1
|
Mazzoli M, Gallotti R, Privitera F, Colet P, Ramasco JJ. Spatial immunization to abate disease spreading in transportation hubs. Nat Commun 2023; 14:1448. [PMID: 36941266 PMCID: PMC10027826 DOI: 10.1038/s41467-023-36985-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
Proximity social interactions are crucial for infectious diseases transmission. Crowded agglomerations pose serious risk of triggering superspreading events. Locations like transportation hubs (airports and stations) are designed to optimize logistic efficiency, not to reduce crowding, and are characterized by a constant in and out flow of people. Here, we analyze the paradigmatic example of London Heathrow, one of the busiest European airports. Thanks to a dataset of anonymized individuals' trajectories, we can model the spreading of different diseases to localize the contagion hotspots and to propose a spatial immunization policy targeting them to reduce disease spreading risk. We also detect the most vulnerable destinations to contagions produced at the airport and quantify the benefits of the spatial immunization technique to prevent regional and global disease diffusion. This method is immediately generalizable to train, metro and bus stations and to other facilities such as commercial or convention centers.
Collapse
Affiliation(s)
- Mattia Mazzoli
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, Paris, France.
| | - Riccardo Gallotti
- CHuB Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo (TN), Trento, Italy
| | | | - Pere Colet
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain
| | - José J Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
| |
Collapse
|
2
|
Improving physical distancing among healthcare workers in a pediatric intensive care unit. Infect Control Hosp Epidemiol 2022; 43:1790-1795. [PMID: 34903308 PMCID: PMC8692852 DOI: 10.1017/ice.2021.501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
BACKGROUND Healthcare workers (HCWs) not adhering to physical distancing recommendations is a risk factor for acquisition of severe acute respiratory coronavirus virus 2 (SARS-CoV-2). The study objective was to assess the impact of interventions to improve HCW physical distancing on actual distance between HCWs in a real-life setting. METHODS HCWs voluntarily wore proximity beacons to measure the number and intensity of physical distancing interactions between each other in a pediatric intensive care unit. We compared interactions before and after implementing a bundle of interventions including changes to the layout of workstations, cognitive aids, and individual feedback from wearable proximity beacons. RESULTS Overall, we recorded 10,788 interactions within 6 feet (∼2 m) and lasting >5 seconds. The number of HCWs wearing beacons fluctuated daily and increased over the study period. On average, 13 beacons were worn daily (32% of possible staff; range, 2-32 per day). We recorded 3,218 interactions before the interventions and 7,570 interactions after the interventions began. Using regression analysis accounting for the maximum number of potential interactions if all staff had worn beacons on a given day, there was a 1% decline in the number of interactions per possible interactions in the postintervention period (incident rate ratio, 0.99; 95% confidence interval, 0.98-1.00; P = .02) with fewer interactions occurring at nursing stations, in workrooms and during morning rounds. CONCLUSIONS Using quantitative data from wearable proximity beacons, we found an overall small decline in interactions within 6 feet between HCWs in a busy intensive care unit after a multifaceted bundle of interventions was implemented to improve physical distancing.
Collapse
|
3
|
Novel estimates reveal subnational heterogeneities in disease-relevant contact patterns in the United States. PLoS Comput Biol 2022; 18:e1010742. [PMID: 36459512 DOI: 10.1371/journal.pcbi.1010742] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 12/14/2022] [Accepted: 11/16/2022] [Indexed: 12/04/2022] Open
Abstract
Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources using multilevel regression with poststratification (MRP). We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.
Collapse
|
4
|
Hagaman A, Lopez Mercado D, Poudyal A, Bemme D, Boone C, van Heerden A, Byanjankar P, Man Maharjan S, Thapa A, Kohrt BA. "Now, I have my baby so I don't go anywhere": A mixed method approach to the 'everyday' and young motherhood integrating qualitative interviews and passive digital data from mobile devices. PLoS One 2022; 17:e0269443. [PMID: 35802694 PMCID: PMC9269952 DOI: 10.1371/journal.pone.0269443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 05/21/2022] [Indexed: 11/18/2022] Open
Abstract
The impacts of early pregnancy and young motherhood on everyday life, including interpersonal and individual behavior, are not well-known. Passive digital sensing on mobile technology including smartphones and passive Bluetooth beacons can yield information such as geographic movement, physical activity, and mother-infant proximity to illuminate behavioral patterns of a mother's everyday in Nepal. We contribute to mixed-methods research by triangulating passive sensing data (GPS, accelerometry, Bluetooth proximity) with multiple forms of qualitative data to characterize behavioral patterns and experiences of young motherhood in the first year postpartum. We triangulated this digital information in a constant comparative analysis with in-depth interviews, daily diaries, and fieldnotes. We reveal typical behavioral patterns of rural young mothers and highlight opportunities for integrating this information to improve health and well-being.
Collapse
Affiliation(s)
- Ashley Hagaman
- Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
- * E-mail:
| | - Damaris Lopez Mercado
- Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Anubhuti Poudyal
- Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, Washington, DC, United States of America
| | - Dörte Bemme
- Department for Global Health and Social Medicine, Centre for Society & Mental Health, King’s College London, London, United Kingdom
| | - Clare Boone
- Yale University, New Haven, Connecticut, United States of America
| | - Alastair van Heerden
- Human and Social Development, Human Sciences Research Council, Pietermaritzburg, South Africa
- Medical Research Council/Wits Developmental Pathways for Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Prabin Byanjankar
- Transcultural Psychosocial Organization (TPO) Nepal, Kathmandu, Nepal
| | | | - Ada Thapa
- Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, Washington, DC, United States of America
| | - Brandon A. Kohrt
- Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, Washington, DC, United States of America
- George Washington School of Medicine and Health Sciences, Washington, DC, United States of America
| |
Collapse
|
5
|
Ribeiro M, Nunes N, Nisi V, Schöning J. Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies. PERSONAL AND UBIQUITOUS COMPUTING 2022; 26:505-519. [PMID: 32958999 PMCID: PMC7495138 DOI: 10.1007/s00779-020-01441-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 08/12/2020] [Indexed: 05/14/2023]
Abstract
In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people's mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.
Collapse
Affiliation(s)
- Miguel Ribeiro
- Instituto Superior Téncico, ITI/LARSyS, ULisbon, Portugal
| | - Nuno Nunes
- Instituto Superior Téncico, ITI/LARSyS, ULisbon, Portugal
| | | | - Johannes Schöning
- Human-Computer Interaction, University of Bremen, ITI/LARSyS, Bremen, Germany
| |
Collapse
|
6
|
Keller SC, Salinas AB, Oladapo-Shittu O, Cosgrove SE, Lewis-Cherry R, Osei P, Gurses AP, Jacak R, Zudock KK, Blount KM, Bowden KV, Rock C, Sick-Samuels AC, Vecchio-Pagan B. The case for wearable proximity devices to inform physical distancing among healthcare workers. JAMIA Open 2021; 4:ooab095. [PMID: 34926997 PMCID: PMC8672930 DOI: 10.1093/jamiaopen/ooab095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/16/2021] [Accepted: 11/11/2021] [Indexed: 12/23/2022] Open
Abstract
Objective Despite the importance of physical distancing in reducing SARS-CoV-2
transmission, this practice is challenging in healthcare. We piloted use of
wearable proximity beacons among healthcare workers (HCWs) in an inpatient
unit to highlight considerations for future use of trackable technologies in
healthcare settings. Materials and Methods We performed a feasibility pilot study in a non-COVID adult medical unit from
September 28 to October 28, 2020. HCWs wore wearable proximity beacons, and
interactions defined as <6 feet for ≥5 s were recorded.
Validation was performed using direct observations. Results A total of 6172 close proximity interactions were recorded, and with the
removal of 2033 false-positive interactions, 4139 remained. The highest
proportion of interactions occurred between 7:00 Am–9:00
Am. Direct observations of HCWs substantiated these
findings. Discussion This pilot study showed that wearable beacons can be used to monitor and
quantify HCW interactions in inpatient settings. Conclusion Technology can be used to track HCW physical distancing. Physical distancing, or social distancing, is important in preventing COVID-19.
It is hard for healthcare workers (HCWs) to physically distance at work. We
tested a device (proximity beacon) that HCWs could wear to measure their
distance from each other among HCWs on a medical unit. The device measured any
time HCWs were within 6 feet of each other for at least 5 s. We watched HCWs who
were close to each other. The devices and our observations showed that 7:00
Am—9:00 Am was the highest risk time for not
physically distancing. This study shows that wearable devices can be a tool to
monitor HCWs physical distancing on a hospital unit.
