1
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Zhong Z, Takayasu H, Takayasu M. Human mobility description by physical analogy of electric circuit network based on GPS data. Sci Rep 2024; 14:13380. [PMID: 38862614 PMCID: PMC11167031 DOI: 10.1038/s41598-024-63719-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 05/31/2024] [Indexed: 06/13/2024] Open
Abstract
Human mobility in an urban area is complicated; the origins, destinations, and transportation modes of each person differ. The quantitative description of urban human mobility has recently attracted the attention of researchers, and it highly related to urban science problems. Herein, combined with physics inspiration, we introduce a revised electric circuit model (RECM) in which moving people are regarded as charged particles and analogical concepts of electromagnetism such as human conductivity and human potential enable us to capture the characteristics of urban human mobility. We introduce the unit system, ensure the uniqueness of the calculation result, and reduce the computation cost of the algorithm to 1/10,000 compared with the original ECM, making the model more universal and easier to use. We compared features including human conductivity and potential between different major cities in Japan to show our improvement of the universality and the application range of the model. Furthermore, based on inspiration of physics, we propose a route generation model (RGM) to simulate a human flow pattern that automatically determines suitable routes between a given origin and destination as a source and sink, respectively. These discoveries are expected to lead to new approaches to the solution of urban science problems.
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Affiliation(s)
- Zhihua Zhong
- School of Computing, Tokyo Institute of Technology, 4259, Nagatsutachō, Midori Ward, Yokohama, 226-8503, Japan
| | - Hideki Takayasu
- School of Computing, Tokyo Institute of Technology, 4259, Nagatsutachō, Midori Ward, Yokohama, 226-8503, Japan
- Sony Computer Science Laboratories, Tokyo, Japan
| | - Misako Takayasu
- School of Computing, Tokyo Institute of Technology, 4259, Nagatsutachō, Midori Ward, Yokohama, 226-8503, Japan.
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2
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Mittal KM, Timme M, Schröder M. Efficient self-organization of informal public transport networks. Nat Commun 2024; 15:4910. [PMID: 38851756 PMCID: PMC11162447 DOI: 10.1038/s41467-024-49193-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 05/27/2024] [Indexed: 06/10/2024] Open
Abstract
The Global South, encompassing more than 80% of the world population, heavily relies on informal paratransit services with ad-hoc routes. Yet, it remains unclear how efficiently such informal public transport services organize and operate. Here, we analyze and compare the structural efficiency of more than 7000 formal and informal bus service routes in 36 cities across 22 countries globally. Intriguingly, informal transport self-organizes in ways at or above efficiency levels of centralized services. They exhibit fewer detours, more uniform paths, and comparable interconnectivities, all while remaining profitable without the major subsidies common in the Global North. These insights challenge the global perception of informal transport as an inferior alternative to centrally organized services. More generally, analyzing large-scale microscopic transport data and condensing them into informative macroscopic observables may qualitatively improve system understanding and reveal specific options to create more accessible, efficient, and sustainable public transport solutions.
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Affiliation(s)
- Kush Mohan Mittal
- Chair of Network Dynamics, Institute of Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), TUD Dresden University of Technology, Dresden, Germany
| | - Marc Timme
- Chair of Network Dynamics, Institute of Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), TUD Dresden University of Technology, Dresden, Germany
- Lakeside Labs, Klagenfurt, Austria
| | - Malte Schröder
- Chair of Network Dynamics, Institute of Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), TUD Dresden University of Technology, Dresden, Germany.
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3
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You L, Zhu R, Kwan MP, Chen M, Zhang F, Yang B, Wong MS, Qin Z. Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning. Innovation (N Y) 2024; 5:100587. [PMID: 38426200 PMCID: PMC10901825 DOI: 10.1016/j.xinn.2024.100587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Affiliation(s)
- Linlin You
- School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Rui Zhu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A∗STAR), 1 Fusionopolis Way, Singapore 138632, Republic of Singapore
| | - Mei-Po Kwan
- Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing 210023, China
| | - Fan Zhang
- Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
| | - Bisheng Yang
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Man Sing Wong
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zheng Qin
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A∗STAR), 1 Fusionopolis Way, Singapore 138632, Republic of Singapore
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4
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John RS, Miller JC, Muylaert RL, Hayman DTS. High connectivity and human movement limits the impact of travel time on infectious disease transmission. J R Soc Interface 2024; 21:20230425. [PMID: 38196378 PMCID: PMC10777149 DOI: 10.1098/rsif.2023.0425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
The speed of spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the importance of understanding how infections are transmitted in a highly connected world. Prior to vaccination, changes in human mobility patterns were used as non-pharmaceutical interventions to eliminate or suppress viral transmission. The rapid spread of respiratory viruses, various intervention approaches, and the global dissemination of SARS-CoV-2 underscore the necessity for epidemiological models that incorporate mobility to comprehend the spread of the virus. Here, we introduce a metapopulation susceptible-exposed-infectious-recovered model parametrized with human movement data from 340 cities in China. Our model replicates the early-case trajectory in the COVID-19 pandemic. We then use machine learning algorithms to determine which network properties best predict spread between cities and find travel time to be most important, followed by the human movement-weighted personalized PageRank. However, we show that travel time is most influential locally, after which the high connectivity between cities reduces the impact of travel time between individual cities on transmission speed. Additionally, we demonstrate that only significantly reduced movement substantially impacts infection spread times throughout the network.
