitions, tipping points and other non-linear phenomena in complex human and natural systems. Increasing amounts of temporal network data are now becoming available to study such spreading processes of behaviours, opinions, ideas, diseases and innovations to test hypotheses regarding their specific properties. To this end, we here present a methodology based on dose-response functions and hypothesis testing using surrogate data models that randomise most aspects of the empirical data while conserving certain structures relevant to contagion, group or homophily dynamics. We demonstrate this methodology for synthetic temporal network data of spreading processes generated by the adaptive voter model. Furthermore, we apply it to empirical temporal network data from the Copenhagen Networks Study. This data set provides a physically-close-contact network between several hundreds of university students participating in the study over the course of 3 months. We study the potential spreading dynamics of the health-related behaviour "regularly going to the fitness studio" on this network. Based on a hierarchy of surrogate data models, we find that our method neither provides significant evidence for an influence of a dose-response-type network spreading process in this data set, nor significant evidence for homophily. The empirical dynamics in exercise behaviour are likely better described by individual features such as the disposition towards the behaviour, and the persistence to maintain it, as well as external influences affecting the whole group, and the non-trivial network structure. The proposed methodology is generic and promising also for applications to other temporal network data sets and traits of interest.
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Affiliation(s)
- Jonathan F. Donges
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Jakob H. Lochner
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Institute for Theoretical Physics, University of Leipzig, Leipzig, Germany
| | - Niklas H. Kitzmann
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Institute for Physics and Astronomy, University of Potsdam, Potsdam, Germany
| | - Jobst Heitzig
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
| | - Sune Lehmann
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Marc Wiedermann
- Earth System Analysis and Complexity Science, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- Robert Koch-Institut, Berlin, Germany
- Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany
| | - Jürgen Vollmer
- Institute for Theoretical Physics, University of Leipzig, Leipzig, Germany
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43
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Madoery PG, Detke R, Blanco L, Comerci S, Fraire J, Gonzalez Montoro A, Bellassai JC, Britos G, Ojeda S, Finochietto JM. Feature selection for proximity estimation in COVID-19 contact tracing apps based on Bluetooth Low Energy (BLE). PERVASIVE AND MOBILE COMPUTING 2021; 77:101474. [PMID: 34602920 PMCID: PMC8475095 DOI: 10.1016/j.pmcj.2021.101474] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 08/28/2021] [Accepted: 09/08/2021] [Indexed: 06/13/2023]
Abstract
During the COVID-19 pandemic, contact tracing apps based on the Bluetooth Low Energy (BLE) technology found in smartphones have been deployed by multiple countries despite BLE's debatable performance for determining close contacts among users. Current solutions estimate proximity based on a single feature: the mean attenuation of the BLE signal. In this context, a new generation of these apps which better exploits data from the BLE signal and other sensors available on phones can be fostered. Collected data can be used to extract multiple features that feed machine learning models which can potentially improve the accuracy of today's solutions. In this work, we consider the use of machine learning models to evaluate different feature sets that can be extracted from the received BLE signal, and assess the performance gain as more features are introduced in these models. Since indoor conditions have a strong impact in assessing the risk of being exposed to the SARS-CoV-2, we analyze the environment (indoor or outdoor) role in these models, aiming at understanding the need for apps that could increase proximity accuracy if aware of its environment. Results show that a better accuracy can be obtained in outdoor locations with respect to indoor ones, and that indoor proximity estimation can benefit more from the introduction of more features with respect to the outdoor estimation case. Accuracy can be increased about 10% when multiple features are considered if the device is aware of its environment, reaching a performance of up to 83% in indoor spaces and up to 91% in outdoor ones. These results encourage future contact tracing apps to integrate this awareness not only to better assess the associated risk of a given environment but also to improve the proximity accuracy for detecting close contacts.