Collapse
Affiliation(s)
- Sara C Keller
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Alejandra B Salinas
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Opeyemi Oladapo-Shittu
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sara E Cosgrove
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Robin Lewis-Cherry
- Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Patience Osei
- Armstrong Institute of Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ayse P Gurses
- Department of Anesthesiology and Critical Care Medicine, Armstrong Institute of Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ron Jacak
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA
| | - Kristina K Zudock
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA
| | - Kianna M Blount
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA
| | - Kenneth V Bowden
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA
| | - Clare Rock
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Anna C Sick-Samuels
- Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Briana Vecchio-Pagan
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA
| |
Collapse
|
7
|
Derjany P, Namilae S, Srinivasan A. Parameter Space Exploration in Pedestrian Queue Design to Mitigate Infectious Disease Spread. J Indian Inst Sci 2021; 101:329-339. [PMID: 34366585 PMCID: PMC8329629 DOI: 10.1007/s41745-021-00254-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/06/2021] [Indexed: 12/14/2022]
Abstract
Reducing the interactions between pedestrians in crowded environments can potentially curb the spread of infectious diseases including COVID-19. The mixing of susceptible and infectious individuals in many high-density man-made environments such as waiting queues involves pedestrian movement, which is generally not taken into account in modeling studies of disease dynamics. In this paper, a social force-based pedestrian-dynamics approach is used to evaluate the contacts among proximate pedestrians which are then integrated with a stochastic epidemiological model to estimate the infectious disease spread in a localized outbreak. Practical application of such multiscale models to real-life scenarios can be limited by the uncertainty in human behavior, lack of data during early stage epidemics, and inherent stochasticity in the problem. We parametrize the sources of uncertainty and explore the associated parameter space using a novel high-efficiency parameter sweep algorithm. We show the effectiveness of a low-discrepancy sequence (LDS) parameter sweep in reducing the number of simulations required for effective parameter space exploration in this multiscale problem. The algorithms are applied to a model problem of infectious disease spread in a pedestrian queue similar to that at an airport security check point. We find that utilizing the low-discrepancy sequence-based parameter sweep, even for one component of the multiscale model, reduces the computational requirement by an order of magnitude.
Collapse
Affiliation(s)
- Pierrot Derjany
- Aerospace Engineering Department, Embry Riddle Aeronautical University, Daytona Beach, FL USA
| | - Sirish Namilae
- Aerospace Engineering Department, Embry Riddle Aeronautical University, Daytona Beach, FL USA
| | | |
Collapse
|
8
|
Colman E, Colizza V, Hanks EM, Hughes DP, Bansal S. Social fluidity mobilizes contagion in human and animal populations. eLife 2021; 10:62177. [PMID: 34328080 PMCID: PMC8324292 DOI: 10.7554/elife.62177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 06/25/2021] [Indexed: 11/13/2022] Open
Abstract
Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.
Collapse
Affiliation(s)
- Ewan Colman
- Department of Biology, Georgetown University, Washington, United States.,Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP UMRS 1136), F75012, Paris, France
| | - Ephraim M Hanks
- Department of Statistics, Eberly College of Science, Penn State University, State College, United States
| | - David P Hughes
- Department of Entomology, College of Agricultural Sciences, Penn State University, State College, United States
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, United States
| |
Collapse
|
9
|
Rocha LEC, Ryckebusch J, Schoors K, Smith M. The scaling of social interactions across animal species. Sci Rep 2021; 11:12584. [PMID: 34131247 PMCID: PMC8206375 DOI: 10.1038/s41598-021-92025-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/01/2021] [Indexed: 02/05/2023] Open
Abstract
Social animals self-organise to create groups to increase protection against predators and productivity. One-to-one interactions are the building blocks of these emergent social structures and may correspond to friendship, grooming, communication, among other social relations. These structures should be robust to failures and provide efficient communication to compensate the costs of forming and maintaining the social contacts but the specific purpose of each social interaction regulates the evolution of the respective social networks. We collate 611 animal social networks and show that the number of social contacts E scales with group size N as a super-linear power-law [Formula: see text] for various species of animals, including humans, other mammals and non-mammals. We identify that the power-law exponent [Formula: see text] varies according to the social function of the interactions as [Formula: see text], with [Formula: see text]. By fitting a multi-layer model to our data, we observe that the cost to cross social groups also varies according to social function. Relatively low costs are observed for physical contact, grooming and group membership which lead to small groups with high and constant social clustering. Offline friendship has similar patterns while online friendship shows weak social structures. The intermediate case of spatial proximity (with [Formula: see text] and clustering dependency on network size quantitatively similar to friendship) suggests that proximity interactions may be as relevant for the spread of infectious diseases as for social processes like friendship.
Collapse
Affiliation(s)
- Luis E. C. Rocha
- grid.5342.00000 0001 2069 7798Department of Economics, Ghent University, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Jan Ryckebusch
- grid.5342.00000 0001 2069 7798Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Koen Schoors
- grid.5342.00000 0001 2069 7798Department of Economics, Ghent University, Ghent, Belgium ,grid.77852.3f0000 0000 8618 9465Higher School of Economics, National Research University, Moscow, Russia
| | - Matthew Smith
- grid.20409.3f000000012348339XThe Business School, Edinburgh Napier University, Edinburgh, UK
| |
Collapse
|
10
|
Zuo F, Gao J, Kurkcu A, Yang H, Ozbay K, Ma Q. Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic. JOURNAL OF TRANSPORT & HEALTH 2021; 21:101032. [PMID: 36567866 PMCID: PMC9765816 DOI: 10.1016/j.jth.2021.101032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 01/13/2021] [Accepted: 02/24/2021] [Indexed: 05/06/2023]
Abstract
INTRODUCTION The rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between "social distancing," a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic. METHODS There are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns. RESULTS The results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios.
Collapse
Affiliation(s)
- Fan Zuo
- C2SMART Center, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY, 11201, USA
| | - Jingqin Gao
- C2SMART Center, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY, 11201, USA
| | - Abdullah Kurkcu
- Ulteig, 5575 DTC Parkway, Suite 200, Greenwood Village, CO, 80111, USA
| | - Hong Yang
- Department of Computational Modeling and Simulation Engineering, Old Dominion University, 1117 ENGR & COMP SCI BLDG, Norfolk, VA, 23529, USA
| | - Kaan Ozbay
- C2SMART Center, Department of Civil and Urban Engineering & Center for Urban Science and Progress (CUSP), Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY, 11201, USA
| | - Qingyu Ma
- Department of Computational Modeling and Simulation Engineering, Old Dominion University, 1117 ENGR & COMP SCI BLDG, Norfolk, VA, 23529, USA
| |
Collapse
|
11
|
Xia H, Horn J, Piotrowska MJ, Sakowski K, Karch A, Tahir H, Kretzschmar M, Mikolajczyk R. Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections. PLoS Comput Biol 2021; 17:e1008941. [PMID: 33956787 PMCID: PMC8130968 DOI: 10.1371/journal.pcbi.1008941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 05/18/2021] [Accepted: 04/06/2021] [Indexed: 11/25/2022] Open
Abstract
In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks. Patterns of patients’ transfer between different hospitals contribute crucially to the risk of hospital-acquired infections (HAIs) in the health care system. To quantify this risk, network models can be applied. The estimated risk can be inaccurate in the case of incomplete data on hospital admissions, which can be a consequence of the multiplicity of insurance companies as it is the case in Germany. To develop a better understanding of how incompleteness of data affects network measures and the simulated spread of HAI, we compared those measures derived from sampled, scale-up and original data, based on hospitalization data from three AOK insurance companies. We found that common network measures were affected by incompleteness, but the simulated prevalence as a measure of epidemic spread in the network was robust over a large range of incompleteness proportions. Epidemics and the transition of the infectious diseases may be modelled on hospital data with a coverage as low as 10% of the local population, whilst maintaining accuracy to within 10% of the true population prevalence.
Collapse
Affiliation(s)
- Hanjue Xia
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle, Saxony-Anhalt, Germany
| | - Johannes Horn
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle, Saxony-Anhalt, Germany
| | - Monika J. Piotrowska
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Konrad Sakowski
- Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Poland
- Institute of High Pressure Physics, Polish Academy of Sciences, Warsaw, Poland
| | - André Karch
- Institute for Epidemiology and Social Medicine, University of Münster, Münster, North Rhine-Westphalia, Germany
| | - Hannan Tahir
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mirjam Kretzschmar
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle, Saxony-Anhalt, Germany
- * E-mail:
| |
Collapse
|
12
|
Leal Neto O, Haenni S, Phuka J, Ozella L, Paolotti D, Cattuto C, Robles D, Lichand G. Combining Wearable Devices and Mobile Surveys to Study Child and Youth Development in Malawi: Implementation Study of a Multimodal Approach. JMIR Public Health Surveill 2021; 7:e23154. [PMID: 33536159 PMCID: PMC7980111 DOI: 10.2196/23154] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 11/05/2020] [Accepted: 02/02/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Multimodal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs (from phone surveys to signals from noninvasive biomarkers) to understand children's health and development outcomes more integrally from multiple perspectives. OBJECTIVE The aim of this work was to describe an implementation study using a multimodal approach combining noninvasive biomarkers, social contact patterns, mobile surveying, and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at a high frequency. METHODS We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collected through wearable devices (electrocardiography [ECG] hand pads and electroencephalography [EEG] headbands). Additionally, wearable proximity sensors to elicit the social network were deployed among children and their caregivers. Mobile surveys using interactive voice response calls were also used as an additional layer of data collection. An end-line face-to-face survey was conducted at the end of the study. RESULTS During the implementation, 82 EEG/ECG data entry points were collected across four villages. The sampled children for EEG/ECG were 0 to 5 years old. EEG/ECG data were collected once a week. In every session, children wore the EEG headband for 5 minutes and the ECG hand pad for 3 minutes. In total, 3531 calls were sent over 5 weeks, with 2291 participants picking up the calls and 984 of those answering the consent question. In total, 585 people completed the surveys over the course of 5 weeks. CONCLUSIONS This study achieved its objective of demonstrating the feasibility of generating data through the unprecedented use of a multimodal approach for tracking child development in Malawi, which is one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex. It is the case not only that no data stream in isolation can accurately characterize it, but also that even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes at a sufficiently high frequency allows researchers to make progress by considering contact patterns, reported symptoms and behaviors, and critical biomarkers all at once. This application showcases that even in developing countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools for understanding child well-being and development.
