1
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Badalyan A, Ruggeri N, De Bacco C. Structure and inference in hypergraphs with node attributes. Nat Commun 2024; 15:7073. [PMID: 39152121 PMCID: PMC11329712 DOI: 10.1038/s41467-024-51388-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 08/06/2024] [Indexed: 08/19/2024] Open
Abstract
Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace. Here we show how these node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We consider the problem of community detection in hypergraphs and develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions and to detect communities more accurately than using either of these types of information alone. The method learns automatically from the input data the extent to which structure and attributes contribute to explain the data, down weighing or discarding attributes if not informative. Our algorithmic implementation is efficient and scales to large hypergraphs and interactions of large numbers of units. We apply our method to a variety of systems, showing strong performance in hyperedge prediction tasks and in selecting community divisions that correlate with attributes when these are informative, but discarding them otherwise. Our approach illustrates the advantage of using informative node attributes when available with higher-order data.
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Affiliation(s)
- Anna Badalyan
- Max Planck Institute for Intelligent Systems, Cyber Valley, Tübingen, Germany
| | - Nicolò Ruggeri
- Max Planck Institute for Intelligent Systems, Cyber Valley, Tübingen, Germany.
- Department of Computer Science, ETH, Zürich, Switzerland.
| | - Caterina De Bacco
- Max Planck Institute for Intelligent Systems, Cyber Valley, Tübingen, Germany.
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2
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Leclerc QJ, Duval A, Guillemot D, Opatowski L, Temime L. Using contact network dynamics to implement efficient interventions against pathogen spread in hospital settings: A modelling study. PLoS Med 2024; 21:e1004433. [PMID: 39078828 PMCID: PMC11341093 DOI: 10.1371/journal.pmed.1004433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 08/22/2024] [Accepted: 06/24/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND Long-term care facilities (LTCFs) are hotspots for pathogen transmission. Infection control interventions are essential, but the high density and heterogeneity of interindividual contacts within LTCF may hinder their efficacy. Here, we explore how the patient-staff contact structure may inform effective intervention implementation. METHODS AND FINDINGS Using an individual-based model (IBM), we reproduced methicillin-resistant Staphylococcus aureus colonisation transmission dynamics over a detailed contact network recorded within a French LTCF of 327 patients and 263 staff over 3 months. Simulated baseline cumulative colonisation incidence was 21 patients (prediction interval: 11, 31) and 35 staff (prediction interval: 19, 54). We examined the potential impact of 3 types of interventions against transmission (reallocation reducing the number of unique contacts per staff, reinforced contact precautions, and hypothetical vaccination protecting against acquisition), targeted towards specific populations. All 3 interventions were effective when applied to all nurses or healthcare assistants (median reduction in MRSA colonisation incidence up to 35%), but the benefit did not exceed 8% when targeting any other single staff category. We identified "supercontactor" individuals with most contacts ("frequency-based," overrepresented among nurses, porters, and rehabilitation staff) or with the longest cumulative time spent in contact ("duration-based," overrepresented among healthcare assistants and patients in elderly care or persistent vegetative state (PVS)). Targeting supercontactors enhanced interventions against pathogen spread in the LTCF. With contact precautions, targeting frequency-based staff supercontactors led to the highest incidence reduction (20%, 95% CI: 19, 21). Vaccinating a mix of frequency- and duration-based staff supercontactors led to a higher reduction (23%, 95% CI: 22, 24) than all other approaches. Although based on data from a single LTCF, when varying epidemiological parameters to extend to other pathogens, our results suggest that targeting supercontactors is always the most effective strategy, indicating this approach could be applied to prevent transmission of other nosocomial pathogens. CONCLUSIONS By characterising the contact structure in hospital settings and identifying the categories of staff and patients more likely to be supercontactors, with either more or longer contacts than others, interventions against nosocomial spread could be more effective. We find that the most efficient implementation strategy depends on the intervention (reallocation, contact precautions, vaccination) and target population (staff, patients, supercontactors). Importantly, both staff and patients may be supercontactors, highlighting the importance of including patients in measures to prevent pathogen transmission in LTCF.
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Affiliation(s)
- Quentin J. Leclerc
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires, Conservatoire National des Arts et Métiers, Paris, France
| | - Audrey Duval
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires, Conservatoire National des Arts et Métiers, Paris, France
| | - Didier Guillemot
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- AP-HP, Paris Saclay, Department of Public Health, Medical Information, Clinical Research, Garches, France
| | - Lulla Opatowski
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
| | - Laura Temime
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires, Conservatoire National des Arts et Métiers, Paris, France
- Institut Pasteur, Conservatoire National des Arts et Métiers, Unité PACRI, Paris, France
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3
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Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Giovanni Maldonado Briones H, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Augusto Joaquim O, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: A mixed methods research protocol. PLoS One 2024; 19:e0301638. [PMID: 38913670 PMCID: PMC11195963 DOI: 10.1371/journal.pone.0301638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 03/15/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. METHODS To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. DISCUSSION Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
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Affiliation(s)
- Obianuju Genevieve Aguolu
- Division of Epidemiology, College of Public Heath, The Ohio State University, Columbus, Ohio, United States of America
| | - Moses Chapa Kiti
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Kristin Nelson
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic–Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Maputo, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | | | | | | | - Gifta John
- Christian Medical College Vellore, Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Sara Kim
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Holin Chen
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
| | - Inci Yildirim
- Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas, United States of America
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Zou Y, Peng X, Yang W, Zhang J, Lin W. Dynamics of simplicial SEIRS epidemic model: global asymptotic stability and neural Lyapunov functions. J Math Biol 2024; 89:12. [PMID: 38879853 DOI: 10.1007/s00285-024-02119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/17/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024]
Abstract
The transmission of infectious diseases on a particular network is ubiquitous in the physical world. Here, we investigate the transmission mechanism of infectious diseases with an incubation period using a networked compartment model that contains simplicial interactions, a typical high-order structure. We establish a simplicial SEIRS model and find that the proportion of infected individuals in equilibrium increases due to the many-body connections, regardless of the type of connections used. We analyze the dynamics of the established model, including existence and local asymptotic stability, and highlight differences from existing models. Significantly, we demonstrate global asymptotic stability using the neural Lyapunov function, a machine learning technique, with both numerical simulations and rigorous analytical arguments. We believe that our model owns the potential to provide valuable insights into transmission mechanisms of infectious diseases on high-order network structures, and that our approach and theory of using neural Lyapunov functions to validate model asymptotic stability can significantly advance investigations on complex dynamics of infectious disease.
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Affiliation(s)
- Yukun Zou
- Research Institute of Intelligent Complex Systems, Fudan University, 220 Handan Road, Shanghai, 200433, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Shanghai, 200433, China
| | - Xiaoxiao Peng
- Research Institute of Intelligent Complex Systems, Fudan University, 220 Handan Road, Shanghai, 200433, China
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, 220 Handan Road, Shanghai, 200433, China
| | - Wei Yang
- Research Institute of Intelligent Complex Systems, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| | - Jingdong Zhang
- Research Institute of Intelligent Complex Systems, Fudan University, 220 Handan Road, Shanghai, 200433, China
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, 220 Handan Road, Shanghai, 200433, China
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, 220 Handan Road, Shanghai, 200433, China
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, 220 Handan Road, Shanghai, 200433, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Shanghai, 200433, China
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5
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Gallo L, Lacasa L, Latora V, Battiston F. Higher-order correlations reveal complex memory in temporal hypergraphs. Nat Commun 2024; 15:4754. [PMID: 38834592 DOI: 10.1038/s41467-024-48578-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 05/02/2024] [Indexed: 06/06/2024] Open
Abstract
Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. The analysis of human interaction data reveals the existence of coherent and interdependent mesoscopic structures, thus capturing aggregation, fragmentation and nucleation processes in social systems. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data.
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Affiliation(s)
- Luca Gallo
- Department of Network and Data Science, Central European University, Vienna, Austria.
| | - Lucas Lacasa
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
| | - Vito Latora
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK
- Department of Physics and Astronomy, University of Catania, 95125, Catania, Italy
- INFN Sezione di Catania, Via S. Sofia, 64, 95125, Catania, Italy
- Complexity Science Hub Vienna, A-1080, Vienna, Austria
| | - Federico Battiston
- Department of Network and Data Science, Central European University, Vienna, Austria.
