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Li CY, Yin J, Chen L. Impact of social distancing on disease transmission risk in the context of a pandemic. Phys Rev E 2023; 108:054115. [PMID: 38115525 DOI: 10.1103/physreve.108.054115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/12/2023] [Indexed: 12/21/2023]
Abstract
Changes in pedestrian dynamics caused by social distancing policies place new demands on pedestrian motion modeling during the pandemic. This study summarizes pedestrian movement characteristics during the pandemic, based on which, the traditional floor-field cellular automata model was improved by introducing two floor fields related to pedestrian density to simulate social distancing in crowded places. Especially, the cumulative density field guides pedestrians in route selection, thereby compensating for the limitation of the previous models in which only local repulsion was considered. By selecting an appropriate combination of parameters, the desired social distancing behavior can be observed. Then, the rationality of our model is verified by the fundamental diagram. Moreover, to assess the influences of social distancing on the risk of disease transmission, we considered both person-person transmission and environment-person transmission. The simulation results show that although social distancing is effective in preventing interpersonal transmission, an increase in environmental transmission may somewhat offset this effect. We also examined the influence of individual motion heterogeneity on infection spread and found that the containment was the best when only patients complied with the social distancing restriction. The trade-off between safety and efficiency associated with social distancing was also initially explored in this study.
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Affiliation(s)
- Chuan-Yao Li
- School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
| | - Jie Yin
- School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
| | - Liang Chen
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
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2
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Bregar K. Indoor UWB Positioning and Position Tracking Data Set. Sci Data 2023; 10:744. [PMID: 37884571 PMCID: PMC10603152 DOI: 10.1038/s41597-023-02639-5] [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: 03/17/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Indoor positioning has become a hot topic in various fields, such as industry, healthcare, and commerce. Ultra-wideband (UWB) radio technology provides a cost-effective solution for range-based positioning, offering exceptionally high accuracy and precision. Its ultra-high temporal resolution enables range measurements with accuracy of a few centimeters. To develop and evaluate range-based positioning systems, we collected measurements in four different indoor environments using eight fixed devices and one mobile positioning device. To eliminate the fluctuation of walking speed from the data, we pre-defined a path in each indoor environment, similar to the human walking path, which was sampled at equidistant positions. We collected multiple range measurements and channel impulse response (CIR) data at each tag position on the path. The resulting dataset supports the development of range-based positioning and position tracking algorithms with various combinations of network topologies and anchor-tag combinations. We have also provided a full set of data analysis tools that enable the reproducibility of results and serve as a basis for further development of range-based UWB positioning algorithms.
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Affiliation(s)
- Klemen Bregar
- Institut Jožef Stefan, Department of Communication Systems, Ljubljana, 1000, Slovenia.
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3
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Epton T, Ghio D, Ballard LM, Allen SF, Kassianos AP, Hewitt R, Swainston K, Fynn WI, Rowland V, Westbrook J, Jenkinson E, Morrow A, McGeechan GJ, Stanescu S, Yousuf AA, Sharma N, Begum S, Karasouli E, Scanlan D, Shorter GW, Arden MA, Armitage CJ, O'Connor DB, Kamal A, McBride E, Swanson V, Hart J, Byrne-Davis L, Chater A, Drury J. Interventions to promote physical distancing behaviour during infectious disease pandemics or epidemics: A systematic review. Soc Sci Med 2022; 303:114946. [PMID: 35605431 PMCID: PMC8957361 DOI: 10.1016/j.socscimed.2022.114946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Physical distancing, defined as keeping 1-2m apart when co-located, can prevent cases of droplet or aerosol transmitted infectious diseases such as SARS-CoV2. During the COVID-19 pandemic, distancing was a recommendation or a requirement in many countries. This systematic review aimed to determine which interventions and behavior change techniques (BCTs) are effective in promoting adherence to distancing and through which potential mechanisms of action (MOAs). METHODS Six databases were searched. The review included studies that were (a) conducted on humans, (b) reported physical distancing interventions, (c) included any comparator (e.g., pre-intervention versus post-intervention; randomized controlled trial), and (d) reported actual distancing or predictors of distancing behavior. Risk of bias was assessed using the Mixed Methods Appraisal Tool. BCTs and potential MoAs were identified in each intervention. RESULTS Six articles (with seven studies and 19 comparisons) indicated that distancing interventions could successfully change MoAs and behavior. Successful BCTs (MoAs) included feedback on behavior (e.g., motivation); information about health consequences, salience of health consequences (e.g., beliefs about consequences), demonstration (e.g., beliefs about capabilities), and restructuring the physical environment (e.g., environmental context and resources). The most promising interventions were proximity buzzers, directional systems, and posters with loss-framed messages that demonstrated the behaviors. CONCLUSIONS The evidence indicates several BCTs and potential MoAs that should be targeted in interventions and highlights gaps that should be the focus of future research.