Collapse
Affiliation(s)
- Onicio Leal Neto
- Department of Economics, University of Zurich, Zurich, Switzerland
| | - Simon Haenni
- Department of Economics, University of Zurich, Zurich, Switzerland
| | - John Phuka
- College of Medicine, University of Malawi, Lilongwe, Malawi
| | | | | | - Ciro Cattuto
- ISI Foundation, Turin, Italy
- Department of Computer Science, University of Torino, Turin, Italy
| | - Daniel Robles
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | | |
Collapse
|
13
|
Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study. PLoS Comput Biol 2021; 17:e1008600. [PMID: 33534784 PMCID: PMC7857595 DOI: 10.1371/journal.pcbi.1008600] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/02/2020] [Indexed: 11/23/2022] Open
Abstract
The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread. Pathogens including multidrug resistant enterobacteriacae (MDR-E) inside hospital settings are associated with higher morbidity, mortality, and healthcare costs. Better understanding of the transmission routes of these pathogens is required to develop targeted and efficient measures to contain the spread of MDR-E in a hospital. We analyzed datasets from five hospitals in different countries to explore patient movement patterns between departments of these hospitals (intra-hospital movements). We assessed whether movement patterns differ between patients at high or low risk of colonization. Our results show that in every intra-hospital network, there exist a few departments which are strongly connected and many peripheral departments which are loosely connected. High-risk patients stay on average longer in the hospital, and move more frequently between departments than low-risk patients. Targeting interventions to strongly connected departments may help to reduce pathogen spread inside the hospital. To explore this, we simulated the spread of MDR-E inside one hospital using an agent-based model that includes patient movements. In the simulations, we found positive correlations between departments’ prevalence and network characteristics such as degree and weighted degree, thus highlighting the importance of targeting interventions to departments of higher (weighted) degree to control the spread of MDR-E.
Collapse
|
14
|
Wirth FN, Johns M, Meurers T, Prasser F. Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review. JMIR Mhealth Uhealth 2020; 8:e22594. [PMID: 33074833 PMCID: PMC7674146 DOI: 10.2196/22594] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/26/2020] [Accepted: 10/09/2020] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches. OBJECTIVE Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented. METHODS We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions. RESULTS We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases. CONCLUSIONS We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).
Collapse
Affiliation(s)
- Felix Nikolaus Wirth
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Marco Johns
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Thierry Meurers
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Fabian Prasser
- Berlin Institute of Health, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| |
Collapse
|
15
|
Sensor-based proximity metrics for team research. A validation study across three organizational contexts. Behav Res Methods 2020; 53:718-743. [PMID: 32813224 PMCID: PMC8062328 DOI: 10.3758/s13428-020-01444-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Wearable sensors are becoming increasingly popular in organizational research. Although validation studies that examine sensor data in conjunction with established social and psychological constructs are becoming more frequent, they are usually limited for two reasons: first, most validation studies are carried out under laboratory settings. Only a handful of studies have been carried out in real-world organizational environments. Second, for those studies carried out in field settings, reported findings are derived from a single case only, thus seriously limiting the possibility of studying the influence of contextual factors on sensor-based measurements. This article presents a validation study of expressive and instrumental ties across nine relatively small R&D teams. The convergent validity of Bluetooth (BT) detections is reported for friendship and advice-seeking ties under three organizational contexts: research labs, private companies, and university-based teams. Results show that, in general, BT detections correlated strongly with self-reported measurements. However, the organizational context affects both the strength of the observed correlation and its direction. Whereas advice-seeking ties generally occur in close spatial proximity and are best identified in university environments, friendship relationships occur at a greater spatial distance, especially in research labs. We conclude with recommendations for fine-tuning the validity of sensor measurements by carefully examining the opportunities for organizational embedding in relation to the research question and collecting complementary data through mixed-method research designs.
Collapse
|
16
|
Bu F, Aiello AE, Xu J, Volfovsky A. Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks. J Am Stat Assoc 2020. [DOI: 10.1080/01621459.2020.1790376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Fan Bu
- Department of Statistical Science, Duke University, Durham, NC
| | - Allison E. Aiello
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jason Xu
- Department of Statistical Science, Duke University, Durham, NC
| | | |
Collapse
|
17
|
Ming DK, Sangkaew S, Chanh HQ, Nhat PTH, Yacoub S, Georgiou P, Holmes AH. Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses. Int J Infect Dis 2020; 96:648-654. [PMID: 32497806 PMCID: PMC7263257 DOI: 10.1016/j.ijid.2020.05.086] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/15/2020] [Accepted: 05/23/2020] [Indexed: 01/12/2023] Open
Abstract
Optimal management of infectious diseases is guided by up-to-date information at the individual and public health levels. For infections of global importance, including emerging pandemics such as COVID-19 or prevalent endemic diseases such as dengue, identifying patients at risk of severe disease and clinical deterioration can be challenging, considering that the majority present with a mild illness. In our article, we describe the use of wearable technology for continuous physiological monitoring in healthcare settings. Deployment of wearables in hospital settings for the management of infectious diseases, or in the community to support syndromic surveillance during outbreaks, could provide significant, cost-effective advantages and improve healthcare delivery. We highlight a range of promising technologies employed by wearable devices and discuss the technical and ethical issues relating to implementation in the clinic, focusing on low- and middle- income countries. Finally, we propose a set of essential criteria for the rollout of wearable technology for clinical use.
Collapse
Affiliation(s)
- Damien K Ming
- NIHR-Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK; Centre for Antimicrobial Optimisation (CAMO), Imperial College London, UK.
| | - Sorawat Sangkaew
- NIHR-Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK; Department of Family Medicine, Hat Yai Regional Hospital, Thailand
| | - Ho Q Chanh
- Oxford University Clinical Research Unit (OUCRU), Ho Chi Minh City, Viet Nam
| | - Phung T H Nhat
- Oxford University Clinical Research Unit (OUCRU), Ho Chi Minh City, Viet Nam
| | - Sophie Yacoub
- Oxford University Clinical Research Unit (OUCRU), Ho Chi Minh City, Viet Nam; Centre for Tropical Medicine and Global Health, University of Oxford, UK
| | | | - Alison H Holmes
- NIHR-Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK; Centre for Antimicrobial Optimisation (CAMO), Imperial College London, UK
| |
Collapse
|
18
|
Abstract
With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing-based risk alert systems to spontaneously obtain and analyze social data to infer the state of the COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas by inferring future propagation patterns. The CovidSens concept is motivated by three observations: (1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, (2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable mobile devices that are able to perform non-trivial on-device computation for data processing and analytics. We envision an unprecedented opportunity to leverage the posts generated by the ordinary people to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information about the COVID-19 with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively, and alert them to remain prepared? How to leverage the computational power on the edge devices (e.g., smartphones, IoT devices, UAVs) to construct fully integrated edge-based social sensing platforms for rapid detection of the COVID-19 spread? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in developing reliable social sensing-based risk alert systems. We envision that approaches originating from multiple disciplines (e.g., AI, estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.
Collapse
|
19
|
Rocha LEC, Singh V, Esch M, Lenaerts T, Liljeros F, Thorson A. Dynamic contact networks of patients and MRSA spread in hospitals. Sci Rep 2020; 10:9336. [PMID: 32518310 PMCID: PMC7283340 DOI: 10.1038/s41598-020-66270-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 05/14/2020] [Indexed: 11/09/2022] Open
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.