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6
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Chen B, Hou G, Li A. Temporal local clustering coefficient uncovers the hidden pattern in temporal networks. Phys Rev E 2024; 109:064302. [PMID: 39020959 DOI: 10.1103/physreve.109.064302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 05/07/2024] [Indexed: 07/20/2024]
Abstract
Identifying and extracting topological characteristics are essential for understanding associated structures and organizational principles of complex networks. For temporal networks where the network topology varies with time, beyond the classical patterns such as small-worldness and scale-freeness extracted from the perspective of traditional aggregated static networks, the temporality and simultaneity of time-varying interactions should also be included. Here we extend the traditional analysis on the local clustering coefficient C in static networks and study the dynamical local clustering coefficient of temporal networks. We demonstrate that the temporal local clustering coefficient TC conveys the hidden information of nodes' neighboring connectance when interactions occur at various rhythms. By systematically analyzing various empirical datasets, we find that TC uncovers different interaction patterns in different types of temporal networks. Specifically, we show that TC has a strong positive correlation with C in efficiency-related networks, whereas they are uncorrelated in social activity-related networks. Moreover, TC helps to exclude interference from accidental interactions and reflect the actual clustering properties of network nodes. Our results shed light on the importance of digging into dynamical characteristics to fundamentally understand the underlying temporal structures of real complex systems.
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Affiliation(s)
| | - Guyu Hou
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, People's Republic of China
| | - Aming Li
- Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing 100871, People's Republic of China
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7
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Duval A, Leclerc QJ, Guillemot D, Temime L, Opatowski L. An algorithm to build synthetic temporal contact networks based on close-proximity interactions data. PLoS Comput Biol 2024; 20:e1012227. [PMID: 38870216 PMCID: PMC11207132 DOI: 10.1371/journal.pcbi.1012227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 06/26/2024] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical individual contact data provide unprecedented information to characterize such heterogeneity and are increasingly available, but are usually collected over a limited period, and can suffer from observation bias. We propose an algorithm to stochastically reconstruct realistic temporal networks from individual contact data in healthcare settings (HCS) and test this approach using real data previously collected in a long-term care facility (LTCF). Our algorithm generates full networks from recorded close-proximity interactions, using hourly inter-individual contact rates and information on individuals' wards, the categories of staff involved in contacts, and the frequency of recurring contacts. It also provides data augmentation by reconstructing contacts for days when some individuals are present in the HCS without having contacts recorded in the empirical data. Recording bias is formalized through an observation model, to allow direct comparison between the augmented and observed networks. We validate our algorithm using data collected during the i-Bird study, and compare the empirical and reconstructed networks. The algorithm was substantially more accurate to reproduce network characteristics than random graphs. The reconstructed networks reproduced well the assortativity by ward (first-third quartiles observed: 0.54-0.64; synthetic: 0.52-0.64) and the hourly staff and patient contact patterns. Importantly, the observed temporal correlation was also well reproduced (0.39-0.50 vs 0.37-0.44), indicating that our algorithm could recreate a realistic temporal structure. The algorithm consistently recreated unobserved contacts to generate full reconstructed networks for the LTCF. To conclude, we propose an approach to generate realistic temporal contact networks and reconstruct unobserved contacts from summary statistics computed using individual-level interaction networks. This could be applied and extended to generate contact networks to other HCS using limited empirical data, to subsequently inform individual-based epidemic models.
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Affiliation(s)
- Audrey Duval
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires (MESuRS), Conservatoire National des Arts et Métiers, Paris, France
| | - Quentin J. Leclerc
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires (MESuRS), Conservatoire National des Arts et Métiers, Paris, France
| | - Didier Guillemot
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
- AP-HP, Paris Saclay, Department of Public Health, Medical Information, Clinical research, Garches, France
| | - Laura Temime
- Laboratoire Modélisation, Epidémiologie et Surveillance des Risques Sanitaires (MESuRS), Conservatoire National des Arts et Métiers, Paris, France
- Institut Pasteur, Conservatoire National des Arts et Métiers, Unité PACRI, Paris, France
| | - Lulla Opatowski
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Paris, France
- INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018, CESP, Versailles, France
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8
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Contreras DA, Cencetti G, Barrat A. Infection patterns in simple and complex contagion processes on networks. PLoS Comput Biol 2024; 20:e1012206. [PMID: 38857274 PMCID: PMC11192313 DOI: 10.1371/journal.pcbi.1012206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 06/21/2024] [Accepted: 05/28/2024] [Indexed: 06/12/2024] Open
Abstract
Contagion processes, representing the spread of infectious diseases, information, or social behaviors, are often schematized as taking place on networks, which encode for instance the interactions between individuals. The impact of the network structure on spreading process has been widely investigated, but not the reverse question: do different processes unfolding on a given network lead to different infection patterns? How do the infection patterns depend on a model's parameters or on the nature of the contagion processes? Here we address this issue by investigating the infection patterns for a variety of models. In simple contagion processes, where contagion events involve one connection at a time, we find that the infection patterns are extremely robust across models and parameters. In complex contagion models instead, in which multiple interactions are needed for a contagion event, non-trivial dependencies on models parameters emerge, as the infection pattern depends on the interplay between pairwise and group contagions. In models involving threshold mechanisms moreover, slight parameter changes can significantly impact the spreading paths. Our results show that it is possible to study crucial features of a spread from schematized models, and inform us on the variations between spreading patterns in processes of different nature.
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Affiliation(s)
- Diego Andrés Contreras
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Giulia Cencetti
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
- Fondazione Bruno Kessler, Trento, Italy
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
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Dall’Amico L, Kleynhans J, Gauvin L, Tizzoni M, Ozella L, Makhasi M, Wolter N, Language B, Wagner RG, Cohen C, Tempia S, Cattuto C. Estimating household contact matrices structure from easily collectable metadata. PLoS One 2024; 19:e0296810. [PMID: 38483886 PMCID: PMC10939291 DOI: 10.1371/journal.pone.0296810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/18/2023] [Indexed: 03/17/2024] Open
Abstract
Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure.
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Affiliation(s)
| | - Jackie Kleynhans
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Laetitia Gauvin
- ISI Foundation, Turin, Italy
- Institute for Research on sustainable Development, UMR215 PRODIG, Aubervilliers, France
| | - Michele Tizzoni
- ISI Foundation, Turin, Italy
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | | | - Mvuyo Makhasi
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
| | - Nicole Wolter
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - Brigitte Language
- Unit for Environmental Science and Management, Climatology Research Group, North-West University, Potchefstroom, South Africa
| | - Ryan G. Wagner
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Agincourt, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stefano Tempia
- National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ciro Cattuto
- ISI Foundation, Turin, Italy
- Department of Informatics, University of Turin, Turin, Italy
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10
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Allen AJ, Moore C, Hébert-Dufresne L. Compressing the Chronology of a Temporal Network with Graph Commutators. PHYSICAL REVIEW LETTERS 2024; 132:077402. [PMID: 38427895 PMCID: PMC11223189 DOI: 10.1103/physrevlett.132.077402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 10/20/2023] [Accepted: 01/10/2024] [Indexed: 03/03/2024]
Abstract
Studies of dynamics on temporal networks often represent the network as a series of "snapshots," static networks active for short durations of time. We argue that successive snapshots can be aggregated if doing so has little effect on the overlying dynamics. We propose a method to compress network chronologies by progressively combining pairs of snapshots whose matrix commutators have the smallest dynamical effect. We apply this method to epidemic modeling on real contact tracing data and find that it allows for significant compression while remaining faithful to the epidemic dynamics.
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Affiliation(s)
- Andrea J. Allen
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
- Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA
| | | | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
- Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA
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11
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Shirreff G, Huynh BT, Duval A, Pereira LC, Annane D, Dinh A, Lambotte O, Bulifon S, Guichardon M, Beaune S, Toubiana J, Kermorvant-Duchemin E, Chéron G, Cordel H, Argaud L, Douplat M, Abraham P, Tazarourte K, Martin-Gaujard G, Vanhems P, Hilliquin D, Nguyen D, Chelius G, Fraboulet A, Temime L, Opatowski L, Guillemot D. Assessing respiratory epidemic potential in French hospitals through collection of close contact data (April-June 2020). Sci Rep 2024; 14:3702. [PMID: 38355640 PMCID: PMC10866902 DOI: 10.1038/s41598-023-50228-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 12/17/2023] [Indexed: 02/16/2024] Open
Abstract
The transmission risk of SARS-CoV-2 within hospitals can exceed that in the general community because of more frequent close proximity interactions (CPIs). However, epidemic risk across wards is still poorly described. We measured CPIs directly using wearable sensors given to all present in a clinical ward over a 36-h period, across 15 wards in three hospitals in April-June 2020. Data were collected from 2114 participants and combined with a simple transmission model describing the arrival of a single index case to the ward to estimate the risk of an outbreak. Estimated epidemic risk ranged four-fold, from 0.12 secondary infections per day in an adult emergency to 0.49 per day in general paediatrics. The risk presented by an index case in a patient varied 20-fold across wards. Using simulation, we assessed the potential impact on outbreak risk of targeting the most connected individuals for prevention. We found that targeting those with the highest cumulative contact hours was most impactful (20% reduction for 5% of the population targeted), and on average resources were better spent targeting patients. This study reveals patterns of interactions between individuals in hospital during a pandemic and opens new routes for research into airborne nosocomial risk.
