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Unim B, Zile-Velika I, Pavlovska Z, Lapao L, Peyroteo M, Misins J, Forjaz MJ, Nogueira P, Grisetti T, Palmieri L. The role of digital tools and emerging devices in COVID-19 contact tracing during the first 18 months of the pandemic: a systematic review. Eur J Public Health 2024; 34:i11-i28. [PMID: 38946444 PMCID: PMC11215323 DOI: 10.1093/eurpub/ckae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
BACKGROUND Contact tracing is a public health intervention implemented in synergy with other preventive measures to curb epidemics, like the coronavirus pandemic. The development and use of digital devices have increased worldwide to enhance the contact tracing process. The aim of the study was to evaluate the effectiveness and impact of tracking coronavirus disease 2019 (COVID-19) patients using digital solutions. METHODS Observational studies on digital contact tracing (DCT), published 2020-21, in English were identified through a systematic literature review performed on nine online databases. An ad hoc form was used for data extraction of relevant information. Quality assessment of the included studies was performed with validated tools. A qualitative synthesis of the findings is reported. RESULTS Over 8000 records were identified and 37 were included in the study: 24 modelling and 13 population-based studies. DCT improved the identification of close contacts of COVID-19 cases and reduced the effective reproduction number of COVID-19-related infections and deaths by over 60%. It impacted positively on societal and economic costs, in terms of lockdowns and use of resources, including staffing. Privacy and security issues were reported in 27 studies. CONCLUSIONS DCT contributed to curbing the COVID-19 pandemic, especially with the high uptake rate of the devices and in combination with other public health measures, especially conventional contact tracing. The main barriers to the implementation of the devices are uptake rate, security and privacy issues. Public health digitalization and contact tracing are the keys to countries' emergency preparedness for future health crises.
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Affiliation(s)
- Brigid Unim
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy
| | | | - Zane Pavlovska
- Centre for Disease Prevention and Control of Latvia, Riga, Latvia
| | - Luis Lapao
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, Caparica, Portugal
- CHRC, Nova Medical School, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Mariana Peyroteo
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, Caparica, Portugal
- CHRC, Nova Medical School, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Janis Misins
- Centre for Disease Prevention and Control of Latvia, Riga, Latvia
| | - Maria João Forjaz
- National Center of Epidemiology, Health Institute Carlos III and RICAPPS, Madrid, Spain
| | - Paulo Nogueira
- CHRC, National School of Public Health, Nova de Lisboa University, Lisbon, Portugal
- Nursing Research, Innovation and Development Centre of Lisbon (CIDNUR), Nursing School of Lisbon, Lisbon, Portugal
- Instituto de Saúde Ambiental (ISAMB), Laboratório para a Sustentabilidade do Uso da Terra e dos Serviços dos Ecossistemas—TERRA, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Tiziana Grisetti
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy
| | - Luigi Palmieri
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy
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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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Aung AH, Li AL, Kyaw WM, Khanna R, Lim WY, Ang H, Chow ALP. Harnessing a real-time location system for contact tracing in a busy emergency department. J Hosp Infect 2023; 141:63-70. [PMID: 37660888 DOI: 10.1016/j.jhin.2023.08.015] [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: 04/27/2023] [Revised: 07/31/2023] [Accepted: 08/12/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND With the persistent threat of emerging infectious diseases (EIDs), digital contact tracing (CT) tools can augment conventional CT for the prevention of healthcare-associated infectious disease transmission. However, their performance has yet to be evaluated comprehensively in the fast-paced emergency department (ED) setting. OBJECTIVE This study compared the CT performance of a radiofrequency identification (RFID)-based real-time location system (RTLS) with conventional electronic medical record (EMR) review against continuous direct observation of close contacts ('gold standard') in a busy ED during the coronavirus disease 2019 pandemic period. METHODS This cross-sectional study was conducted at the ED of a large tertiary care hospital in Singapore from December 2020 to April 2021. CT performance [sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and kappa] of the RTLS, EMR review and a combination of the two approaches (hybrid CT) was compared with direct observation. Finally, the mean absolute error (MAE) in the duration of each contact episode found via the RTLS and direct observation was calculated. RESULTS In comparison with EMR review, both the RTLS and the hybrid CT approach had higher sensitivity (0.955 vs 0.455 for EMR review) and a higher NPV (0.997 vs 0.968 for EMR review). The RTLS had the highest PPV (0.777 vs 0.714 for EMR review vs 0.712 for hybrid CT). The RTLS had the strongest agreement with direct observation (kappa=0.848). The MAE between contact durations of 80 direct observations and their respective RTLS contact times was 1.81 min. CONCLUSION The RTLS was validated to be a high-performing CT tool, with significantly higher sensitivity than conventional CT via EMR review. The RTLS can be used with confidence in time-strapped EDs for time-sensitive CT for the prevention of healthcare-associated transmission of EIDs.