Collapse
Affiliation(s)
- Luis E C Rocha
- Department of Economics, Ghent University, Ghent, Belgium. .,Department of Physics and Astronomy, Ghent University, Ghent, Belgium.
| | | | - Markus Esch
- Department of Engineering, Saarland University of Applied Sciences, Saarbrücken, Germany
| | - Tom Lenaerts
- MLG, Université Libre de Bruxelles, Brussels, Belgium.,AI-lab, Vrije Universteit Brussel, Brussels, Belgium.,Interuniversity Institute for Bioinformatics, Brussels, Belgium
| | - Fredrik Liljeros
- Department of Sociology, Stockholm University, Stockholm, Sweden
| | - Anna Thorson
- Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden.,World Health Organisation, Geneva, Switzerland
| |
Collapse
|
20
|
Millson C, Considine A, Cramp ME, Holt A, Hubscher S, Hutchinson J, Jones K, Leithead J, Masson S, Menon K, Mirza D, Neuberger J, Prasad R, Pratt A, Prentice W, Shepherd L, Simpson K, Thorburn D, Westbrook R, Tripathi D. Adult liver transplantation: UK clinical guideline - part 2: surgery and post-operation. Frontline Gastroenterol 2020; 11:385-396. [PMID: 32879722 PMCID: PMC7447281 DOI: 10.1136/flgastro-2019-101216] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 09/01/2019] [Accepted: 09/30/2019] [Indexed: 02/06/2023] Open
Abstract
Survival rates for patients following liver transplantation exceed 90% at 12 months and approach 70% at 10 years. Part 1 of this guideline has dealt with all aspects of liver transplantation up to the point of placement on the waiting list. Part 2 explains the organ allocation process, organ donation and organ type and how this influences the choice of recipient. After organ allocation, the transplant surgery and the critical early post-operative period are, of necessity, confined to the liver transplant unit. However, patients will eventually return to their referring secondary care centre with a requirement for ongoing supervision. Part 2 of this guideline concerns three key areas of post liver transplantation care for the non-transplant specialist: (1) overseeing immunosuppression, including interactions and adherence; (2) the transplanted organ and how to initiate investigation of organ dysfunction; and (3) careful oversight of other organ systems, including optimising renal function, cardiovascular health and the psychosocial impact. The crucial significance of this holistic approach becomes more obvious as time passes from the transplant, when patients should expect the responsibility for managing the increasing number of non-liver consequences to lie with primary and secondary care.
Collapse
Affiliation(s)
- Charles Millson
- Department of Hepatology, York Teaching Hospitals NHS Foundation Trust, York, UK
| | - Aisling Considine
- Pharmacy department, King's College Hospital NHS Foundation Trust, London, UK
| | - Matthew E Cramp
- South West Liver Unit, Plymouth Hospitals NHS Trust, Plymouth, UK
| | - Andrew Holt
- Liver Unit, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Stefan Hubscher
- Department of Cellular Pathology, University of Birmingham, Birmingham, UK
| | - John Hutchinson
- Department of Hepatology, York Teaching Hospitals NHS Foundation Trust, York, UK
| | - Kate Jones
- Liver Transplantation Service, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Joanna Leithead
- Department of Hepatology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Steven Masson
- Liver Unit, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Krish Menon
- Liver Transplantation & HPB Surgery, King’s College Hospital NHS Foundation Trust, London, UK
| | - Darius Mirza
- Liver Transplantation & HPB surgery, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - James Neuberger
- Liver Unit, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Raj Prasad
- Liver Transplantation & HPB Surgery, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Anthony Pratt
- Pharmacy Department, York Teaching Hospital NHS Foundation Trust, York, UK
| | - Wendy Prentice
- Palliative Care Medicine, King’s College Hospital NHS Foundation Trust, London, UK
| | - Liz Shepherd
- Liver Transplantation Service, Royal Free London NHS Foundation Trust, London, UK
| | - Ken Simpson
- Scottish Liver Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Doug Thorburn
- Department of Hepatology, Royal Free London NHS Foundation Trust, London, UK
| | - Rachel Westbrook
- Department of Hepatology, Royal Free London NHS Foundation Trust, London, UK
| | - Dhiraj Tripathi
- Liver Unit, University Hospitals Birmingham NHS Foundation Trust, Birminghams, UK
| |
Collapse
|
21
|
Rocha LE, Terenius O, Veissier I, Meunier B, Nielsen PP. Persistence of sociality in group dynamics of dairy cattle. Appl Anim Behav Sci 2020. [DOI: 10.1016/j.applanim.2019.104921] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
22
|
Mones E, Stopczynski A, Pentland A'S, Hupert N, Lehmann S. Optimizing targeted vaccination across cyber-physical networks: an empirically based mathematical simulation study. J R Soc Interface 2019; 15:rsif.2017.0783. [PMID: 29298957 DOI: 10.1098/rsif.2017.0783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 12/01/2017] [Indexed: 01/13/2023] Open
Abstract
Targeted vaccination, whether to minimize the forward transmission of infectious diseases or their clinical impact, is one of the 'holy grails' of modern infectious disease outbreak response, yet it is difficult to achieve in practice due to the challenge of identifying optimal targets in real time. If interruption of disease transmission is the goal, targeting requires knowledge of underlying person-to-person contact networks. Digital communication networks may reflect not only virtual but also physical interactions that could result in disease transmission, but the precise overlap between these cyber and physical networks has never been empirically explored in real-life settings. Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. We show that individuals selected on the basis of their closeness centrality within cyber networks (what we call 'cyber-directed vaccination') can enhance vaccination campaigns against diseases with short-range (but not full-range) modes of transmission.
Collapse
Affiliation(s)
- Enys Mones
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Arkadiusz Stopczynski
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.,Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Nathaniel Hupert
- Weill Cornell Medical College, Cornell University, Ithaca, NY, USA
| | - Sune Lehmann
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark .,The Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
23
|
Müller J, Fàbregues S, Guenther EA, Romano MJ. Using Sensors in Organizational Research-Clarifying Rationales and Validation Challenges for Mixed Methods. Front Psychol 2019; 10:1188. [PMID: 31178800 PMCID: PMC6543914 DOI: 10.3389/fpsyg.2019.01188] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 05/06/2019] [Indexed: 11/13/2022] Open
Abstract
Sensor-based data are becoming increasingly widespread in social, behavioral, and organizational sciences. Far from providing a neutral window on "reality," sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral-, and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors.
Collapse
Affiliation(s)
- Jörg Müller
- IN3 – Universitat Oberta de Catalunya, Castelldefels, Spain
| | | | - Elisabeth Anna Guenther
- Universität Klagenfurt, Universitätszentrum für Frauen- und Geschlechterstudien, Klagenfurt, Austria
| | | |
Collapse
|
24
|
Duval A, Obadia T, Boëlle PY, Fleury E, Herrmann JL, Guillemot D, Temime L, Opatowski L. Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings. PLoS Comput Biol 2019; 15:e1006496. [PMID: 31145725 PMCID: PMC6542504 DOI: 10.1371/journal.pcbi.1006496] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 04/08/2019] [Indexed: 11/19/2022] Open
Abstract
Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC-and ESBL-KP-carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics only, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI network's ability to support observed incident-colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species. CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination.
Collapse
Affiliation(s)
- Audrey Duval
- Equipe PheMI, unité B2PHI, Inserm, Université de Versailles Saint Quentin, Institut Pasteur,Paris, France
| | - Thomas Obadia
- Malaria: Parasites & Hosts Unit, Department of Parasites & Insect Vectors, Institut Pasteur,Paris, France
- Institut Pasteur—Bioinformatics and Biostatistics Hub—C3BI, USR 3756 IP CNRS—Paris, France
| | - Pierre-Yves Boëlle
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, PARIS France
| | - Eric Fleury
- Univ Lyon, Cnrs, ENS de Lyon, Inria, UCB Lyon 1, LIP UMR 5668, Lyon, FRANCE
| | - Jean-Louis Herrmann
- INSERM U1173, UFR Simone Veil, Versailles-Saint-Quentin University, Saint-Quentin en Yvelines, France AP-HP, Service de Microbiologie, Hôpital Raymond Poincaré, Garches, France
| | - Didier Guillemot
- Equipe PheMI, unité B2PHI, Inserm, Université de Versailles Saint Quentin, Institut Pasteur,Paris, France
| | - Laura Temime
- Laboratoire MESuRS, Conservatoire national des Arts et Métiers, Paris, France
- Institut Pasteur, Cnam, unité PACRI, Paris, France
| | - Lulla Opatowski
- Equipe PheMI, unité B2PHI, Inserm, Université de Versailles Saint Quentin, Institut Pasteur,Paris, France
| | | |
Collapse
|
25
|
de Barbaro K. Automated sensing of daily activity: A new lens into development. Dev Psychobiol 2019; 61:444-464. [PMID: 30883745 PMCID: PMC7343175 DOI: 10.1002/dev.21831] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 10/09/2018] [Accepted: 11/29/2018] [Indexed: 11/10/2022]
Abstract
Rapidly maturing technologies for sensing and activity recognition can provide unprecedented access to the complex structure daily activity and interaction, promising new insight into the mechanisms by which experience shapes developmental outcomes. Motion data, autonomic activity, and "snippets" of audio and video recordings can be conveniently logged by wearable sensors (Lazer et al., 2009). Machine learning algorithms can process these signals into meaningful markers, from child and parent behavior to outcomes such as depression or teenage drinking. Theoretically motivated aspects of daily activity can be combined and synchronized to examine reciprocal effects between children's behaviors and their environments or internal processes. Captured over longitudinal time, such data provide a new opportunity to study the processes by which individual differences emerge and stabilize. This paper introduces the reader to developments in sensing and activity recognition with implications for developmental phenomena across the lifespan, sketching a framework for leveraging mobile sensors for transactional analyses that bridge micro- and longitudinal- timescales of development. It finishes by detailing resources and best practices to facilitate the next generation of developmentalists to contribute to this emerging area.