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Affiliation(s)
- George Shirreff
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion, Université Paris Cité, Paris, France
- UVSQ, Inserm, CESP, Anti-Infective Evasion and Pharmacoepidemiology Team, Université Paris-Saclay, Montigny-Le-Bretonneux, France
- Modélisation, Épidémiologie Et Surveillance Des Risques Sanitaires (MESuRS), Conservatoire National Des Arts Et Métiers, Paris, France
| | - Bich-Tram Huynh
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion, Université Paris Cité, Paris, France
- UVSQ, Inserm, CESP, Anti-Infective Evasion and Pharmacoepidemiology Team, Université Paris-Saclay, Montigny-Le-Bretonneux, France
| | - Audrey Duval
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion, Université Paris Cité, Paris, France
| | - Lara Cristina Pereira
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion, Université Paris Cité, Paris, France
| | - Djillali Annane
- IHU PROMETHEUS, Raymond Poincaré Hospital (APHP), INSERM, Université Paris Saclay Campus Versailles, Paris, France
| | - Aurélien Dinh
- Service de Maladies Infectieuses Et Tropicales, AP-HP. Paris Saclay, Hôpital Raymond Poincaré, Garches, France
| | - Olivier Lambotte
- Service de Médecine Interne Et Immunologie Clinique, AP-HP. Paris Saclay, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
- UMR1184, IMVA-HB, Inserm, CEA, Université Paris Saclay, Le Kremlin Bicêtre, France
| | - Sophie Bulifon
- Service de Pneumologie, AP-HP. Paris Saclay, Hôpital de Bicêtre, Le Kremlin Bicêtre, France
| | - Magali Guichardon
- Service de Gériatrie, AP-HP. Paris Saclay, Hôpital Paul Brousse, Villejuif, France
| | - Sebastien Beaune
- Service Des Urgences Adultes, AP-HP. Paris Saclay, Hôpital Ambroise Paré, Boulogne-Billancourt, France
| | - Julie Toubiana
- Service de Pédiatrie Générale, AP-HP. Centre - Université Paris Cité, Hôpital Necker-Enfants Malades, Paris, France
| | - Elsa Kermorvant-Duchemin
- Service de Réanimation Néonatale, AP-HP. Centre - Université Paris Cité, Hôpital Necker-Enfants Malades, Paris, France
| | - Gerard Chéron
- Service Des Urgences Pédiatriques, AP-HP. Centre - Université Paris Cité, Hôpital Necker-Enfants Malades, Paris, France
| | - Hugues Cordel
- Service de Maladies Infectieuses Et Tropicales, AP-HP. Hôpitaux Universitaires Paris Seine-Saint-Denis, Hôpital Avicenne, Bobigny, France
| | - Laurent Argaud
- Service de Réanimation Adulte, Hospices Civils de Lyon - Université Claude Bernard, Hôpital Edouard Herriot, Lyon, France
| | - Marion Douplat
- Service Des Urgences Adultes, Hospices Civils de Lyon - Université Claude Bernard, Hôpital Lyon Sud, Pierre-Bénite, France
| | - Paul Abraham
- Service d'Anesthésie-Réanimation, Hospices Civils de Lyon - Université Claude Bernard, Hôpital Edouard Herriot, Lyon, France
| | - Karim Tazarourte
- Service Des Urgences Adultes, Hospices Civils de Lyon - Université Claude Bernard, Hôpital Edouard Herriot, Lyon, France
| | - Géraldine Martin-Gaujard
- Service de Gériatrie, Hospices Civils de Lyon - Université Claude Bernard, Hôpital Edouard Herriot, Lyon, France
| | - Philippe Vanhems
- Service Hygiène, Épidémiologie, Infectiovigilance Et Prévention, Hospices Civils de Lyon - Université Claude Bernard, Lyon, France
- Centre International de Recherche en Infectiologie, Team Public Health, Epidemiology and Evolutionary Ecology of Infectious Diseases (PHE3ID), Univ Lyon, Inserm, U1111, CNRS, UMR5308, ENS de Lyon, Université Claude Bernard Lyon 1, Lyon, France
| | - Delphine Hilliquin
- Service Hygiène, Épidémiologie, Infectiovigilance Et Prévention, Hospices Civils de Lyon - Université Claude Bernard, Lyon, France
| | - Duc Nguyen
- Service Des Maladies Infectieuses Et Tropicales, CHU de Bordeaux, Hôpital Pellegrin, Bordeaux, France
| | | | | | - Laura Temime
- Modélisation, Épidémiologie Et Surveillance Des Risques Sanitaires (MESuRS), Conservatoire National Des Arts Et Métiers, Paris, France
- PACRI Unit, Conservatoire National Des Arts Et Métiers, Institut Pasteur, Paris, France
| | - Lulla Opatowski
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion, Université Paris Cité, Paris, France
- UVSQ, Inserm, CESP, Anti-Infective Evasion and Pharmacoepidemiology Team, Université Paris-Saclay, Montigny-Le-Bretonneux, France
| | - Didier Guillemot
- Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion, Université Paris Cité, Paris, France.
- UVSQ, Inserm, CESP, Anti-Infective Evasion and Pharmacoepidemiology Team, Université Paris-Saclay, Montigny-Le-Bretonneux, France.
- Department of Public Health, Medical Information, Clinical Research, AP-HP. Paris Saclay, Paris, France.
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12
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Albery GF, Bansal S, Silk MJ. Comparative approaches in social network ecology. Ecol Lett 2024; 27:e14345. [PMID: 38069575 DOI: 10.1111/ele.14345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 01/31/2024]
Abstract
Social systems vary enormously across the animal kingdom, with important implications for ecological and evolutionary processes such as infectious disease dynamics, anti-predator defence, and the evolution of cooperation. Comparing social network structures between species offers a promising route to help disentangle the ecological and evolutionary processes that shape this diversity. Comparative analyses of networks like these are challenging and have been used relatively little in ecology, but are becoming increasingly feasible as the number of empirical datasets expands. Here, we provide an overview of multispecies comparative social network studies in ecology and evolution. We identify a range of advancements that these studies have made and key challenges that they face, and we use these to guide methodological and empirical suggestions for future research. Overall, we hope to motivate wider publication and analysis of open social network datasets in animal ecology.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington, District of Columbia, USA
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, District of Columbia, USA
| | - Matthew J Silk
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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13
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Aguolu OG, Kiti MC, Nelson K, Liu CY, Sundaram M, Gramacho S, Jenness S, Melegaro A, Sacoor C, Bardaji A, Macicame I, Jose A, Cavele N, Amosse F, Uamba M, Jamisse E, Tchavana C, Briones HGM, Jarquín C, Ajsivinac M, Pischel L, Ahmed N, Mohan VR, Srinivasan R, Samuel P, John G, Ellington K, Joaquim OA, Zelaya A, Kim S, Chen H, Kazi M, Malik F, Yildirim I, Lopman B, Omer SB. Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299472. [PMID: 38105989 PMCID: PMC10723497 DOI: 10.1101/2023.12.05.23299472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
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Affiliation(s)
| | | | - Kristin Nelson
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Carol Y. Liu
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Maria Sundaram
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Sergio Gramacho
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Samuel Jenness
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Alessia Melegaro
- DONDENA Centre for Research in Social Dynamics and Public Policy, Bocconi University, Italy
| | | | - Azucena Bardaji
- Manhiça Health Research Centre, Manhica, Mozambique
- ISGlobal, Hospital Clinic – Universitat de Barcelona, Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ivalda Macicame
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Americo Jose
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | - Nilzio Cavele
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | - Migdalia Uamba
- Polana Caniço Health Research and Training Centre, CISPOC, Mozambique
| | | | | | | | - Claudia Jarquín
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - María Ajsivinac
- Centro de Estudios en Salud (CES), Universidad del Valle de Guatemala
| | - Lauren Pischel
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Noureen Ahmed
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | | | | | | | - Gifta John
- Christian Medical College Vellore, India
| | - Kye Ellington
- Rollins School of Public Health, Emory University, Georgia, USA
| | | | - Alana Zelaya
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Sara Kim
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Holin Chen
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Momin Kazi
- The Aga Khan University, Karachi, Pakistán
| | - Fauzia Malik
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
| | - Inci Yildirim
- Yale School of Medicine, Yale University, Connecticut, USA
| | - Benjamin Lopman
- Rollins School of Public Health, Emory University, Georgia, USA
| | - Saad B. Omer
- Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas, Texas
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14
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Bhaumik J, Masuda N. Fixation probability in evolutionary dynamics on switching temporal networks. J Math Biol 2023; 87:64. [PMID: 37768362 PMCID: PMC10539469 DOI: 10.1007/s00285-023-01987-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/03/2023] [Accepted: 08/13/2023] [Indexed: 09/29/2023]
Abstract
Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. Research in the past two decades has found various families of amplifiers while suppressors still remain somewhat elusive. It has also been discovered that most networks are amplifiers of selection under the birth-death updating combined with uniform initialization, which is a standard condition assumed widely in the literature. In the present study, we extend the birth-death processes to temporal (i.e., time-varying) networks. For the sake of tractability, we restrict ourselves to switching temporal networks, in which the network structure deterministically alternates between two static networks at constant time intervals or stochastically in a Markovian manner. We show that, in a majority of cases, switching networks are less amplifying than both of the two static networks constituting the switching networks. Furthermore, most small switching networks, i.e., networks on six nodes or less, are suppressors, which contrasts to the case of static networks.