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Affiliation(s)
- Pablo G Madoery
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
- Instituto de Estudios Avanzados en Ingeniería y Tecnología (IDIT) - CONICET, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Ramiro Detke
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Lucas Blanco
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Sandro Comerci
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Juan Fraire
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
| | - Aldana Gonzalez Montoro
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Juan Carlos Bellassai
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Grisel Britos
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Silvia Ojeda
- Facultad de Matemática, Astronomía, Física y Computación - Universidad Nacional de Córdoba, Av. Medina Allende 2144, Córdoba, Argentina
| | - Jorge M Finochietto
- Facultad de Ciencias Exactas, Físicas y Naturales - Universidad Nacional de Córdoba, Av. Velez Sarsfield 1611, Córdoba, Argentina
- Instituto de Estudios Avanzados en Ingeniería y Tecnología (IDIT) - CONICET, Av. Velez Sarsfield 1611, Córdoba, Argentina
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44
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Truszkowska A, Thakore M, Zino L, Butail S, Caroppo E, Jiang Z, Rizzo A, Porfiri M. Designing the Safe Reopening of US Towns Through High-Resolution Agent-Based Modeling. ADVANCED THEORY AND SIMULATIONS 2021; 4:2100157. [PMID: 34514293 PMCID: PMC8420460 DOI: 10.1002/adts.202100157] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/19/2021] [Indexed: 12/13/2022]
Abstract
As COVID-19 vaccine is being rolled out in the US, public health authorities are gradually reopening the economy. To date, there is no consensus on a common approach among local authorities. Here, a high-resolution agent-based model is proposed to examine the interplay between the increased immunity afforded by the vaccine roll-out and the transmission risks associated with reopening efforts. The model faithfully reproduces the demographics, spatial layout, and mobility patterns of the town of New Rochelle, NY - representative of the urban fabric of the US. Model predictions warrant caution in the reopening under the current rate at which people are being vaccinated, whereby increasing access to social gatherings in leisure locations and households at a 1% daily rate can lead to a 28% increase in the fatality rate within the next three months. The vaccine roll-out plays a crucial role on the safety of reopening: doubling the current vaccination rate is predicted to be sufficient for safe, rapid reopening.
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Affiliation(s)
- Agnieszka Truszkowska
- Center for Urban Science and ProgressTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
- Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Malav Thakore
- Department of Mechanical EngineeringNorthern Illinois UniversityDeKalbIL60115USA
| | - Lorenzo Zino
- Faculty of Science and EngineeringUniversity of GroningenNijenborgh 4Groningen9747 AGThe Netherlands
| | - Sachit Butail
- Department of Mechanical EngineeringNorthern Illinois UniversityDeKalbIL60115USA
| | - Emanuele Caroppo
- Department of Mental HealthLocal Health Unit ROMA 2Rome00159Italy
- University Research Center He.R.A.Università Cattolica del Sacro CuoreRome00168Italy
| | - Zhong‐Ping Jiang
- Department of Electrical and Computer EngineeringTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
| | - Alessandro Rizzo
- Department of Electronics and TelecommunicationsPolitecnico di TorinoTurin10129Italy
- Office of InnovationTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Maurizio Porfiri
- Center for Urban Science and ProgressTandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
- Department of Mechanical and Aerospace EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
- Department of Biomedical EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
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45
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Hâncean MG, Lerner J, Perc M, Ghiţă MC, Bunaciu DA, Stoica AA, Mihăilă BE. The role of age in the spreading of COVID-19 across a social network in Bucharest. JOURNAL OF COMPLEX NETWORKS 2021; 9:cnab026. [PMID: 34642603 PMCID: PMC8499891 DOI: 10.1093/comnet/cnab026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/19/2021] [Indexed: 05/28/2023]
Abstract
We analyse officially procured data detailing the COVID-19 transmission in Romania's capital Bucharest between 1st August and 31st October 2020. We apply relational hyperevent models on 19,713 individuals with 13,377 infection ties to determine to what degree the disease spread is affected by age whilst controlling for other covariate and human-to-human transmission network effects. We find that positive cases are more likely to nominate alters of similar age as their sources of infection, thus providing evidence for age homophily. We also show that the relative infection risk is negatively associated with the age of peers, such that the risk of infection increases as the average age of contacts decreases. Additionally, we find that adults between the ages 35 and 44 are pivotal in the transmission of the disease to other age groups. Our results may contribute to better controlling future COVID-19 waves, and they also point to the key age groups which may be essential for vaccination given their prominent role in the transmission of the virus.