Collapse
Affiliation(s)
- Kaya de Barbaro
- Department of Psychology, The University of Texas at Austin, Austin, Texas
| |
Collapse
|
26
|
Munasinghe L, Asai Y, Nishiura H. Quantifying heterogeneous contact patterns in Japan: a social contact survey. Theor Biol Med Model 2019; 16:6. [PMID: 30890153 PMCID: PMC6425701 DOI: 10.1186/s12976-019-0102-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 03/05/2019] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Social contact surveys can greatly help in quantifying the heterogeneous patterns of infectious disease transmission. The present study aimed to conduct a contact survey in Japan, offering estimates of contact by age and location and validating a social contact matrix using a seroepidemiological dataset of influenza. METHODS An internet-based questionnaire survey was conducted, covering all 47 prefectures in Japan and including a total of 1476 households. The social contact matrix was quantified assuming reciprocity and using the maximum likelihood method. By imposing several parametric assumptions for the next-generation matrix, the empirical seroepidemiological data of influenza A (H1N1) 2009 was analysed and we estimated the basic reproduction number, R0. RESULTS In total, the reported number of contacts on weekdays was 10,682 whereas that on weekend days was 8867. Strong age-dependent assortativity was identified. Forty percent of weekday contacts took place at schools or workplaces, but that declined to 14% on weekends. Accounting for the age-dependent heterogeneity with the known social contact matrix, the minimum value of the Akaike information criterion was obtained and R0 was estimated at 1.45 (95% confidence interval: 1.42, 1.49). CONCLUSIONS Survey datasets will be useful for parameterizing the heterogeneous transmission model of various directly transmitted infectious diseases in Japan. Age-dependent assortativity, especially among children, along with numerous contacts in school settings on weekdays implies the potential effectiveness of school closure.
Collapse
Affiliation(s)
- Lankeshwara Munasinghe
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Sapporo, Japan
| | - Yusuke Asai
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Sapporo, Japan
| | - Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Sapporo, Japan
| |
Collapse
|
27
|
Peters JS, Andrews JR, Hatherill M, Hermans S, Martinez L, Schurr E, van der Heijden Y, Wood R, Rustomjee R, Kana BD. Advances in the understanding of Mycobacterium tuberculosis transmission in HIV-endemic settings. THE LANCET. INFECTIOUS DISEASES 2019; 19:e65-e76. [PMID: 30554995 PMCID: PMC6401310 DOI: 10.1016/s1473-3099(18)30477-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Revised: 07/18/2018] [Accepted: 07/19/2018] [Indexed: 12/28/2022]
Abstract
Tuberculosis claims more human lives than any other infectious disease. This alarming epidemic has fuelled the development of novel antimicrobials and diagnostics. However, public health interventions that interrupt transmission have been slow to emerge, particularly in HIV-endemic settings. Transmission of tuberculosis is complex, involving various environmental, bacteriological, and host factors, among which concomitant HIV infection is important. Preventing person-to-person spread is central to halting the epidemic and, consequently, tuberculosis transmission is now being studied with renewed interest. In this Series paper, we review recent advances in the understanding of tuberculosis transmission, from the view of source-case infectiousness, inherent susceptibility of exposed individuals, appending tools for predicting risk of disease progression, the biophysical nature of the contagion, and the environments in which transmission occurs and is sustained in populations. We focus specifically on how HIV infection affects these features with a view to describing novel transmission blocking strategies in HIV-endemic settings.
Collapse
Affiliation(s)
- Julian S Peters
- Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand and the National Health Laboratory Service, Johannesburg, South Africa
| | - Jason R Andrews
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Sabine Hermans
- Desmond Tutu HIV Centre, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa; Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Leonardo Martinez
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Erwin Schurr
- Research Institute of the McGill University Health Centre, Montréal, QC, Canada
| | - Yuri van der Heijden
- Vanderbilt Tuberculosis Center and Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Robin Wood
- Desmond Tutu HIV Centre, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Roxana Rustomjee
- Tuberculosis Clinical Research Branch, Therapeutic Research Program, Division of AIDS National Institute of Allergy and Infectious Diseases, National Institutes of Health, North Bethesda, MD, USA
| | - Bavesh D Kana
- Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand and the National Health Laboratory Service, Johannesburg, South Africa; South African Medical Research Council HIV-TB Pathogenesis and Treatment Research Unit, Centre for the AIDS Programme of Research in South Africa, Durban, South Africa.
| |
Collapse
|
28
|
Dawson DE, Farthing TS, Sanderson MW, Lanzas C. Transmission on empirical dynamic contact networks is influenced by data processing decisions. Epidemics 2019; 26:32-42. [PMID: 30528207 PMCID: PMC6613374 DOI: 10.1016/j.epidem.2018.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 08/01/2018] [Accepted: 08/27/2018] [Indexed: 11/02/2022] Open
Abstract
Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R0, time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.
Collapse
Affiliation(s)
- Daniel E Dawson
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA.
| | - Trevor S Farthing
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA
| | - Michael W Sanderson
- Center for Outcomes Research and Epidemiology, Department of Diagnostic Medicine and Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA
| | - Cristina Lanzas
- Department of Pathobiology and Population Health, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, 27606, USA
| |
Collapse
|
29
|
Milwid RM, O’Sullivan TL, Poljak Z, Laskowski M, Greer AL. Validation of modified radio-frequency identification tag firmware, using an equine population case study. PLoS One 2019; 14:e0210148. [PMID: 30625195 PMCID: PMC6326514 DOI: 10.1371/journal.pone.0210148] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 12/18/2018] [Indexed: 11/21/2022] Open
Abstract
Background Contact networks can be used to assess disease spread potential within a population. However, the data required to generate the networks can be challenging to collect. One method of collecting this type of data is by using radio-frequency identification (RFID) tags. The OpenBeacon RFID system generally consists of tags and readers. Communicating tags should be within 10m of the readers, which are powered by an external power source. The readers are challenging to implement in agricultural settings due to the lack of a power source and the large area needed to be covered. Methods OpenBeacon firmware was modified to use the tag’s onboard flash memory for data storage. The tags were deployed within an equine facility for a 7-day period. Tags were attached to the horses’ halters, worn by facility staff, and placed in strategic locations around the facility to monitor which participants had contact with the specified locations during the study period. When the tags came within 2m of each other, they recorded the contact event participant IDs, and start and end times. At the end of the study period, the data were downloaded to a computer and analyzed using network analysis methods. Results The resulting networks were plausible given the facility schedule as described in a survey completed by the facility manager. Furthermore, changes in the daily facility operations as described in the survey were reflected in the tag-collected data. In terms of the battery life, 88% of batteries maintained a charge for at least 6 days. Lastly, no consistent trends were evident in the horses’ centrality metrics. Discussion This study demonstrates the utility of RFID tags for the collection of equine contact data. Future work should include the collection of contact data from multiple equine facilities to better characterize equine disease spread potential in Ontario.
Collapse
Affiliation(s)
- Rachael M. Milwid
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada
| | | | - Zvonimir Poljak
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada
| | - Marek Laskowski
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada
- Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Amy L. Greer
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada
- * E-mail:
| |
Collapse
|
30
|
Moinet A, Pastor-Satorras R, Barrat A. Effect of risk perception on epidemic spreading in temporal networks. Phys Rev E 2018; 97:012313. [PMID: 29448478 PMCID: PMC7217520 DOI: 10.1103/physreve.97.012313] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Indexed: 11/07/2022]
Abstract
Many progresses in the understanding of epidemic spreading models have been obtained thanks to numerous modeling efforts and analytical and numerical studies, considering host populations with very different structures and properties, including complex and temporal interaction networks. Moreover, a number of recent studies have started to go beyond the assumption of an absence of coupling between the spread of a disease and the structure of the contacts on which it unfolds. Models including awareness of the spread have been proposed, to mimic possible precautionary measures taken by individuals that decrease their risk of infection, but have mostly considered static networks. Here, we adapt such a framework to the more realistic case of temporal networks of interactions between individuals. We study the resulting model by analytical and numerical means on both simple models of temporal networks and empirical time-resolved contact data. Analytical results show that the epidemic threshold is not affected by the awareness but that the prevalence can be significantly decreased. Numerical studies on synthetic temporal networks highlight, however, the presence of very strong finite-size effects, resulting in a significant shift of the effective epidemic threshold in the presence of risk awareness. For empirical contact networks, the awareness mechanism leads as well to a shift in the effective threshold and to a strong reduction of the epidemic prevalence.