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Affiliation(s)
- Jnanajyoti Bhaumik
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, 14260-2900, USA
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, 14260-2900, USA.
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, NY, 14260-5030, USA.
- Center for Computational Social Science, Kobe University, Kobe, 657-8501, Japan.
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15
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Cui J, Cho S, Kamruzzaman M, Bielskas M, Vullikanti A, Prakash BA. Using spectral characterization to identify healthcare-associated infection (HAI) patients for clinical contact precaution. Sci Rep 2023; 13:16197. [PMID: 37758756 PMCID: PMC10533902 DOI: 10.1038/s41598-023-41852-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Healthcare-associated infections (HAIs) are a major problem in hospital infection control. Although HAIs can be suppressed using contact precautions, such precautions are expensive, and we can only apply them to a small fraction of patients (i.e., a limited budget). In this work, we focus on two clinical problems arising from the limited budget: (a) choosing the best patients to be placed under precaution given a limited budget to minimize the spread (the isolation problem), and (b) choosing the best patients to release when limited budget requires some of the patients to be cleared from precaution (the clearance problem). A critical challenge in addressing them is that HAIs have multiple transmission pathways such that locations can also accumulate 'load' and spread the disease. One of the most common practices when placing patients under contact precautions is the regular clearance of pathogen loads. However, standard propagation models like independent cascade (IC)/susceptible-infectious-susceptible (SIS) cannot capture such mechanisms directly. Hence to account for this challenge, using non-linear system theory, we develop a novel spectral characterization of a recently proposed pathogen load based model, 2-MODE-SIS model, on people/location networks to capture spread dynamics of HAIs. We formulate the two clinical problems using this spectral characterization and develop effective and efficient algorithms for them. Our experiments show that our methods outperform several natural structural and clinical approaches on real-world hospital testbeds and pick meaningful solutions.
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Affiliation(s)
- Jiaming Cui
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Sungjun Cho
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Methun Kamruzzaman
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
| | - Matthew Bielskas
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA
| | - Anil Vullikanti
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, 22904, USA
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA
| | - B Aditya Prakash
- College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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16
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Gustin MP, Pujo-Menjouet L, Vanhems P. Influenza transmissibility among patients and health-care professionals in a geriatric short-stay unit using individual contact data. Sci Rep 2023; 13:10547. [PMID: 37386032 PMCID: PMC10310843 DOI: 10.1038/s41598-023-36908-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 06/12/2023] [Indexed: 07/01/2023] Open
Abstract
Detailed information are lacking on influenza transmissibility in hospital although clusters are regularly reported. In this pilot study, our goal was to estimate the transmission rate of H3N2 2012-influenza, among patients and health care professionals in a short-term Acute Care for the Elderly Unit by using a stochastic approach and a simple susceptible-exposed-infectious-removed model. Transmission parameters were derived from documented individual contact data collected by Radio Frequency IDentification technology at the epidemic peak. From our model, nurses appeared to transmit infection to a patient more frequently with a transmission rate of 1.04 per day on average compared to 0.38 from medical doctors. This transmission rate was 0.34 between nurses. These results, even obtained in this specific context, might give a relevant insight of the influenza dynamics in hospitals and will help to improve and to target control measures for preventing nosocomial transmission of influenza. The investigation of nosocomial transmission of SARS-COV-2 might gain from similar approaches.
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Affiliation(s)
- Marie-Paule Gustin
- Department of Public Health, Institute of Pharmacy, CIRI-Centre International de Recherche en Infectiologie, Inserm, U1111, CNRS, UMR 5308, ENS Lyon, Equipe PHIE3D, University Lyon, University Claude Bernard Lyon 1, 7 Rue Guillaume Paradin, 69372, Lyon, France
| | - Laurent Pujo-Menjouet
- University of Lyon, University Claude Bernard Lyon 1, CNRS UMR5208, Inria, Dracula Team, Institut Camille Jordan, 69622, Villeurbanne, France.
| | - Philippe Vanhems
- Hospices Civils de Lyon, Service Hygiène, CIRI-Centre International de Recherche en Infectiologie, Université Lyon, Université Claude Bernard Lyon 1, Inserm, U1111, CNRS, UMR5308, ENS Lyon, Lyon, France
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17
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Cencetti G, Contreras DA, Mancastroppa M, Barrat A. Distinguishing Simple and Complex Contagion Processes on Networks. PHYSICAL REVIEW LETTERS 2023; 130:247401. [PMID: 37390429 DOI: 10.1103/physrevlett.130.247401] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/25/2023] [Accepted: 05/17/2023] [Indexed: 07/02/2023]
Abstract
Contagion processes on networks, including disease spreading, information diffusion, or social behaviors propagation, can be modeled as simple contagion, i.e., as a contagion process involving one connection at a time, or as complex contagion, in which multiple interactions are needed for a contagion event. Empirical data on spreading processes, however, even when available, do not easily allow us to uncover which of these underlying contagion mechanisms is at work. We propose a strategy to discriminate between these mechanisms upon the observation of a single instance of a spreading process. The strategy is based on the observation of the order in which network nodes are infected, and on its correlations with their local topology: these correlations differ between processes of simple contagion, processes involving threshold mechanisms, and processes driven by group interactions (i.e., by "higher-order" mechanisms). Our results improve our understanding of contagion processes and provide a method using only limited information to distinguish between several possible contagion mechanisms.
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Affiliation(s)
| | - Diego Andrés Contreras
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Marco Mancastroppa
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
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18
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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.
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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.
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19
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Brattig Correia R, Barrat A, Rocha LM. Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs. PLoS Comput Biol 2023; 19:e1010854. [PMID: 36821564 PMCID: PMC9949650 DOI: 10.1371/journal.pcbi.1010854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 01/06/2023] [Indexed: 02/24/2023] Open
Abstract
The structure of social networks strongly affects how different phenomena spread in human society, from the transmission of information to the propagation of contagious diseases. It is well-known that heterogeneous connectivity strongly favors spread, but a precise characterization of the redundancy present in social networks and its effect on the robustness of transmission is still lacking. This gap is addressed by the metric backbone, a weight- and connectivity-preserving subgraph that is sufficient to compute all shortest paths of weighted graphs. This subgraph is obtained via algebraically-principled axioms and does not require statistical sampling based on null-models. We show that the metric backbones of nine contact networks obtained from proximity sensors in a variety of social contexts are generally very small, 49% of the original graph for one and ranging from about 6% to 20% for the others. This reflects a surprising amount of redundancy and reveals that shortest paths on these networks are very robust to random attacks and failures. We also show that the metric backbone preserves the full distribution of shortest paths of the original contact networks-which must include the shortest inter- and intra-community distances that define any community structure-and is a primary subgraph for epidemic transmission based on pure diffusion processes. This suggests that the organization of social contact networks is based on large amounts of shortest-path redundancy which shapes epidemic spread in human populations. Thus, the metric backbone is an important subgraph with regard to epidemic spread, the robustness of social networks, and any communication dynamics that depend on complex network shortest paths.