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Affiliation(s)
- Tracy Epton
- Manchester Centre for Health Psychology, University of Manchester, UK,Corresponding author. Manchester Centre for Health Psychology, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Daniela Ghio
- Manchester Centre for Health Psychology, University of Manchester, UK
| | | | - Sarah F. Allen
- School of Social Sciences, Humanities and Law, Teesside University, UK
| | | | | | - Katherine Swainston
- Psychology, Centre for Applied Psychological Science, Teesside University, UK
| | | | | | | | - Elizabeth Jenkinson
- Faculty of Health and Applied Sciences, University of the West of England, Bristol, UK
| | | | | | - Sabina Stanescu
- School of Psychology, University of Southampton, Southampton, UK
| | | | - Nisha Sharma
- Department of Clinical Health Psychology, Royal National Orthopaedic Hospital, UK
| | - Suhana Begum
- Department of Psychology, City University of London, UK,Surrey County Council, UK
| | | | - Daniel Scanlan
- Research and Communication, Education Support, London, N5 1EW, UK
| | - Gillian W. Shorter
- Centre for Improving Health Related Quality of Life, Queen's University Belfast, UK
| | - Madelynne A. Arden
- Centre for Behavioural Science and Applied Psychology, Sheffield Hallam University, UK
| | - Christopher J. Armitage
- Manchester Centre for Health Psychology, University of Manchester, UK,Manchester University NHS Foundation Trust, University of Manchester, UK,Manchester Academic Health Science Centre, University of Manchester, UK,NIHR Greater Manchester Patient Safety Translational Research Centre, University of Manchester, UK
| | | | - Atiya Kamal
- Department of Psychology, Birmingham City University, UK
| | - Emily McBride
- Department of Behavioural Science and Health, University College London, UK
| | | | - Jo Hart
- Manchester Centre for Health Psychology, University of Manchester, UK,Division of Medical Education, University of Manchester, UK
| | - Lucie Byrne-Davis
- Manchester Centre for Health Psychology, University of Manchester, UK,Division of Medical Education, University of Manchester, UK
| | | | - John Drury
- School of Psychology, University of Sussex, UK
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4
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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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5
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Dekker MM, Blanken TF, Dablander F, Ou J, Borsboom D, Panja D. Quantifying agent impacts on contact sequences in social interactions. Sci Rep 2022; 12:3483. [PMID: 35241710 PMCID: PMC8894368 DOI: 10.1038/s41598-022-07384-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/10/2022] [Indexed: 01/12/2023] Open
Abstract
Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions—since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time—analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called contact sequence centrality, which quantifies the impact of an individual on the contact sequences, reflecting the individual’s behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential ‘behavioral super-spreaders’. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.
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Affiliation(s)
- Mark M Dekker
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands. .,Centre for Complex Systems Studies, Utrecht University, Minnaertgebouw, Leuvenlaan 4, 3584 CE, Utrecht, The Netherlands.
| | - Tessa F Blanken
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 VZ, Amsterdam, The Netherlands
| | - Fabian Dablander
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 VZ, Amsterdam, The Netherlands
| | - Jiamin Ou
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands.,Department of Sociology, Utrecht University, Padualaan 14, 3584 CH, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 VZ, Amsterdam, The Netherlands
| | - Debabrata Panja
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands.,Centre for Complex Systems Studies, Utrecht University, Minnaertgebouw, Leuvenlaan 4, 3584 CE, Utrecht, The Netherlands
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Promoting physical distancing during COVID-19: a systematic approach to compare behavioral interventions. Sci Rep 2021; 11:19463. [PMID: 34593931 PMCID: PMC8484546 DOI: 10.1038/s41598-021-98964-z] [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: 03/08/2021] [Accepted: 09/13/2021] [Indexed: 12/24/2022] Open
Abstract
In the wake of the COVID-19 pandemic, physical distancing behavior turned out to be key to mitigating the virus spread. Therefore, it is crucial that we understand how we can successfully alter our behavior and promote physical distancing. We present a framework to systematically assess the effectiveness of behavioral interventions to stimulate physical distancing. In addition, we demonstrate the feasibility of this framework in a large-scale natural experiment (N = 639) conducted during an art fair. In an experimental design, we varied interventions to evaluate the effect of face masks, walking directions, and immediate feedback on visitors' contacts. We represent visitors as nodes, and their contacts as links in a contact network. Subsequently, we used network modelling to test for differences in these contact networks. We find no evidence that face masks influence physical distancing, while unidirectional walking directions and buzzer feedback do positively impact physical distancing. This study offers a feasible way to optimize physical distancing interventions through scientific research. As such, the presented framework provides society with the means to directly evaluate interventions, so that policy can be based on evidence rather than conjecture.
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