Collapse
Affiliation(s)
- Antoine Moinet
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France.,Departament de Fisica, Universitat Politecnica de Catalunya, Campus Nord B4, 08034 Barcelona, Spain
| | - Romualdo Pastor-Satorras
- Departament de Fisica, Universitat Politecnica de Catalunya, Campus Nord B4, 08034 Barcelona, Spain
| | - Alain Barrat
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France.,Data Science Laboratory, ISI Foundation, Torino, Italy
| |
Collapse
|
31
|
Ozella L, Gesualdo F, Tizzoni M, Rizzo C, Pandolfi E, Campagna I, Tozzi AE, Cattuto C. Close encounters between infants and household members measured through wearable proximity sensors. PLoS One 2018; 13:e0198733. [PMID: 29879196 PMCID: PMC5991752 DOI: 10.1371/journal.pone.0198733] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 05/24/2018] [Indexed: 11/25/2022] Open
Abstract
Describing and understanding close proximity interactions between infant and family members can provide key information on transmission opportunities of respiratory infections within households. Among respiratory infections, pertussis represents a public health priority. Pertussis infection can be particularly harmful to young, unvaccinated infants and for these patients, family members represent the main sources of transmission. Here, we report on the use of wearable proximity sensors based on RFID technology to measure face-to-face proximity between family members within 16 households with infants younger than 6 months for 2–5 consecutive days of data collection. The sensors were deployed over the course of approximately 1 year, in the context of a national research project aimed at the improvement of infant pertussis prevention strategies. We investigated differences in close-range interactions between family members and we assessed whether demographic variables or feeding practices affect contact patterns between parents and infants. A total of 5,958 contact events were recorded between 55 individuals: 16 infants, 4 siblings, 31 parents and 4 grandparents. The aggregated contact networks, obtained for each household, showed a heterogeneous distribution of the cumulative time spent in proximity with the infant by family members. Contact matrices defined by age and by family role showed that most of the contacts occurred between the infant and other family members (70%), while 30% of contacts was among family members (infants excluded). Many contacts were observed between infants and adults, in particular between infant and mother, followed by father, siblings and grandparents. A larger number of contacts and longer contact durations between infant and other family members were observed in families adopting exclusive breastfeeding, compared to families in which the infant receives artificial or mixed feeding. Our results demonstrate how a high-resolution measurement of contact matrices within infants’ households is feasible using wearable proximity sensing devices. Moreover, our findings suggest the mother is responsible for the large majority of the infant’s contact pattern, thus being the main potential source of infection for a transmissible disease. As the contribution to the infants’ contact pattern by other family members is very variable, vaccination against pertussis during pregnancy is probably the best strategy to protect young, unvaccinated infants.
Collapse
Affiliation(s)
- Laura Ozella
- Data Science Laboratory, ISI Foundation, Turin, Italy
- * E-mail:
| | - Francesco Gesualdo
- Innovation and Clinical Pathways Unit, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | | | - Caterina Rizzo
- Department of Infectious Disease, Istituto Superiore di Sanità, Rome, Italy
| | - Elisabetta Pandolfi
- Innovation and Clinical Pathways Unit, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Ilaria Campagna
- Innovation and Clinical Pathways Unit, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Alberto Eugenio Tozzi
- Innovation and Clinical Pathways Unit, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Ciro Cattuto
- Data Science Laboratory, ISI Foundation, Turin, Italy
| |
Collapse
|
32
|
Silk MJ, Weber NL, Steward LC, Hodgson DJ, Boots M, Croft DP, Delahay RJ, McDonald RA. Contact networks structured by sex underpin sex-specific epidemiology of infection. Ecol Lett 2018; 21:309-318. [PMID: 29266710 PMCID: PMC6849844 DOI: 10.1111/ele.12898] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 08/23/2017] [Accepted: 11/16/2017] [Indexed: 01/04/2023]
Abstract
Contact networks are fundamental to the transmission of infection and host sex often affects the acquisition and progression of infection. However, the epidemiological impacts of sex-related variation in animal contact networks have rarely been investigated. We test the hypothesis that sex-biases in infection are related to variation in multilayer contact networks structured by sex in a population of European badgers Meles meles naturally infected with Mycobacterium bovis. Our key results are that male-male and between-sex networks are structured at broader spatial scales than female-female networks and that in male-male and between-sex contact networks, but not female-female networks, there is a significant relationship between infection and contacts with individuals in other groups. These sex differences in social behaviour may underpin male-biased acquisition of infection and may result in males being responsible for more between-group transmission. This highlights the importance of sex-related variation in host behaviour when managing animal diseases.
Collapse
Affiliation(s)
- Matthew J. Silk
- Environment and Sustainability InstituteUniversity of ExeterPenrynTR10 9FEUK
| | - Nicola L. Weber
- Centre for Ecology and ConservationUniversity of ExeterPenrynTR10 9FEUK
| | - Lucy C. Steward
- Environment and Sustainability InstituteUniversity of ExeterPenrynTR10 9FEUK
| | - David J. Hodgson
- Centre for Ecology and ConservationUniversity of ExeterPenrynTR10 9FEUK
| | - Mike Boots
- Centre for Ecology and ConservationUniversity of ExeterPenrynTR10 9FEUK
- Department of Integrative BiologyUniversity of California, Berkeley3040 Valley Life Sciences BuildingBerkeleyCA94720USA
| | - Darren P. Croft
- Centre for Research in Animal BehaviourUniversity of ExeterExeterEX4 4QGUK
| | - Richard J. Delahay
- National Wildlife Management Centre, Animal and Plant Health AgencyWoodchester ParkNympsfield, StonehouseGL10 3UJUK
| | - Robbie A. McDonald
- Environment and Sustainability InstituteUniversity of ExeterPenrynTR10 9FEUK
| |
Collapse
|
33
|
Yoo JH. Principle and perspective of healthcare-associated infection control. JOURNAL OF THE KOREAN MEDICAL ASSOCIATION 2018. [DOI: 10.5124/jkma.2018.61.1.5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Jin-Hong Yoo
- Division of Infectious Diseases, Department of Internal Medicine, Bucheon St. Mary's Hospital, The Catholic University of Korea College of Medicine, Bucheon, Korea
| |
Collapse
|
34
|
Petit J, Lauwens B, Fanelli D, Carletti T. Theory of Turing Patterns on Time Varying Networks. PHYSICAL REVIEW LETTERS 2017; 119:148301. [PMID: 29053314 DOI: 10.1103/physrevlett.119.148301] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Indexed: 06/07/2023]
Abstract
The process of pattern formation for a multispecies model anchored on a time varying network is studied. A nonhomogeneous perturbation superposed to an homogeneous stable fixed point can be amplified following the Turing mechanism of instability, solely instigated by the network dynamics. By properly tuning the frequency of the imposed network evolution, one can make the examined system behave as its averaged counterpart, over a finite time window. This is the key observation to derive a closed analytical prediction for the onset of the instability in the time dependent framework. Continuously and piecewise constant periodic time varying networks are analyzed, setting the framework for the proposed approach. The extension to nonperiodic settings is also discussed.
Collapse
Affiliation(s)
- Julien Petit
- naXys, Namur Institute for Complex Systems, University of Namur, B5000 Namur, Belgium
- Department of Mathematics, Royal Military Academy, B1000 Brussels, Belgium
| | - Ben Lauwens
- Department of Mathematics, Royal Military Academy, B1000 Brussels, Belgium
| | - Duccio Fanelli
- Dipartimento di Fisica e Astronomia and CSDC, Università degli Studi di Firenze, 50019 Sesto Fiorentino, Italy
- INFN Sezione di Firenze, 50019 Sesto Fiorentino, Italy
| | - Timoteo Carletti
- naXys, Namur Institute for Complex Systems, University of Namur, B5000 Namur, Belgium
| |
Collapse
|
35
|
Measuring distance through dense weighted networks: The case of hospital-associated pathogens. PLoS Comput Biol 2017; 13:e1005622. [PMID: 28771581 PMCID: PMC5542422 DOI: 10.1371/journal.pcbi.1005622] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/13/2017] [Indexed: 12/02/2022] Open
Abstract
Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult. Shared patients can spread hospital-associated pathogens between hospitals, together forming a large network in which all hospitals are connected. We set out to measure the distance between hospitals in such a network, best reflecting the risk of a hospital-associated pathogen spreading from one to the other. The central problem is that this risk may not be a directly reflected by the weight of the direct connections between hospitals, because the pathogen could arrive through a longer indirect route, first causing a problem in an intermediate hospital. We determined the optimal balance between connection weights and path length, by testing different weighting factors between them against simulated spread of a pathogen. We found that while strong connections are important risk factor for a hospital’s direct neighbours, weak connections offer ideal indirect routes for hospital-associated pathogens to travel further faster. These routes should not be underestimated when designing control strategies.