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Affiliation(s)
- Rion Brattig Correia
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- Department of Systems Science and Industrial Engineering, Center for Social and Biomedical Complexity, Binghamton University, Binghamton New York, United States of America
| | - Alain Barrat
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Luis M. Rocha
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- Department of Systems Science and Industrial Engineering, Center for Social and Biomedical Complexity, Binghamton University, Binghamton New York, United States of America
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20
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Bauzá Mingueza F, Floría M, Gómez-Gardeñes J, Arenas A, Cardillo A. Characterization of interactions' persistence in time-varying networks. Sci Rep 2023; 13:765. [PMID: 36641475 PMCID: PMC9840642 DOI: 10.1038/s41598-022-25907-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 12/06/2022] [Indexed: 01/15/2023] Open
Abstract
Many complex networked systems exhibit volatile dynamic interactions among their vertices, whose order and persistence reverberate on the outcome of dynamical processes taking place on them. To quantify and characterize the similarity of the snapshots of a time-varying network-a proxy for the persistence,-we present a study on the persistence of the interactions based on a descriptor named temporality. We use the average value of the temporality, [Formula: see text], to assess how "special" is a given time-varying network within the configuration space of ordered sequences of snapshots. We analyse the temporality of several empirical networks and find that empirical sequences are much more similar than their randomized counterparts. We study also the effects on [Formula: see text] induced by the (time) resolution at which interactions take place.
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Affiliation(s)
- Francisco Bauzá Mingueza
- Department of Theoretical Physics, University of Zaragoza, 50006, Zaragoza, Spain
- GOTHAM Lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
| | - Mario Floría
- GOTHAM Lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
- Department of Condensed Matter Physics, University of Zaragoza, 50006, Zaragoza, Spain
| | - Jesús Gómez-Gardeñes
- GOTHAM Lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
- Department of Condensed Matter Physics, University of Zaragoza, 50006, Zaragoza, Spain
| | - Alex Arenas
- Department of Computer Science and Mathematics, University Rovira i Virgili, 43007, Tarragona, Spain
| | - Alessio Cardillo
- Department of Computer Science and Mathematics, University Rovira i Virgili, 43007, Tarragona, Spain.
- GOTHAM Lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain.
- Internet Interdisciplinary Institute (IN3), Open University of Catalonia, 08018, Barcelona, Spain.
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21
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Investigating healthcare worker mobility and patient contacts within a UK hospital during the COVID-19 pandemic. COMMUNICATIONS MEDICINE 2022; 2:165. [PMID: 36564506 PMCID: PMC9782286 DOI: 10.1038/s43856-022-00229-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Insights into behaviours relevant to the transmission of infections are extremely valuable for epidemiological investigations. Healthcare worker (HCW) mobility and patient contacts within the hospital can contribute to nosocomial outbreaks, yet data on these behaviours are often limited. METHODS Using electronic medical records and door access logs from a London teaching hospital during the COVID-19 pandemic, we derive indicators for HCW mobility and patient contacts at an aggregate level. We assess the spatial-temporal variations in HCW behaviour and, to demonstrate the utility of these behavioural markers, investigate changes in the indirect connectivity of patients (resulting from shared contacts with HCWs) and spatial connectivity of floors (owing to the movements of HCWs). RESULTS Fluctuations in HCW mobility and patient contacts were identified during the pandemic, with the most prominent changes in behaviour on floors handling the majority of COVID-19 patients. The connectivity between floors was disrupted by the pandemic and, while this stabilised after the first wave, the interconnectivity of COVID-19 and non-COVID-19 wards always featured. Daily rates of indirect contact between patients provided evidence for reactive staff cohorting in response to the number of COVID-19 patients in the hospital. CONCLUSIONS Routinely collected electronic records in the healthcare environment provide a means to rapidly assess and investigate behaviour change in the HCW population, and can support evidence based infection prevention and control activities. Integrating frameworks like ours into routine practice will empower decision makers and improve pandemic preparedness by providing tools to help curtail nosocomial outbreaks of communicable diseases.
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22
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A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks. Sci Rep 2022; 12:21355. [PMID: 36494427 PMCID: PMC9734664 DOI: 10.1038/s41598-022-24652-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/18/2022] [Indexed: 12/13/2022] Open
Abstract
We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial network structure, i.e., non-adaptive approach, and performing partial node rank recalculation after node removal, i.e., semi-adaptive approach. To quantify the efficacy of each vaccination strategy, we used three epidemic spread indicators: the size of the largest connected component, the total number of infected at the end of the epidemic, and the maximum number of simultaneously infected individuals. We show that the best vaccination strategies in the non-adaptive and semi-adaptive approaches are different and that the best strategy also depends on the number of available vaccines. Furthermore, a partial recalculation of the node centrality increases the efficacy of the vaccination strategies by up to 80%.
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23
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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.
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24
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$$\Delta $$-Conformity: multi-scale node assortativity in feature-rich stream graphs. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2022. [DOI: 10.1007/s41060-022-00375-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractMulti-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the time-varying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: $$\Delta $$
Δ
-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes’ connectivity along a period of time $$\Delta $$
Δ
starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce $$\Delta $$
Δ
-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.
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25
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Inference of hyperedges and overlapping communities in hypergraphs. Nat Commun 2022; 13:7229. [PMID: 36433942 PMCID: PMC9700742 DOI: 10.1038/s41467-022-34714-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions. Furthermore, our model has an efficient numerical implementation, and it runs faster than dyadic algorithms on pairwise records projected from higher-order data. We apply our method to a variety of real-world systems, showing strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges. Our approach illustrates the fundamental advantages of a hypergraph probabilistic model when modeling relational systems with higher-order interactions.
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26
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Kardaś-Słoma L, Fournier S, Dupont JC, Rochaix L, Birgand G, Zahar JR, Lescure FX, Kernéis S, Durand-Zaleski I, Lucet JC. Cost-effectiveness of strategies to control the spread of carbapenemase-producing Enterobacterales in hospitals: a modelling study. Antimicrob Resist Infect Control 2022; 11:117. [PMID: 36117231 PMCID: PMC9484055 DOI: 10.1186/s13756-022-01149-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/03/2022] [Indexed: 11/24/2022] Open
Abstract
Background Spread of resistant bacteria causes severe morbidity and mortality. Stringent control measures can be expensive and disrupt hospital organization. In the present study, we assessed the effectiveness and cost-effectiveness of control strategies to prevent the spread of Carbapenemase-producing Enterobacterales (CPE) in a general hospital ward (GW). Methods A dynamic, stochastic model simulated the transmission of CPE by the hands of healthcare workers (HCWs) and the environment in a hypothetical 25-bed GW. Input parameters were based on published data; we assumed the prevalence at admission of 0.1%. 12 strategies were compared to the baseline (no control) and combined different prevention and control interventions: targeted or universal screening at admission (TS or US), contact precautions (CP), isolation in a single room, dedicated nursing staff (DNS) for carriers and weekly screening of contact patients (WSC). Time horizon was one year. Outcomes were the number of CPE acquisitions, costs, and incremental cost-effectiveness ratios (ICER). A hospital perspective was adopted to estimate costs, which included laboratory costs, single room, contact precautions, staff time, i.e. infection control nurse and/or dedicated nursing staff, and lost bed-days due to prolonged hospital stay of identified carriers. The model was calibrated on actual datasets. Sensitivity analyses were performed. Results The baseline scenario resulted in 0.93 CPE acquisitions/1000 admissions and costs 32,050 €/1000 admissions. All control strategies increased costs and improved the outcome. The efficiency frontier was represented by: (1) TS with DNS at a 17,407 €/avoided CPE case, (2) TS + DNS + WSC at a 30,700 €/avoided CPE case and (3) US + DNS + WSC at 181,472 €/avoided CPE case. Other strategies were dominated. Sensitivity analyses showed that TS + CP might be cost-effective if CPE carriers are identified upon admission or if the cases have a short hospital stay. However, CP were effective only when high level of compliance with hand hygiene was obtained. Conclusions Targeted screening at admission combined with DNS for identified CPE carriers with or without weekly screening were the most cost-effective options to limit the spread of CPE. These results support current recommendations from several high-income countries. Supplementary Information The online version contains supplementary material available at 10.1186/s13756-022-01149-0.
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27
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Erkol Ş, Mazzilli D, Radicchi F. Effective submodularity of influence maximization on temporal networks. Phys Rev E 2022; 106:034301. [PMID: 36266883 DOI: 10.1103/physreve.106.034301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
We study influence maximization on temporal networks. This is a special setting where the influence function is not submodular, and there is no optimality guarantee for solutions achieved via greedy optimization. We perform an exhaustive analysis on both real and synthetic networks. We show that the influence function of randomly sampled sets of seeds often violates the necessary conditions for submodularity. However, when sets of seeds are selected according to the greedy optimization strategy, the influence function behaves effectively as a submodular function. Specifically, violations of the necessary conditions for submodularity are never observed in real networks, and only rarely in synthetic ones. The direct comparison with exact solutions obtained via brute-force search indicates that the greedy strategy provides approximate solutions that are well within the optimality gap guaranteed for strictly submodular functions. Greedy optimization appears, therefore, to be an effective strategy for the maximization of influence on temporal networks.