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Affiliation(s)
- A H Aung
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
| | - A L Li
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - W M Kyaw
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - R Khanna
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - W-Y Lim
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - H Ang
- Department of Emergency Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - A L P Chow
- Department of Preventive and Population Medicine, Tan Tock Seng Hospital, Singapore, Singapore; Lee Kong Chian School of Medicine, Singapore, Singapore; Infectious Disease Research and Training Office, National Centre for Infectious Disease, Singapore, Singapore; Saw Swee Hock School of Public Health, Singapore, Singapore
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Zhang B, Lei H, Cai Y, Zhong Z, Jiao Z. COVID-19 contact tracking based on person reidentification and geospatial data. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101558. [PMID: 37251782 PMCID: PMC10110285 DOI: 10.1016/j.jksuci.2023.101558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 03/08/2023] [Accepted: 04/09/2023] [Indexed: 05/31/2023]
Abstract
Efficient contact tracing is a crucial step in preventing the spread of COVID-19. However, the current methods rely heavily on manual investigation and truthful reporting by high-risk individuals. Mobile applications and Bluetooth-based contact tracing methods have also been adopted, but privacy concerns and reliance on personal data have limited their effectiveness. To address these challenges, in this paper, a geospatial big data method that combines person reidentification and geospatial information for contact tracing is proposed. The proposed real-time person reidentification model can identify individuals across multiple surveillance cameras, and the surveillance data is fused with geographic information and mapped onto a 3D geospatial model to track movement trajectories. After real-world verification, the proposed method achieves a first accuracy rate of 91.56%, a first-five accuracy rate of 97.70%, and a mean average precision of 78.03% with an inference speed of 13 ms per image. Importantly, the proposed method does not rely on personal information, mobile phones, or wearable devices, avoiding the limitations of existing contact tracing schemes and providing significant implications for public health in the post-COVID-19 era.
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Affiliation(s)
- Boxing Zhang
- Guangdong Key Laboratory of Modern Control Technology, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China
- Kunming University of Sciences and Technology, Faculty of Information Engineering and Automation, Kunming, China
| | - Huan Lei
- Guangdong Key Laboratory of Modern Control Technology, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China
| | - Yingjie Cai
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Zhenyu Zhong
- Guangdong Key Laboratory of Modern Control Technology, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China
| | - Zeyu Jiao
- Guangdong Key Laboratory of Modern Control Technology, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China
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Pozo-Martin F, Beltran Sanchez MA, Müller SA, Diaconu V, Weil K, El Bcheraoui C. Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review. Eur J Epidemiol 2023; 38:243-266. [PMID: 36795349 PMCID: PMC9932408 DOI: 10.1007/s10654-023-00963-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 12/31/2022] [Indexed: 02/17/2023]
Abstract
Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.
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Affiliation(s)
- Francisco Pozo-Martin
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany.
| | | | - Sophie Alice Müller
- Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Viorela Diaconu
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Kilian Weil
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Charbel El Bcheraoui
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
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Handmann E, Camanor SW, Fallah MP, Candy N, Parker D, Gries A, Grünewald T. Feasibility of digital contact tracing in low-income settings - pilot trial for a location-based DCT app. BMC Public Health 2023; 23:146. [PMID: 36670358 PMCID: PMC9859743 DOI: 10.1186/s12889-022-14888-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/16/2022] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Data about the effectiveness of digital contact tracing are based on studies conducted in countries with predominantly high- or middle-income settings. Up to now, little research is done to identify specific problems for the implementation of such technique in low-income countries. METHODS A Bluetooth-assisted GPS location-based digital contact tracing (DCT) app was tested by 141 participants during 14 days in a hospital in Monrovia, Liberia in February 2020. The DCT app was compared to a paper-based reference system. Hits between participants and 10 designated infected participants were recorded simultaneously by both methods. Additional data about GPS and Bluetooth adherence were gathered and surveys to estimate battery consumption and app adherence were conducted. DCT apps accuracy was evaluated in different settings. RESULTS GPS coordinates from 101/141 (71.6%) participants were received. The number of hours recorded by the participants during the study period, true Hours Recorded (tHR), was 496.3 h (1.1% of maximum Hours recordable) during the study period. With the paper-based method 1075 hits and with the DCT app five hits of designated infected participants with other participants have been listed. Differences between true and maximum recording times were due to failed permission settings (45%), data transmission issues (11.3%), of the participants 10.1% switched off GPS and 32.5% experienced other technical or compliance problems. In buildings, use of Bluetooth increased the accuracy of the DCT app (GPS + BT 22.9 m ± 21.6 SD vs. GPS 60.9 m ± 34.7 SD; p = 0.004). GPS accuracy in public transportation was 10.3 m ± 10.05 SD with a significant (p = 0.007) correlation between precision and phone brand. GPS resolution outdoors was 10.4 m ± 4.2 SD. CONCLUSION In our study several limitations of the DCT together with the impairment of GPS accuracy in urban settings impede the solely use of a DCT app. It could be feasible as a supplement to traditional manual contact tracing. DKRS, DRKS00029327 . Registered 20 June 2020 - Retrospectively registered.