Collapse
|
36
|
Abstract
Social network analysis has become a prominent tool to study animal social life, and there is an increasing need to develop new systems to collect social information automatically, systematically, and reliably. Here we explore the use of a freely accessible Automated Learning Device for Monkeys (ALDM) to collect such social information on a group of 22 captive baboons (Papio papio). We compared the social network obtained from the co-presence of the baboons in ten ALDM testing booths to the social network obtained through standard behavioral observation techniques. The results show that the co-presence network accurately reflects the social organization of the group, and also indicate under which conditions the co-presence network is most informative. In particular, the best correlation between the two networks was obtained with a minimum of 40 days of computer records and for individuals with at least 500 records per day. We also show through random permutation tests that the observed correlations go beyond what would be observed by simple synchronous activity, to reflect a preferential choice of closely located testing booths. The use of automatized cognitive testing therefore presents a new way of obtaining a large and regular amount of social information that is necessary to develop social network analysis. It also opens the possibility of studying dynamic changes in network structure with time and in relation to the cognitive performance of individuals.
Collapse
|
37
|
Predicting epidemic evolution on contact networks from partial observations. PLoS One 2017; 12:e0176376. [PMID: 28445537 PMCID: PMC5405999 DOI: 10.1371/journal.pone.0176376] [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: 08/21/2016] [Accepted: 04/10/2017] [Indexed: 11/19/2022] Open
Abstract
The massive employment of computational models in network epidemiology calls for the development of improved inference methods for epidemic forecast. For simple compartment models, such as the Susceptible-Infected-Recovered model, Belief Propagation was proved to be a reliable and efficient method to identify the origin of an observed epidemics. Here we show that the same method can be applied to predict the future evolution of an epidemic outbreak from partial observations at the early stage of the dynamics. The results obtained using Belief Propagation are compared with Monte Carlo direct sampling in the case of SIR model on random (regular and power-law) graphs for different observation methods and on an example of real-world contact network. Belief Propagation gives in general a better prediction that direct sampling, although the quality of the prediction depends on the quantity under study (e.g. marginals of individual states, epidemic size, extinction-time distribution) and on the actual number of observed nodes that are infected before the observation time.
Collapse
|
38
|
“Contextualizing Context”: Reconciling Environmental Exposures, Social Networks, and Location Preferences in Health Research. Curr Environ Health Rep 2017; 4:51-60. [DOI: 10.1007/s40572-017-0121-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
39
|
Smieszek T, Castell S, Barrat A, Cattuto C, White PJ, Krause G. Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes. BMC Infect Dis 2016. [PMID: 27449511 DOI: 10.1186/s12879-016-1676-y/figures/3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
BACKGROUND Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement - paper diaries vs. wearable proximity sensors - that were applied concurrently to the same population, and we measured acceptability. METHODS We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability. RESULTS There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants' aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors. CONCLUSION Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for.
Collapse
Affiliation(s)
- Timo Smieszek
- NIHR Health Protection Research Unit in Modelling Methodology and MRC Outbreak Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
- Modelling and Economics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, UK
| | - Stefanie Castell
- Department for Epidemiology, Helmholtz-Centre for Infection Research, Braunschweig, Germany.
| | - Alain Barrat
- Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, Marseille, 13288, France
- Data Science Laboratory, ISI Foundation, Torino, Italy
| | - Ciro Cattuto
- Data Science Laboratory, ISI Foundation, Torino, Italy
| | - Peter J White
- NIHR Health Protection Research Unit in Modelling Methodology and MRC Outbreak Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
- Modelling and Economics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, UK
| | - Gérard Krause
- Department for Epidemiology, Helmholtz-Centre for Infection Research, Braunschweig, Germany
- Hannover Medical School, Hannover, Germany
| |
Collapse
|
40
|
Smieszek T, Castell S, Barrat A, Cattuto C, White PJ, Krause G. Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes. BMC Infect Dis 2016; 16:341. [PMID: 27449511 PMCID: PMC4957345 DOI: 10.1186/s12879-016-1676-y] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 06/10/2016] [Indexed: 11/27/2022] Open
Abstract
Background Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement – paper diaries vs. wearable proximity sensors – that were applied concurrently to the same population, and we measured acceptability. Methods We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability. Results There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants’ aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors. Conclusion Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1676-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Timo Smieszek
- NIHR Health Protection Research Unit in Modelling Methodology and MRC Outbreak Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Modelling and Economics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, UK
| | - Stefanie Castell
- Department for Epidemiology, Helmholtz-Centre for Infection Research, Braunschweig, Germany.
| | - Alain Barrat
- Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, Marseille, 13288, France.,Data Science Laboratory, ISI Foundation, Torino, Italy
| | - Ciro Cattuto
- Data Science Laboratory, ISI Foundation, Torino, Italy
| | - Peter J White
- NIHR Health Protection Research Unit in Modelling Methodology and MRC Outbreak Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Modelling and Economics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, UK
| | - Gérard Krause
- Department for Epidemiology, Helmholtz-Centre for Infection Research, Braunschweig, Germany.,Hannover Medical School, Hannover, Germany
| |
Collapse
|
41
|
Kiti MC, Tizzoni M, Kinyanjui TM, Koech DC, Munywoki PK, Meriac M, Cappa L, Panisson A, Barrat A, Cattuto C, Nokes DJ. Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors. EPJ DATA SCIENCE 2016; 5:21. [PMID: 27471661 PMCID: PMC4944592 DOI: 10.1140/epjds/s13688-016-0084-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Accepted: 06/06/2016] [Indexed: 06/06/2023]
Abstract
UNLABELLED Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the developing country setting have not been investigated. We present and analyse data arising from a prospective study of 5 households in rural Kenya, followed through 3 consecutive days. Pre-study focus group discussions with key community groups were held. All residents of selected households carried wearable proximity sensors to collect data on their close (<1.5 metres) interactions. Data collection for residents of three of the 5 households was contemporaneous. Contact matrices and temporal networks for 75 individuals are defined and mixing patterns by age and time of day in household contacts determined. Our study demonstrates the stability of numbers and durations of contacts across days. The contact durations followed a broad distribution consistent with data from other settings. Contacts within households occur mainly among children and between children and adults, and are characterised by daily regular peaks in the morning, midday and evening. Inter-household contacts are between adults and more sporadic when measured over several days. Community feedback indicated privacy as a major concern especially regarding perceptions of non-participants, and that community acceptability required thorough explanation of study tools and procedures. Our results show for a low resource setting how wearable proximity sensors can be used to objectively collect high-resolution temporal data without direct supervision. The methodology appears acceptable in this population following adequate community engagement on study procedures. A target for future investigation is to determine the difference in contact networks within versus between households. We suggest that the results from this study may be used in the design of future studies using similar electronic devices targeting communities, including households and schools, in the developing world context. ELECTRONIC SUPPLEMENTARY MATERIAL The online version of this article (doi:10.1140/epjds/s13688-016-0084-2) contains supplementary material.
Collapse
Affiliation(s)
- Moses C Kiti
- />KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Michele Tizzoni
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - Timothy M Kinyanjui
- />KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya
- />School of Mathematics, The University of Manchester, Manchester, UK
| | | | | | | | - Luca Cappa
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - André Panisson
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - Alain Barrat
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
- />Aix-Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, Marseille, 13288 France
| | - Ciro Cattuto
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - D James Nokes
- />KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya
- />School of Life Sciences and WIDER, University of Warwick, Coventry, UK
| |
Collapse
|
42
|
Génois M, Vestergaard C, Barrat A. Évaluer le risque épidémique à partir de données lacunaires. Med Sci (Paris) 2016; 32:506-8. [DOI: 10.1051/medsci/20163205019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
|
43
|
Brouqui P. Should we provide acute care in single or double room occupancy? Clin Microbiol Infect 2016; 22:402. [PMID: 26850827 DOI: 10.1016/j.cmi.2016.01.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 01/25/2016] [Indexed: 11/26/2022]
Affiliation(s)
- P Brouqui
- Institut Hospitalo-universitaire Mediterranée Infection, Marseille, France.
| |
Collapse
|
44
|
Combining high-resolution contact data with virological data to investigate influenza transmission in a tertiary care hospital. Infect Control Hosp Epidemiol 2015; 36:254-60. [PMID: 25695165 DOI: 10.1017/ice.2014.53] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Contact patterns and microbiological data contribute to a detailed understanding of infectious disease transmission. We explored the automated collection of high-resolution contact data by wearable sensors combined with virological data to investigate influenza transmission among patients and healthcare workers in a geriatric unit. DESIGN Proof-of-concept observational study. Detailed information on contact patterns were collected by wearable sensors over 12 days. Systematic nasopharyngeal swabs were taken, analyzed for influenza A and B viruses by real-time polymerase chain reaction, and cultured for phylogenetic analysis. SETTING An acute-care geriatric unit in a tertiary care hospital. PARTICIPANTS Patients, nurses, and medical doctors. RESULTS A total of 18,765 contacts were recorded among 37 patients, 32 nurses, and 15 medical doctors. Most contacts occurred between nurses or between a nurse and a patient. Fifteen individuals had influenza A (H3N2). Among these, 11 study participants were positive at the beginning of the study or at admission, and 3 patients and 1 nurse acquired laboratory-confirmed influenza during the study. Infectious medical doctors and nurses were identified as potential sources of hospital-acquired influenza (HA-Flu) for patients, and infectious patients were identified as likely sources for nurses. Only 1 potential transmission between nurses was observed. CONCLUSIONS Combining high-resolution contact data and virological data allowed us to identify a potential transmission route in each possible case of HA-Flu. This promising method should be applied for longer periods in larger populations, with more complete use of phylogenetic analyses, for a better understanding of influenza transmission dynamics in a hospital setting.