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Affiliation(s)
- Şirag Erkol
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - Dario Mazzilli
- Enrico Fermi Research Center, Via Panisperna 89 A, Rome, Italy
| | - Filippo Radicchi
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
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28
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Liu X, Dou Z, Wang L, Su B, Jin T, Guo Y, Wei J, Zhang N. Close contact behavior-based COVID-19 transmission and interventions in a subway system. JOURNAL OF HAZARDOUS MATERIALS 2022; 436:129233. [PMID: 35739753 PMCID: PMC9132379 DOI: 10.1016/j.jhazmat.2022.129233] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 05/29/2023]
Abstract
During COVID-19 pandemic, analysis on virus exposure and intervention efficiency in public transports based on real passenger's close contact behaviors is critical to curb infectious disease transmission. A monitoring device was developed to gather a total of 145,821 close contact data in subways based on semi-supervision learning. A virus transmission model considering both short- and long-range inhalation and deposition was established to calculate the virus exposure. During rush-hour, short-range inhalation exposure is 3.2 times higher than deposition exposure and 7.5 times higher than long-range inhalation exposure of all passengers in the subway. The close contact rate was 56.1 % and the average interpersonal distance was 0.8 m. Face-to-back was the main pattern during close contact. Comparing with random distribution, if all passengers stand facing in the same direction, personal virus exposure through inhalation (deposition) can be reduced by 74.1 % (98.5 %). If the talk rate was decreased from 20 % to 5 %, the inhalation (deposition) exposure can be reduced by 69.3 % (73.8 %). In addition, we found that virus exposure could be reduced by 82.0 % if all passengers wear surgical masks. This study provides scientific support for COVID-19 prevention and control in subways based on real human close contact behaviors.
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Affiliation(s)
- Xiyue Liu
- Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China
| | - Zhiyang Dou
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Lei Wang
- Institute of Refrigeration and Cryogenics/Key Laboratory of Refrigeration and Cryogenic Technology of Zhejiang Province, Zhejiang University, Hangzhou, China
| | - Boni Su
- China Electric Power Planning & Engineering Institute, Beijing, China
| | - Tianyi Jin
- Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China
| | - Yong Guo
- Department of Building Science, Tsinghua University, Beijing, China
| | - Jianjian Wei
- Institute of Refrigeration and Cryogenics/Key Laboratory of Refrigeration and Cryogenic Technology of Zhejiang Province, Zhejiang University, Hangzhou, China
| | - Nan Zhang
- Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China.
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29
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Contreras DA, Colosi E, Bassignana G, Colizza V, Barrat A. Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases. J R Soc Interface 2022; 19:20220164. [PMID: 35730172 PMCID: PMC9214285 DOI: 10.1098/rsif.2022.0164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/31/2022] [Indexed: 11/12/2022] Open
Abstract
Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.
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Affiliation(s)
- Diego Andrés Contreras
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Elisabetta Colosi
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Giulia Bassignana
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - Alain Barrat
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
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30
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Dekker MM, Schram RD, Ou J, Panja D. Hidden dependence of spreading vulnerability on topological complexity. Phys Rev E 2022; 105:054301. [PMID: 35706267 DOI: 10.1103/physreve.105.054301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
Many dynamical phenomena in complex systems concern spreading that plays out on top of networks with changing architecture over time-commonly known as temporal networks. A complex system's proneness to facilitate spreading phenomena, which we abbreviate as its "spreading vulnerability," is often surmised to be related to the topology of the temporal network featured by the system. Yet, cleanly extracting spreading vulnerability of a complex system directly from the topological information of the temporal network remains a challenge. Here, using data from a diverse set of real-world complex systems, we develop the "entropy of temporal entanglement" as a quantity to measure topological complexities of temporal networks. We show that this parameter-free quantity naturally allows for topological comparisons across vastly different complex systems. Importantly, by simulating three different types of stochastic dynamical processes playing out on top of temporal networks, we demonstrate that the entropy of temporal entanglement serves as a quantitative embodiment of the systems' spreading vulnerability, irrespective of the details of the processes. In being able to do so, i.e., in being able to quantitatively extract a complex system's proneness to facilitate spreading phenomena from topology, this entropic measure opens itself for applications in a wide variety of natural, social, biological, and engineered systems.
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Affiliation(s)
- Mark M Dekker
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
| | - Raoul D Schram
- Information and Technology Services, Heidelberglaan 8, 3584 CS Utrecht, The Netherlands
| | - Jiamin Ou
- Department of Sociology, Utrecht University, Padualaan 14, 3584 CH Utrecht, Netherlands
| | - Debabrata Panja
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
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31
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Hegde C, Rad AB, Sameni R, Clifford GD. Modeling Social Distancing and Quantifying Epidemic Disease Exposure in a Built Environment. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022; 16:289-299. [PMID: 36212235 PMCID: PMC9534385 DOI: 10.1109/jstsp.2022.3145622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
As we transition away from pandemic-induced isolation and social distancing, there is a need to estimate the risk of exposure in built environments. We propose a novel metric to quantify social distancing and the potential risk of exposure to airborne diseases in an indoor setting, which scales with distance and the number of people present. The risk of exposure metric is designed to incorporate the dynamics of particle movement in an enclosed set of rooms for people at different immunity levels, susceptibility due to age, background infection rates, intrinsic individual risk factors (e.g., comorbidities), mask-wearing levels, the half-life of the virus and ventilation rate in the environment. The model parameters have been selected for COVID-19, although the modeling framework applies to other airborne diseases. The performance of the metric is tested using simulations of a real physical environment, combining models for walking, path length dynamics, and air-conditioning replacement action. We have also created a visualization tool to help identify high-risk areas in the built environment. The resulting software framework is being used to help with planning movement and scheduling in a clinical environment ahead of reopening of the facility, for deciding the maximum time within an environment that is safe for a given number of people, for air replacement settings on air-conditioning and heating systems, and for mask-wearing policies. The framework can also be used for identifying locations where foot traffic might create high-risk zones and for planning timetabled transitions of groups of people between activities in different spaces. Moreover, when coupled with individual-level location tracking (via radio-frequency tagging, for example), the exposure risk metric can be used in real-time to estimate the risk of exposure to the coronavirus or other airborne illnesses, and intervene through air-conditioning action modification, changes in timetabling of group activities, mask-wearing policies, or restricting the number of individuals entering a given room/space. All software are provided online under an open-source license.
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Affiliation(s)
- Chaitra Hegde
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Ali Bahrami Rad
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Reza Sameni
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Gari D Clifford
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
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32
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Leoni E, Cencetti G, Santin G, Istomin T, Molteni D, Picco GP, Farella E, Lepri B, Murphy AL. Measuring close proximity interactions in summer camps during the COVID-19 pandemic. EPJ DATA SCIENCE 2022; 11:5. [PMID: 35127327 PMCID: PMC8802275 DOI: 10.1140/epjds/s13688-022-00316-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
Policy makers have implemented multiple non-pharmaceutical strategies to mitigate the COVID-19 worldwide crisis. Interventions had the aim of reducing close proximity interactions, which drive the spread of the disease. A deeper knowledge of human physical interactions has revealed necessary, especially in all settings involving children, whose education and gathering activities should be preserved. Despite their relevance, almost no data are available on close proximity contacts among children in schools or other educational settings during the pandemic. Contact data are usually gathered via Bluetooth, which nonetheless offers a low temporal and spatial resolution. Recently, ultra-wideband (UWB) radios emerged as a more accurate alternative that nonetheless exhibits a significantly higher energy consumption, limiting in-field studies. In this paper, we leverage a novel approach, embodied by the Janus system that combines these radios by exploiting their complementary benefits. The very accurate proximity data gathered in-field by Janus, once augmented with several metadata, unlocks unprecedented levels of information, enabling the development of novel multi-level risk analyses. By means of this technology, we have collected real contact data of children and educators in three summer camps during summer 2020 in the province of Trento, Italy. The wide variety of performed daily activities induced multiple individual behaviors, allowing a rich investigation of social environments from the contagion risk perspective. We consider risk based on duration and proximity of contacts and classify interactions according to different risk levels. We can then evaluate the summer camps' organization, observe the effect of partition in small groups, or social bubbles, and identify the organized activities that mitigate the riskier behaviors. Overall, we offer an insight into the educator-child and child-child social interactions during the pandemic, thus providing a valuable tool for schools, summer camps, and policy makers to (re)structure educational activities safely.
