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Whitfield CA, van Tongeren M, Han Y, Wei H, Daniels S, Regan M, Denning DW, Verma A, Pellis L, Hall I. Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector. PLoS One 2023; 18:e0284805. [PMID: 37146037 PMCID: PMC10162531 DOI: 10.1371/journal.pone.0284805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/06/2023] [Indexed: 05/07/2023] Open
Abstract
OBJECTIVE We aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector. METHODS We devloped a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures. RESULTS The baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times. CONCLUSION This work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.
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Affiliation(s)
- Carl A. Whitfield
- Department of Mathematics, University of Manchester, Manchester, England
- Division of Infection, Immunity & Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester, England
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
| | - Martie van Tongeren
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Yang Han
- Department of Mathematics, University of Manchester, Manchester, England
| | - Hua Wei
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Sarah Daniels
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Martyn Regan
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
- National COVID-19 Response Centre, UK Health Security Agency, London, England
| | - David W. Denning
- Division of Infection, Immunity & Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester, England
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
| | - Arpana Verma
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, Manchester, England
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, Manchester, England
| | - Ian Hall
- Department of Mathematics, University of Manchester, Manchester, England
- Manchester Academic Health Science Centre, University of Manchester, Manchester, England
- Public Health Advice, Guidance and Expertise, UK Health Security Agency, London, England
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Synthetic data in health care: A narrative review. PLOS DIGITAL HEALTH 2023; 2:e0000082. [PMID: 36812604 PMCID: PMC9931305 DOI: 10.1371/journal.pdig.0000082] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023]
Abstract
Data are central to research, public health, and in developing health information technology (IT) systems. Nevertheless, access to most data in health care is tightly controlled, which may limit innovation, development, and efficient implementation of new research, products, services, or systems. Using synthetic data is one of the many innovative ways that can allow organizations to share datasets with broader users. However, only a limited set of literature is available that explores its potentials and applications in health care. In this review paper, we examined existing literature to bridge the gap and highlight the utility of synthetic data in health care. We searched PubMed, Scopus, and Google Scholar to identify peer-reviewed articles, conference papers, reports, and thesis/dissertations articles related to the generation and use of synthetic datasets in health care. The review identified seven use cases of synthetic data in health care: a) simulation and prediction research, b) hypothesis, methods, and algorithm testing, c) epidemiology/public health research, d) health IT development, e) education and training, f) public release of datasets, and g) linking data. The review also identified readily and publicly accessible health care datasets, databases, and sandboxes containing synthetic data with varying degrees of utility for research, education, and software development. The review provided evidence that synthetic data are helpful in different aspects of health care and research. While the original real data remains the preferred choice, synthetic data hold possibilities in bridging data access gaps in research and evidence-based policymaking.
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Bardus M, Al Daccache M, Maalouf N, Al Sarih R, Elhajj IH. Data Management and Privacy Policy of COVID-19 Contact-Tracing Apps: Systematic Review and Content Analysis. JMIR Mhealth Uhealth 2022; 10:e35195. [PMID: 35709334 PMCID: PMC9278406 DOI: 10.2196/35195] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/04/2022] [Accepted: 02/17/2022] [Indexed: 01/18/2023] Open
Abstract
Background COVID-19 digital contact-tracing apps were created to assist public health authorities in curbing the pandemic. These apps require users’ permission to access specific functions on their mobile phones, such as geolocation, Bluetooth or Wi-Fi connections, or personal data, to work correctly. As these functions have privacy repercussions, it is essential to establish how contact-tracing apps respect users’ privacy. Objective This study aimed to systematically map existing contact-tracing apps and evaluate the permissions required and their privacy policies. Specifically, we evaluated the type of permissions, the privacy policies’ readability, and the information included in them. Methods We used custom Google searches and existing lists of contact-tracing apps to identify potentially eligible apps between May 2020 and November 2021. We included