Collapse
|
45
|
Stein ML, van der Heijden PGM, Buskens V, van Steenbergen JE, Bengtsson L, Koppeschaar CE, Thorson A, Kretzschmar MEE. Tracking social contact networks with online respondent-driven detection: who recruits whom? BMC Infect Dis 2015; 15:522. [PMID: 26573658 PMCID: PMC4647802 DOI: 10.1186/s12879-015-1250-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 10/28/2015] [Indexed: 01/13/2023] Open
Abstract
Background Transmission of respiratory pathogens in a population depends on the contact network patterns of individuals. To accurately understand and explain epidemic behaviour information on contact networks is required, but only limited empirical data is available. Online respondent-driven detection can provide relevant epidemiological data on numbers of contact persons and dynamics of contacts between pairs of individuals. We aimed to analyse contact networks with respect to sociodemographic and geographical characteristics, vaccine-induced immunity and self-reported symptoms. Methods In 2014, volunteers from two large participatory surveillance panels in the Netherlands and Belgium were invited for a survey. Participants were asked to record numbers of contacts at different locations and self-reported influenza-like-illness symptoms, and to invite 4 individuals they had met face to face in the preceding 2 weeks. We calculated correlations between linked individuals to investigate mixing patterns. Results In total 1560 individuals completed the survey who reported in total 30591 contact persons; 488 recruiter-recruit pairs were analysed. Recruitment was assortative by age, education, household size, influenza vaccination status and sentiments, indicating that participants tended to recruit contact persons similar to themselves. We also found assortative recruitment by symptoms, reaffirming our objective of sampling contact persons whom a participant may infect or by whom a participant may get infected in case of an outbreak. Recruitment was random by sex and numbers of contact persons. Relationships between pairs were influenced by the spatial distribution of peer recruitment. Conclusions Although complex mechanisms influence online peer recruitment, the observed statistical relationships reflected the observed contact network patterns in the general population relevant for the transmission of respiratory pathogens. This provides useful and innovative input for predictive epidemic models relying on network information. Electronic supplementary material The online version of this article (doi:10.1186/s12879-015-1250-z) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Mart L Stein
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. .,Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
| | - Peter G M van der Heijden
- Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, University Utrecht, Utrecht, The Netherlands. .,Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK.
| | - Vincent Buskens
- Department of Sociology, Faculty of Social and Behavioural Sciences, University Utrecht, Utrecht, The Netherlands.
| | - Jim E van Steenbergen
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands. .,Centre of Infectious Diseases, Leiden University Medical Centre, Leiden, The Netherlands.
| | - Linus Bengtsson
- Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden. .,Flowminder Foundation, Stockholm, Sweden.
| | | | - Anna Thorson
- Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden.
| | - Mirjam E E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. .,Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
| |
Collapse
|
46
|
Mastrandrea R, Fournet J, Barrat A. Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys. PLoS One 2015; 10:e0136497. [PMID: 26325289 PMCID: PMC4556655 DOI: 10.1371/journal.pone.0136497] [Citation(s) in RCA: 144] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 08/05/2015] [Indexed: 12/02/2022] Open
Abstract
Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and of friendships, and we investigate the correlations between the number of neighbors of individuals in the three networks. Overall, diaries and surveys tend to yield a correct picture of the global structural organization of the contact network, albeit with much less links, and give access to a sort of backbone of the contact network corresponding to the strongest links, i.e., the contacts of longest cumulative durations.
Collapse
Affiliation(s)
- Rossana Mastrandrea
- Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France
| | - Julie Fournet
- Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France
| | - Alain Barrat
- Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France
- Data Science Laboratory, ISI Foundation, Torino, Italy
- * E-mail:
| |
Collapse
|
47
|
Dixon BE, Kharrazi H, Lehmann HP. Public Health and Epidemiology Informatics: Recent Research and Trends in the United States. Yearb Med Inform 2015; 10:199-206. [PMID: 26293869 PMCID: PMC4587030 DOI: 10.15265/iy-2015-012] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVES To survey advances in public health and epidemiology informatics over the past three years. METHODS We conducted a review of English-language research works conducted in the domain of public health informatics (PHI), and published in MEDLINE between January 2012 and December 2014, where information and communication technology (ICT) was a primary subject, or a main component of the study methodology. Selected articles were synthesized using a thematic analysis using the Essential Services of Public Health as a typology. RESULTS Based on themes that emerged, we organized the advances into a model where applications that support the Essential Services are, in turn, supported by a socio-technical infrastructure that relies on government policies and ethical principles. That infrastructure, in turn, depends upon education and training of the public health workforce, development that creates novel or adapts existing infrastructure, and research that evaluates the success of the infrastructure. Finally, the persistence and growth of infrastructure depends on financial sustainability. CONCLUSIONS Public health informatics is a field that is growing in breadth, depth, and complexity. Several Essential Services have benefited from informatics, notably, "Monitor Health," "Diagnose & Investigate," and "Evaluate." Yet many Essential Services still have not yet benefited from advances such as maturing electronic health record systems, interoperability amongst health information systems, analytics for population health management, use of social media among consumers, and educational certification in clinical informatics. There is much work to be done to further advance the science of PHI as well as its impact on public health practice.
Collapse
Affiliation(s)
| | | | - H P Lehmann
- Harold Lehmann, 2024 E Monument St, Baltimore MD 21209, Tel. +1 410 502 7569, E-mail:
| |
Collapse
|
48
|
Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. ACTA ACUST UNITED AC 2015. [DOI: 10.1017/nws.2015.10] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
AbstractEmpirical data on contacts between individuals in social contexts play an important role in providing information for models describing human behavior and how epidemics spread in populations. Here, we analyze data on face-to-face contacts collected in an office building. The statistical properties of contacts are similar to other social situations, but important differences are observed in the contact network structure. In particular, the contact network is strongly shaped by the organization of the offices in departments, which has consequences in the design of accurate agent-based models of epidemic spread. We consider the contact network as a potential substrate for infectious disease spread and show that its sparsity tends to prevent outbreaks of rapidly spreading epidemics. Moreover, we define three typical behaviors according to the fraction f of links each individual shares outside its own department: residents, wanderers, and linkers. Linkers (f ~ 50%) act as bridges in the network and have large betweenness centralities. Thus, a vaccination strategy targeting linkers efficiently prevents large outbreaks. As such a behavior may be spotted a priori in the offices' organization or from surveys, without the full knowledge of the time-resolved contact network, this result may help the design of efficient, low-cost vaccination or social-distancing strategies.
Collapse
|
49
|
Pautasso M, Jeger MJ. Network epidemiology and plant trade networks. AOB PLANTS 2014; 6:plu007. [PMID: 24790128 PMCID: PMC4038442 DOI: 10.1093/aobpla/plu007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 02/11/2014] [Indexed: 05/29/2023]
Abstract
Models of epidemics in complex networks are improving our predictive understanding of infectious disease outbreaks. Nonetheless, applying network theory to plant pathology is still a challenge. This overview summarizes some key developments in network epidemiology that are likely to facilitate its application in the study and management of plant diseases. Recent surveys have provided much-needed datasets on contact patterns and human mobility in social networks, but plant trade networks are still understudied. Human (and plant) mobility levels across the planet are unprecedented-there is thus much potential in the use of network theory by plant health authorities and researchers. Given the directed and hierarchical nature of plant trade networks, there is a need for plant epidemiologists to further develop models based on undirected and homogeneous networks. More realistic plant health scenarios would also be obtained by developing epidemic models in dynamic, rather than static, networks. For plant diseases spread by the horticultural and ornamental trade, there is the challenge of developing spatio-temporal epidemic simulations integrating network data. The use of network theory in plant epidemiology is a promising avenue and could contribute to anticipating and preventing plant health emergencies such as European ash dieback.
Collapse
Affiliation(s)
- Marco Pautasso
- Forest Pathology and Dendrology, Institute of Integrative Biology, ETHZ, Zurich, Switzerland
| | - Mike J. Jeger
- Division of Ecology and Evolution & Centre for Environmental Policy, Imperial College London, London, UK
| |
Collapse
|
50
|
Brouqui P. New technologies to trace infection. Clin Microbiol Infect 2014; 20:8-9. [DOI: 10.1111/1469-0691.12467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|