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Affiliation(s)
- Eric Handmann
- Department for Emergency Medicine, University Hospital Leipzig, Leipzig, Germany.
| | | | - Mosoka P. Fallah
- grid.512250.1National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia
| | - Neima Candy
- grid.512250.1National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia
| | | | - André Gries
- grid.411339.d0000 0000 8517 9062Department for Emergency Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Thomas Grünewald
- grid.459629.50000 0004 0389 4214Clinic for Infectious Diseases and Tropical Medicine and Department for Hospital and Environmental Hygiene, Klinikum Chemnitz, Chemnitz, Germany
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Lutz CB, Giabbanelli PJ. When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations? ADVANCED THEORY AND SIMULATIONS 2022; 5:2100343. [PMID: 35441122 PMCID: PMC9011599 DOI: 10.1002/adts.202100343] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/01/2021] [Indexed: 12/25/2022]
Abstract
The COVID‐19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies are utilized (e.g., masks, social distancing, vaccinations), but officials making decisions have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also require significant processing power or time. It is examined whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories resembling the original simulation results. Using four previously published agent‐based models (ABMs) for COVID‐19, a decision tree regression for each ABM is built and its predictions are compared to the corresponding ABM. Accurate machine learning meta‐models are generated from ABMs without strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data: the root‐mean‐square error (RMSE) with 25% of the data is close to the RMSE for the full dataset (0.15 vs 0.14 in one model; 0.07 vs 0.06 in another). However, meta‐models for ABMs employing strong interventions require much more training data (at least 60%) to achieve a similar accuracy. In conclusion, machine learning meta‐models can be used in some scenarios to assist in faster decision‐making.
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Affiliation(s)
- Christopher B. Lutz
- Department of Computer Science & Software Engineering Miami University 205 Benton Hall Oxford OH 45056 USA
| | - Philippe J. Giabbanelli
- Department of Computer Science & Software Engineering Miami University 205 Benton Hall Oxford OH 45056 USA
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Simeoni R, Maccioni G, Giansanti D. The Vaccination Process against the COVID-19: Opportunities, Problems and mHealth Support. Healthcare (Basel) 2021; 9:1165. [PMID: 34574939 PMCID: PMC8472044 DOI: 10.3390/healthcare9091165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/18/2021] [Accepted: 08/31/2021] [Indexed: 01/12/2023] Open
Abstract
The vaccination against the COVID-19, finally available, has the potential to represent an important defence against the pandemic. The identification of both obstacles and tools to combat them are, at this moment, of strategic importance. Previous experiences on vaccinations have shown solutions and paths to take, also based on the behavioural sciences. The objective of the opinion is to face how mobile technology can help us both to fight these problems and to optimize the vaccination process. The opinion has four polarities. The first polarity consists in having detected the problems hampering an effective vaccination process. These problems have been grouped into the following four: Electronic and Informatic divide, Escape, Exposure risk, and Equity. The second polarity consists in having verified how the mobile technology can be useful to face the identified problems. The third polarity highlights the usefulness and importance of using electronic surveys. These tools are based on mobile technology. They are useful problem sensors for the stakeholders. The fourth polarity faces how mobile technology and mHealth can be of aid to optimize the flow of the vaccination process, from the first call up to the certification. This polarity is supported by an example based on the Italian national App IO. The study highlights: (a) on one side, the potential of mobile technology; on the other side, the need for interventions to reduce the digital divide with the purpose to increase its use. (b) How the role of mobile technology can be complementary to other intervention methods.
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Affiliation(s)
- Rossella Simeoni
- Faculty of Medicine and Surgery, Catholic University, San Martino al Cimino, 010130 Viterbo, Italy;
| | - Giovanni Maccioni
- Centre Tisp, Istituto Superiore di Sanità (ISS), Via Regina Elena 299, 00161 Rome, Italy;
| | - Daniele Giansanti
- Centre Tisp, Istituto Superiore di Sanità (ISS), Via Regina Elena 299, 00161 Rome, Italy;
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