contact-tracing or exposure notification apps with a Google Play webpage from which we extracted app characteristics (eg, sponsor, number of installs, and ratings). We used Exodus Privacy to systematically extract the number of permissions and classify them as dangerous or normal. We computed a Permission Accumulated Risk Score representing the threat level to the user’s privacy. We assessed the privacy policies’ readability and evaluated their content using a 13-item checklist, which generated a Privacy Transparency Index. We explored the relationships between app characteristics, Permission Accumulated Risk Score, and Privacy Transparency Index using correlations, chi-square tests, or ANOVAs. Results We identified 180 contact-tracing apps across 152 countries, states, or territories. We included 85.6% (154/180) of apps with a working Google Play page, most of which (132/154, 85.7%) had a privacy policy document. Most apps were developed by governments (116/154, 75.3%) and totaled 264.5 million installs. The average rating on Google Play was 3.5 (SD 0.7). Across the 154 apps, we identified 94 unique permissions, 18% (17/94) of which were dangerous, and 30 trackers. The average Permission Accumulated Risk Score was 22.7 (SD 17.7; range 4-74, median 16) and the average Privacy Transparency Index was 55.8 (SD 21.7; range 5-95, median 55). Overall, the privacy documents were difficult to read (median grade level 12, range 7-23); 67% (88/132) of these mentioned that the apps collected personal identifiers. The Permission Accumulated Risk Score was negatively associated with the average App Store ratings (r=−0.20; P=.03; 120/154, 77.9%) and Privacy Transparency Index (r=−0.25; P<.001; 132/154, 85.7%), suggesting that the higher the risk to one’s data, the lower the apps’ ratings and transparency index. Conclusions Many contact-tracing apps were developed covering most of the planet but with a relatively low number of installs. Privacy-preserving apps scored high in transparency and App Store ratings, suggesting that some users appreciate these apps. Nevertheless, privacy policy documents were difficult to read for an average audience. Therefore, we recommend following privacy-preserving and transparency principles to improve contact-tracing uptake while making privacy documents more readable for a wider public.
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Affiliation(s)
- Marco Bardus
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- Department of Health Promotion and Community Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
| | - Melodie Al Daccache
- Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
| | - Noel Maalouf
- Department of Electrical and Computer Engineering, School of Engineering, Lebanese American University, Byblos, Lebanon
- Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Rayan Al Sarih
- Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Imad H Elhajj
- Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
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Marshall DA, Burgos-Liz L, Pasupathy KS, Padula WV, IJzerman MJ, Wong PK, Higashi MK, Engbers J, Wiebe S, Crown W, Osgood ND. Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research. PHARMACOECONOMICS 2016; 34:115-26. [PMID: 26497003 DOI: 10.1007/s40273-015-0330-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
In the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic-big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.
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Affiliation(s)
- Deborah A Marshall
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Room 3C56 Health Research Innovation Centre, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada.
| | - Lina Burgos-Liz
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Room 3C58 Health Research Innovation Centre, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
| | - Kalyan S Pasupathy
- Clinical Engineering Learning Lab, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | - William V Padula
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Maarten J IJzerman
- Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands
| | - Peter K Wong
- Illinois Divisions and HSHS Medical Group, Hospital Sisters Health System (HSHS), Belleville, IL, USA
| | | | - Jordan Engbers
- Clinical Research Unit, University of Calgary, Calgary, AB, Canada
| | - Samuel Wiebe
- Clinical Research Unit, University of Calgary, Calgary, AB, Canada
| | | | - Nathaniel D Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, Canada
- Department of Community Health & Epidemiology and Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada
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Obadia T, Silhol R, Opatowski L, Temime L, Legrand J, Thiébaut ACM, Herrmann JL, Fleury É, Guillemot D, Boëlle PY. Detailed contact data and the dissemination of Staphylococcus aureus in hospitals. PLoS Comput Biol 2015; 11:e1004170. [PMID: 25789632 PMCID: PMC4366219 DOI: 10.1371/journal.pcbi.1004170] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 02/03/2015] [Indexed: 11/19/2022] Open
Abstract