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Affiliation(s)
- Elia Leoni
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
- DEI, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
| | - Giulia Cencetti
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
| | - Gabriele Santin
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
| | - Timofei Istomin
- DISI, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| | - Davide Molteni
- DISI, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| | | | | | - Bruno Lepri
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
| | - Amy L. Murphy
- DIGIS, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
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33
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PIYARAJ P, KITTIKRAISAK W, BUATHONG S, SINTHUWATTANAWIBOOL C, NIVESVIVAT T, YOOCHAROEN P, NUCHTEAN T, KLUNGTHONG C, LYMAN M, MOTT JA, CHOTTANAPUND S. Encounter patterns and worker absenteeism/presenteeism among healthcare providers in Thailand. CURRENT RESEARCH IN BEHAVIORAL SCIENCES 2022. [DOI: 10.1016/j.crbeha.2022.100067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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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.
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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
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35
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Harper R, Tee P. Balancing capacity and epidemic spread in the global airline network. APPLIED NETWORK SCIENCE 2021; 6:94. [PMID: 34849399 PMCID: PMC8613734 DOI: 10.1007/s41109-021-00432-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 10/06/2021] [Indexed: 06/13/2023]
Abstract
The structure of complex networks has long been understood to play a role in transmission and spreading phenomena on a graph. Such networks form an important part of the structure of society, including transportation networks. As society fights to control the COVID-19 pandemic, an important question is how to choose the optimum balance between the full opening of transport networks and the control of epidemic spread. In this work we investigate the interplay between network dismantling and epidemic spread rate as a proxy for the imposition of travel restrictions to control disease spread. For network dismantling we focus on the weighted and unweighted forms of metrics that capture the topological and informational structure of the network. Our results indicate that there is benefit to a directed approach to imposing travel restrictions, but we identify that more detailed models of the transport network are necessary for definitive results.
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Affiliation(s)
| | - Philip Tee
- Science Group, Moogsoft Inc., San Francisco, CA USA
- The Beyond Center for Fundamental Science, University of Arizona, Tempe, AZ USA
- Department of Informatics, University of Sussex, Falmer, Brighton, UK
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36
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Avraam D, Obradovich N, Pescetelli N, Cebrian M, Rutherford A. The network limits of infectious disease control via occupation-based targeting. Sci Rep 2021; 11:22855. [PMID: 34819577 PMCID: PMC8613398 DOI: 10.1038/s41598-021-02226-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 11/08/2021] [Indexed: 01/08/2023] Open
Abstract
Policymakers commonly employ non-pharmaceutical interventions to reduce the scale and severity of pandemics. Of non-pharmaceutical interventions, physical distancing policies-designed to reduce person-to-person pathogenic spread - have risen to recent prominence. In particular, stay-at-home policies of the sort widely implemented around the globe in response to the COVID-19 pandemic have proven to be markedly effective at slowing pandemic growth. However, such blunt policy instruments, while effective, produce numerous unintended consequences, including potentially dramatic reductions in economic productivity. In this study, we develop methods to investigate the potential to simultaneously contain pandemic spread while also minimizing economic disruptions. We do so by incorporating both occupational and contact network information contained within an urban environment, information that is commonly excluded from typical pandemic control policy design. The results of our methods suggest that large gains in both economic productivity and pandemic control might be had by the incorporation and consideration of simple-to-measure characteristics of the occupational contact network. We find evidence that more sophisticated, and more privacy invasive, measures of this network do not drastically increase performance.
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Affiliation(s)
- Demetris Avraam
- Centre for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Nick Obradovich
- Centre for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
| | - Niccolò Pescetelli
- Centre for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
| | - Manuel Cebrian
- Centre for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany.
| | - Alex Rutherford
- Centre for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany.
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37
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Abstract
AbstractTemporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal subgraphs, also known as temporal motifs, is a challenging task. In this work, we present an efficient technique for extracting temporal motifs in temporal networks. Our method is based on the novel notion of egocentric temporal neighborhoods, namely multi-layer structures centered on an ego node. Each temporal layer of the structure consists of the first-order neighborhood of the ego node, and corresponding nodes in sequential layers are connected by an edge. The strength of this approach lies in the possibility of encoding these structures into a unique bit vector, thus bypassing the problem of graph isomorphism in searching for temporal motifs. This allows our algorithm to mine substantially larger motifs with respect to alternative approaches. Furthermore, by bringing the focus on the temporal dynamics of the interactions of a specific node, our model allows to mine temporal motifs which are visibly interpretable. Experiments on a number of complex networks of social interactions confirm the advantage of the proposed approach over alternative non-egocentric solutions. The egocentric procedure is indeed more efficient in revealing similarities and discrepancies among different social environments, independently of the different technologies used to collect data, which instead affect standard non-egocentric measures.
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38
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Milligan WR, Fuller ZL, Agarwal I, Eisen MB, Przeworski M, Sella G. Impact of essential workers in the context of social distancing for epidemic control. PLoS One 2021; 16:e0255680. [PMID: 34347855 PMCID: PMC8336873 DOI: 10.1371/journal.pone.0255680] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
New emerging infectious diseases are identified every year, a subset of which become global pandemics like COVID-19. In the case of COVID-19, many governments have responded to the ongoing pandemic by imposing social policies that restrict contacts outside of the home, resulting in a large fraction of the workforce either working from home or not working. To ensure essential services, however, a substantial number of workers are not subject to these limitations, and maintain many of their pre-intervention contacts. To explore how contacts among such "essential" workers, and between essential workers and the rest of the population, impact disease risk and the effectiveness of pandemic control, we evaluated several mathematical models of essential worker contacts within a standard epidemiology framework. The models were designed to correspond to key characteristics of cashiers, factory employees, and healthcare workers. We find in all three models that essential workers are at substantially elevated risk of infection compared to the rest of the population, as has been documented, and that increasing the numbers of essential workers necessitates the imposition of more stringent controls on contacts among the rest of the population to manage the pandemic. Importantly, however, different archetypes of essential workers differ in both their individual probability of infection and impact on the broader pandemic dynamics, highlighting the need to understand and target intervention for the specific risks faced by different groups of essential workers. These findings, especially in light of the massive human costs of the current COVID-19 pandemic, indicate that contingency plans for future epidemics should account for the impacts of essential workers on disease spread.
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Affiliation(s)
- William R. Milligan
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
| | - Zachary L. Fuller
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
| | - Ipsita Agarwal
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
| | - Michael B. Eisen
- Howard Hughes Medical Institute, University of California, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, California, United States of America
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
- Department of Systems Biology, Columbia University, New York City, New York, United States of America
- Program for Mathematical Genomics, Columbia University, New York City, New York, United States of America
| | - Guy Sella
- Department of Biological Sciences, Columbia University, New York City, New York, United States of America
- Program for Mathematical Genomics, Columbia University, New York City, New York, United States of America
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39
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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.
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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
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40
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Sajjadi S, Ejtehadi MR, Ghanbarnejad F. Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics. PLoS One 2021; 16:e0253563. [PMID: 34283838 PMCID: PMC8291698 DOI: 10.1371/journal.pone.0253563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/08/2021] [Indexed: 11/18/2022] Open
Abstract
We first propose a quantitative approach to detect high risk outbreaks of independent and coinfective SIR dynamics on three empirical networks: a school, a conference and a hospital contact network. This measurement is based on the k-means clustering method and identifies proper samples for calculating the mean outbreak size and the outbreak probability. Then we systematically study the impact of different temporal correlations on high risk outbreaks over the original and differently shuffled counterparts of each network. We observe that, on the one hand, in the coinfection process, randomization of the sequence of the events increases the mean outbreak size of high-risk cases. On the other hand, these correlations do not have a consistent effect on the independent infection dynamics, and can either decrease or increase this mean. Randomization of the daily pattern correlations has no strong impact on the size of the outbreak in either the coinfection or the independent spreading cases. We also observe that an increase in the mean outbreak size does not always coincide with an increase in the outbreak probability; therefore, we argue that merely considering the mean outbreak size of all realizations may lead us into falsely estimating the outbreak risks. Our results suggest that some sort of contact randomization in the organizational level in schools, events or hospitals might help to suppress the spreading dynamics while the risk of an outbreak is high.
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Affiliation(s)
- Sina Sajjadi
- Department of Physics, Sharif University of Technology, Tehran, Iran
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41
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Ito-Masui A, Kawamoto E, Esumi R, Imai H, Shimaoka M. Sociometric wearable devices for studying human behavior in corporate and healthcare workplaces. Biotechniques 2021; 71:392-399. [PMID: 34164992 DOI: 10.2144/btn-2020-0160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Wearable sensor technology enables objective data collection of direct human interactions. The authors review sociometric wearable devices (SWD) and their application in healthcare. Human interactions captured by wearable sensors have been shown to correlate with social constructs such as teamwork and productivity in the office. Application of SWD in the field of healthcare requires special considerations: validation studies have shown technological disadvantages in acute medical settings. Application of SWD in healthcare should be considered based on the strengths and weaknesses of the methodology. SWD can also play an important role in investigation of human interaction and epidemic spread. When study designs and methodologies are carefully considered, incorporation of SWD in healthcare research has promising potential for new insights.