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Affiliation(s)
| | - Jürgen Lerner
- Department of Computer and Information Science, University of Konstanz, 78457, Konstanz, Germany
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404332, Taiwan, Alma Mater Europaea, Slovenska ulica 17, 2000 Maribor, Slovenia and Complexity Science Hub Vienna, Josefstädterstraße 39, 1080 Vienna, Austria
| | - Maria Cristina Ghiţă
- Department of Sociology, University of Bucharest, Bucharest, Panduri 90-92, 050663, Romania
| | - David-Andrei Bunaciu
- Department of Sociology, University of Bucharest, Bucharest, Panduri 90-92, 050663, Romania
| | | | - Bianca-Elena Mihăilă
- Department of Sociology, University of Bucharest, Bucharest, Panduri 90-92, 050663, Romania
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46
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Otto SP, Day T, Arino J, Colijn C, Dushoff J, Li M, Mechai S, Van Domselaar G, Wu J, Earn DJD, Ogden NH. The origins and potential future of SARS-CoV-2 variants of concern in the evolving COVID-19 pandemic. Curr Biol 2021; 31:R918-R929. [PMID: 34314723 PMCID: PMC8220957 DOI: 10.1016/j.cub.2021.06.049] [Citation(s) in RCA: 196] [Impact Index Per Article: 65.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
One year into the global COVID-19 pandemic, the focus of attention has shifted to the emergence and spread of SARS-CoV-2 variants of concern (VOCs). After nearly a year of the pandemic with little evolutionary change affecting human health, several variants have now been shown to have substantial detrimental effects on transmission and severity of the virus. Public health officials, medical practitioners, scientists, and the broader community have since been scrambling to understand what these variants mean for diagnosis, treatment, and the control of the pandemic through nonpharmaceutical interventions and vaccines. Here we explore the evolutionary processes that are involved in the emergence of new variants, what we can expect in terms of the future emergence of VOCs, and what we can do to minimise their impact.
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Affiliation(s)
- Sarah P Otto
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| | - Troy Day
- Department of Mathematics and Statistics, Department of Biology, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Julien Arino
- Department of Mathematics and Data Science Nexus, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Jonathan Dushoff
- Department of Biology and M.G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Michael Li
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON N1G 3W4, Canada
| | - Samir Mechai
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC J2S 2M2, Canada
| | - Gary Van Domselaar
- National Microbiology Laboratory - Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada; Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada
| | - David J D Earn
- Department of Mathematics and Statistics and M.G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC J2S 2M2, Canada
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47
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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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48
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Fyles M, Fearon E, Overton C, Wingfield T, Medley GF, Hall I, Pellis L, House T. Using a household-structured branching process to analyse contact tracing in the SARS-CoV-2 pandemic. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200267. [PMID: 34053253 PMCID: PMC8165594 DOI: 10.1098/rstb.2020.0267] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2021] [Indexed: 01/19/2023] Open
Abstract
We explore strategies of contact tracing, case isolation and quarantine of exposed contacts to control the SARS-CoV-2 epidemic using a branching process model with household structure. This structure reflects higher transmission risks among household members than among non-household members. We explore strategic implementation choices that make use of household structure, and investigate strategies including two-step tracing, backwards tracing, smartphone tracing and tracing upon symptom report rather than test results. The primary model outcome is the effect of contact tracing, in combination with different levels of physical distancing, on the growth rate of the epidemic. Furthermore, we investigate epidemic extinction times to indicate the time period over which interventions must be sustained. We consider effects of non-uptake of isolation/quarantine, non-adherence, and declining recall of contacts over time. Our results find that, compared to self-isolation of cases without contact tracing, a contact tracing strategy designed to take advantage of household structure allows for some relaxation of physical distancing measures but cannot completely control the epidemic absent of other measures. Even assuming no imported cases and sustainment of moderate physical distancing, testing and tracing efforts, the time to bring the epidemic to extinction could be in the order of months to years. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Martyn Fyles
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
| | - Elizabeth Fearon