Close proximity interactions (CPIs) measured by wireless electronic devices are increasingly used in epidemiological models. However, no evidence supports that electronically collected CPIs inform on the contacts leading to transmission. Here, we analyzed Staphylococcus aureus carriage and CPIs recorded simultaneously in a long-term care facility for 4 months in 329 patients and 261 healthcare workers to test this hypothesis. In the broad diversity of isolated S. aureus strains, 173 transmission events were observed between participants. The joint analysis of carriage and CPIs showed that CPI paths linking incident cases to other individuals carrying the same strain (i.e. possible infectors) had fewer intermediaries than predicted by chance (P < 0.001), a feature that simulations showed to be the signature of transmission along CPIs. Additional analyses revealed a higher dissemination risk between patients via healthcare workers than via other patients. In conclusion, S. aureus transmission was consistent with contacts defined by electronically collected CPIs, illustrating their potential as a tool to control hospital-acquired infections and help direct surveillance. Recent advances in communication technologies allow monitoring high-resolution contact networks. Close proximity interactions (CPIs) measured by wireless sensors are increasingly used to inform contact networks for the dissemination of pathogens in computational models, although empirical justification is lacking. Here, we conducted a longitudinal prospective study for four months in a hospital, including both patients and healthcare workers (HCWs). High-resolution CPIs were recorded continuously, and participants undertook weekly nasal swabs to detect S. aureus carriage. We set out to test whether the contact network measured by CPIs supported observed transmission episodes. A simulation study was first conducted to choose a test statistic for the association of CPI paths with transmission, showing that CPI path length from transmitter to incident case was the most powerful. Then, we selected patients presenting incident S. aureus colonization in the data. We showed that CPI paths existed to carriers of the same strain, with path lengths significantly shorter than between random pairs of participants, in agreement with the transmission hypothesis. In-hospital contact networks measured by CPIs inform on opportunities for pathogen transmission. These could be used in surveillance systems to help prevent the spread of nosocomial pathogens.
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Affiliation(s)
- Thomas Obadia
- Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France
- INSERM, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France
- * E-mail: (TO); (PYB)
| | - Romain Silhol
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Lulla Opatowski
- Inserm UMR 1181 “Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases” (B2PHI), F-75015, Paris, France
- Institut Pasteur, UMR 1181, B2PHI, F-75015, Paris, France
- Univ. Versailles St Quentin, UMR 1181, B2PHI, F-78180 Montigny-le-Bretonneux
| | - Laura Temime
- Laboratoire MESuRS, Conservatoire National des Arts et Métiers, 75003, Paris, France
| | - Judith Legrand
- Univ Paris-Sud, UMR 0320/UMR8120 Génétique Quantitative et Evolution—Le Moulon, F-91190, Gif-sur-Yvette, France
| | - Anne C. M. Thiébaut
- Inserm UMR 1181 “Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases” (B2PHI), F-75015, Paris, France
- Institut Pasteur, UMR 1181, B2PHI, F-75015, Paris, France
- Univ. Versailles St Quentin, UMR 1181, B2PHI, F-78180 Montigny-le-Bretonneux
| | - Jean-Louis Herrmann
- INSERM U1173, UFR Simone Veil, Versailles-Saint-Quentin University, 78180, Saint-Quentin en Yvelines, France
- AP-HP, Hôpital Raymond Poincaré, Service de Microbiologie, F-92380, Garches, France
| | - Éric Fleury
- ENS de Lyon, Université de Lyon, Laboratoire de l’Informatique du Parallélisme (UMR CNRS 5668—ENS de Lyon—UCB Lyon 1), IXXI Rhône Alpes Complex Systems Institute, Lyon, France
- Inria—Institut National de Recherche en Informatique et en Automatique, Montbonnet, France
| | - Didier Guillemot
- Inserm UMR 1181 “Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases” (B2PHI), F-75015, Paris, France
- Institut Pasteur, UMR 1181, B2PHI, F-75015, Paris, France
- Univ. Versailles St Quentin, UMR 1181, B2PHI, F-78180 Montigny-le-Bretonneux
- AP-HP, Raymond Poincare Hospital, F-92380 Garches, France
| | - Pierre-Yves Boëlle
- Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France
- INSERM, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France
- AP-HP, Hôpital Saint-Antoine, Département de Santé Publique, F-75571, Paris, France
- * E-mail: (TO); (PYB)
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Temporal aggregation impacts on epidemiological simulations employing microcontact data. BMC Med Inform Decis Mak 2012; 12:132. [PMID: 23153380 PMCID: PMC3575321 DOI: 10.1186/1472-6947-12-132] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 10/31/2012] [Indexed: 11/30/2022] Open
Abstract
Background Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population’s contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes. Methods We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks. Results No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs – which extrapolate from data collected over a brief period – exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation. Conclusions The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.
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