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Affiliation(s)
- Asami Ito-Masui
- Emergency & Critical Care Center, Mie University Hospital, Mie, 5148507, Japan.,Department of Emergency & Disaster Medicine, Mie University Graduate School of Medicine, Mie, 5148507, Japan.,Department of Molecular Pathobiology & Cell Adhesion Biology, Mie University Graduate School of Medicine, Mie, 5148507, Japan
| | - Eiji Kawamoto
- Emergency & Critical Care Center, Mie University Hospital, Mie, 5148507, Japan.,Department of Emergency & Disaster Medicine, Mie University Graduate School of Medicine, Mie, 5148507, Japan.,Department of Molecular Pathobiology & Cell Adhesion Biology, Mie University Graduate School of Medicine, Mie, 5148507, Japan
| | - Ryo Esumi
- Emergency & Critical Care Center, Mie University Hospital, Mie, 5148507, Japan.,Department of Emergency & Disaster Medicine, Mie University Graduate School of Medicine, Mie, 5148507, Japan.,Department of Molecular Pathobiology & Cell Adhesion Biology, Mie University Graduate School of Medicine, Mie, 5148507, Japan
| | - Hiroshi Imai
- Emergency & Critical Care Center, Mie University Hospital, Mie, 5148507, Japan.,Department of Emergency & Disaster Medicine, Mie University Graduate School of Medicine, Mie, 5148507, Japan
| | - Motomu Shimaoka
- Department of Molecular Pathobiology & Cell Adhesion Biology, Mie University Graduate School of Medicine, Mie, 5148507, Japan
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42
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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.
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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
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43
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Linhares CDG, Ponciano JR, Paiva JGS, Travençolo BAN, Rocha LEC. A comparative analysis for visualizing the temporal evolution of contact networks: a user study. J Vis (Tokyo) 2021. [DOI: 10.1007/s12650-021-00759-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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44
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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.
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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
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45
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Vicente R, Mohamed Y, Eguíluz VM, Zemmar E, Bayer P, Neimat JS, Hernesniemi J, Nelson BJ, Zemmar A. Modelling the Impact of Robotics on Infectious Spread Among Healthcare Workers. Front Robot AI 2021; 8:652685. [PMID: 34113657 PMCID: PMC8185357 DOI: 10.3389/frobt.2021.652685] [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: 01/12/2021] [Accepted: 05/07/2021] [Indexed: 12/20/2022] Open
Abstract
The Coronavirus disease 2019 (Covid-19) pandemic has brought the world to a standstill. Healthcare systems are critical to maintain during pandemics, however, providing service to sick patients has posed a hazard to frontline healthcare workers (HCW) and particularly those caring for elderly patients. Various approaches are investigated to improve safety for HCW and patients. One promising avenue is the use of robots. Here, we model infectious spread based on real spatio-temporal precise personal interactions from a geriatric unit and test different scenarios of robotic integration. We find a significant mitigation of contamination rates when robots specifically replace a moderate fraction of high-risk healthcare workers, who have a high number of contacts with patients and other HCW. While the impact of robotic integration is significant across a range of reproductive number R0, the largest effect is seen when R0 is slightly above its critical value. Our analysis suggests that a moderate-sized robotic integration can represent an effective measure to significantly reduce the spread of pathogens with Covid-19 transmission characteristics in a small hospital unit.
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Affiliation(s)
- Raul Vicente
- Department of Neurosurgery, Henan Provincial People's Hospital, Henan University People's Hospital, Henan University School of Medicine, Zhengzhou, China.,Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Youssef Mohamed
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Victor M Eguíluz
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain
| | - Emal Zemmar
- Department of Neurosurgery, University of Louisville, School of Medicine, Louisville, KY, United States
| | - Patrick Bayer
- Department of Neurosurgery, University of Louisville, School of Medicine, Louisville, KY, United States
| | - Joseph S Neimat
- Department of Neurosurgery, University of Louisville, School of Medicine, Louisville, KY, United States
| | - Juha Hernesniemi
- Department of Neurosurgery, Henan Provincial People's Hospital, Henan University People's Hospital, Henan University School of Medicine, Zhengzhou, China
| | - Bradley J Nelson
- Multi-Scale Robotics Laboratory, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - Ajmal Zemmar
- Department of Neurosurgery, Henan Provincial People's Hospital, Henan University People's Hospital, Henan University School of Medicine, Zhengzhou, China.,Department of Neurosurgery, University of Louisville, School of Medicine, Louisville, KY, United States
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46
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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.
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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:
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Kim H, Jo HH, Jeong H. Impact of environmental changes on the dynamics of temporal networks. PLoS One 2021; 16:e0250612. [PMID: 33909631 PMCID: PMC8081251 DOI: 10.1371/journal.pone.0250612] [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: 08/17/2020] [Accepted: 04/10/2021] [Indexed: 11/20/2022] Open
Abstract
Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools. By devising and studying a temporal network model considering a periodically changing environment as well as a preferential activation mechanism, we numerically show that our model could successfully reproduce various empirical results by the MSE method in terms of multiscale temporal correlations. Our results demonstrate that the environmental changes can play an important role in shaping the dynamics of temporal networks when the interactions between nodes are influenced by the environment of the systems.
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Affiliation(s)
- Hyewon Kim
- Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea
| | - Hang-Hyun Jo
- Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea
- Department of Physics, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Hawoong Jeong
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- Center for Complex Systems, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- * E-mail:
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Reorganization of nurse scheduling reduces the risk of healthcare associated infections. Sci Rep 2021; 11:7393. [PMID: 33795708 PMCID: PMC8016903 DOI: 10.1038/s41598-021-86637-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 03/18/2021] [Indexed: 11/21/2022] Open
Abstract
Efficient prevention and control of healthcare associated infections (HAIs) is still an open problem. Using contact data from wearable sensors at a short-stay geriatric ward, we propose a proof-of-concept modeling study that reorganizes nurse schedules for efficient infection control. This strategy switches and reassigns nurses’ tasks through the optimization of shift timelines, while respecting feasibility constraints and satisfying patient-care requirements. Through a Susceptible-Colonized-Susceptible transmission model, we found that schedules reorganization reduced HAI risk by 27% (95% confidence interval [24, 29]%) while preserving timeliness, number, and duration of contacts. More than 30% nurse-nurse contacts should be avoided to achieve an equivalent reduction through simple contact removal. Nurse scheduling can be reorganized to break potential chains of transmission and substantially limit HAI risk, while ensuring the timeliness and quality of healthcare services. This calls for including optimization of nurse scheduling practices in programs for infection control in hospitals.
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Cencetti G, Battiston F, Lepri B, Karsai M. Temporal properties of higher-order interactions in social networks. Sci Rep 2021; 11:7028. [PMID: 33782492 PMCID: PMC8007734 DOI: 10.1038/s41598-021-86469-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 03/08/2021] [Indexed: 12/25/2022] Open
Abstract
Human social interactions in local settings can be experimentally detected by recording the physical proximity and orientation of people. Such interactions, approximating face-to-face communications, can be effectively represented as time varying social networks with links being unceasingly created and destroyed over time. Traditional analyses of temporal networks have addressed mostly pairwise interactions, where links describe dyadic connections among individuals. However, many network dynamics are hardly ascribable to pairwise settings but often comprise larger groups, which are better described by higher-order interactions. Here we investigate the higher-order organizations of temporal social networks by analyzing five publicly available datasets collected in different social settings. We find that higher-order interactions are ubiquitous and, similarly to their pairwise counterparts, characterized by heterogeneous dynamics, with bursty trains of rapidly recurring higher-order events separated by long periods of inactivity. We investigate the evolution and formation of groups by looking at the transition rates between different higher-order structures. We find that in more spontaneous social settings, group are characterized by slower formation and disaggregation, while in work settings these phenomena are more abrupt, possibly reflecting pre-organized social dynamics. Finally, we observe temporal reinforcement suggesting that the longer a group stays together the higher the probability that the same interaction pattern persist in the future. Our findings suggest the importance of considering the higher-order structure of social interactions when investigating human temporal dynamics.
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Affiliation(s)
- Giulia Cencetti
- Mobs Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Trento, Italy
| | - Federico Battiston
- Department of Network and Data Science, Central European University, 1100, Vienna, Austria
| | - Bruno Lepri
- Mobs Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Trento, Italy
| | - Márton Karsai
- Department of Network and Data Science, Central European University, 1100, Vienna, Austria.
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