- Centre for the Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | | | - Tom Wingfield
- Department of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool L7 8XP, UK
- WHO Collaborating Centre on Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Graham F. Medley
- Centre for the Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Ian Hall
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Public Health England, UK
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/, Daresbury WA4 4AD, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/, Daresbury WA4 4AD, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester M13 9PY, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/, Daresbury WA4 4AD, UK
- IBM Research, Hartree Centre, Daresbury WA4 4AD, UK
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49
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Soldano GJ, Fraire JA, Finochietto JM, Quiroga R. COVID-19 mitigation by digital contact tracing and contact prevention (app-based social exposure warnings). Sci Rep 2021; 11:14421. [PMID: 34257350 PMCID: PMC8277769 DOI: 10.1038/s41598-021-93538-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/25/2021] [Indexed: 12/24/2022] Open
Abstract
A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 spread. Due to its sustainability, contact tracing is one of the most frequently applied interventions worldwide, albeit with mixed results. We evaluate the performance of digital contact tracing for different infection detection rates and response time delays. We also introduce and analyze a novel strategy we call contact prevention, which emits high exposure warnings to smartphone users according to Bluetooth-based contact counting. We model the effect of both strategies on transmission dynamics in SERIA, an agent-based simulation platform that implements population-dependent statistical distributions. Results show that contact prevention remains effective in scenarios with high diagnostic/response time delays and low infection detection rates, which greatly impair the effect of traditional contact tracing strategies. Contact prevention could play a significant role in pandemic mitigation, especially in developing countries where diagnostic and tracing capabilities are inadequate. Contact prevention could thus sustainably reduce the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. Depending on infection detection and app adoption rates, applying a combination of digital contact tracing and contact prevention could reduce pandemic-related mortality by 20-56%.
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Affiliation(s)
- Germán J Soldano
- Instituto de Investigaciones en Fisico-Química de Córdoba (INFIQC-CONICET), Córdoba, Argentina
- Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Córdoba, Argentina
| | - Juan A Fraire
- Instituto de Estudios Avanzados En Ingenieria y Tecnología (IDIT-CONICET), Córdoba, Argentina
- Universidad Nacional de Córdoba, Facultad de Ciencias Exactas, Físicas y Naturales, Córdoba, Argentina
- Saarland University, Saarland Informatics Campus, Saarbrücken, Germany
| | - Jorge M Finochietto
- Instituto de Estudios Avanzados En Ingenieria y Tecnología (IDIT-CONICET), Córdoba, Argentina
- Universidad Nacional de Córdoba, Facultad de Ciencias Exactas, Físicas y Naturales, Córdoba, Argentina
| | - Rodrigo Quiroga
- Instituto de Investigaciones en Fisico-Química de Córdoba (INFIQC-CONICET), Córdoba, Argentina.
- Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Córdoba, Argentina.
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50
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Kerr CC, Mistry D, Stuart RM, Rosenfeld K, Hart GR, Núñez RC, Cohen JA, Selvaraj P, Abeysuriya RG, Jastrzębski M, George L, Hagedorn B, Panovska-Griffiths J, Fagalde M, Duchin J, Famulare M, Klein DJ. Controlling COVID-19 via test-trace-quarantine. Nat Commun 2021; 12:2993. [PMID: 34017008 PMCID: PMC8137690 DOI: 10.1038/s41467-021-23276-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023] Open
Abstract
Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.
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Affiliation(s)
- Cliff C Kerr
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA.
| | - Dina Mistry
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Robyn M Stuart
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
- Burnet Institute, Melbourne, VIC, Australia
| | - Katherine Rosenfeld
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Gregory R Hart
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Rafael C Núñez
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Jamie A Cohen
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Prashanth Selvaraj
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | | | | | - Lauren George
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Brittany Hagedorn
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Jasmina Panovska-Griffiths
- Department of Applied Health Research, University College London, London, UK
- Wolfson Centre for Mathematical Biology and The Queen's College, Oxford University, Oxford, UK
| | | | | | - Michael Famulare
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Daniel J Klein
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, WA, USA
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