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Affiliation(s)
- Reju Sam John
- Massey University, Palmerston North 4474, New Zealand
- University of Auckland, Auckland 1010, New Zealand
| | - Joel C. Miller
- La Trobe University, Melbourne 3086, Victoria, Australia
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5
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Klamser PP, Zachariae A, Maier BF, Baranov O, Jongen C, Schlosser F, Brockmann D. Inferring country-specific import risk of diseases from the world air transportation network. PLoS Comput Biol 2024; 20:e1011775. [PMID: 38266041 PMCID: PMC10843136 DOI: 10.1371/journal.pcbi.1011775] [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: 05/03/2023] [Revised: 02/05/2024] [Accepted: 12/21/2023] [Indexed: 01/26/2024] Open
Abstract
Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.
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Affiliation(s)
- Pascal P. Klamser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Adrian Zachariae
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Benjamin F. Maier
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
- Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Olga Baranov
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Clara Jongen
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Frank Schlosser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Dirk Brockmann
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- Center Synergy of Systems (SynoSys), Center for Interdisciplinary Digital Sciences, Technische Universität Dresden, Dresden, Germany
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6
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Duan J, Zeng G, Serok N, Li D, Lieberthal EB, Huang HJ, Havlin S. Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions. Nat Commun 2023; 14:8002. [PMID: 38049413 PMCID: PMC10695996 DOI: 10.1038/s41467-023-43591-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 11/14/2023] [Indexed: 12/06/2023] Open
Abstract
Heavy traffic jams are difficult to predict due to the complexity of traffic dynamics. Understanding the network dynamics of traffic bottlenecks can help avoid critical large traffic jams and improve overall traffic conditions. Here, we develop a method to forecast heavy congestions based on their early propagation stage. Our framework follows the network propagation and dissipation of the traffic jams originated from a bottleneck emergence, growth, and its recovery and disappearance. Based on large-scale urban traffic-speed data, we find that dissipation duration of jams follows approximately power-law distributions, and typically, traffic jams dissolve nearly twice slower than their growth. Importantly, we find that the growth speed, even at the first 15 minutes of a jam, is highly correlated with the maximal size of the jam. Our methodology can be applied in urban traffic control systems to forecast heavy traffic bottlenecks and prevent them before they propagate to large network congestions.
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Grants
- This work was supported by the National Natural Science Foundation of China (Grants 71890971/71890970, H-J.H.; 72225012, D.L.; 72288101, H-J.H. and D.L.; 71822101, D.L.; and 71890973/71890970, D.L.), the Fundamental Research Funds for the Central Universities (D.L.), the Israel Science Foundation (Grant No. 189/19, S.H.), the Binational Israel-China Science Foundation (Grant No. 3132/19, S.H.), and the European Union’s Horizon 2020 research and innovation programme (DIT4Tram, Grant Agreement 953783, S.H. and E.B.L.).
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Affiliation(s)
- Jinxiao Duan
- School of Economics and Management, Beihang University, Beijing, 100191, China
- Department of Physics, Bar-Ilan University, Ramat Gan, 52900, Israel
| | - Guanwen Zeng
- Department of Physics, Bar-Ilan University, Ramat Gan, 52900, Israel
- School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
| | - Nimrod Serok
- Azrieli School of Architecture, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Daqing Li
- School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
| | | | - Hai-Jun Huang
- School of Economics and Management, Beihang University, Beijing, 100191, China.
| | - Shlomo Havlin
- Department of Physics, Bar-Ilan University, Ramat Gan, 52900, Israel.
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7
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Nilforoshan H, Looi W, Pierson E, Villanueva B, Fishman N, Chen Y, Sholar J, Redbird B, Grusky D, Leskovec J. Human mobility networks reveal increased segregation in large cities. Nature 2023; 624:586-592. [PMID: 38030732 PMCID: PMC10733138 DOI: 10.1038/s41586-023-06757-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 10/17/2023] [Indexed: 12/01/2023]
Abstract
A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1-6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city's hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.
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Affiliation(s)
- Hamed Nilforoshan
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Wenli Looi
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, NY, USA
| | - Blanca Villanueva
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Nic Fishman
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Yiling Chen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - John Sholar
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Beth Redbird
- Institute for Policy Research, Northwestern University, Evanston, IL, USA
- Department of Sociology, Northwestern University, Evanston, IL, USA
| | - David Grusky
- Department of Sociology, Stanford University, Stanford, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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8
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Liu P, Zheng Y. Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space. PLoS One 2023; 18:e0294445. [PMID: 37988387 PMCID: PMC10662771 DOI: 10.1371/journal.pone.0294445] [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: 05/05/2023] [Accepted: 10/31/2023] [Indexed: 11/23/2023] Open
Abstract
This paper conducts a systematic statistical analysis of the characteristics of the geographical empirical distributions for the numbers of both cumulative and daily confirmed COVID-19 cases and deaths at county, city, and state levels over a time span from January 2020 to June 2022. The mathematical heavy-tailed distributions can be used for fitting the empirical distributions observed in different temporal stages and geographical scales. The estimations of the shape parameter of the tail distributions using the Generalized Pareto Distribution also support the observations of the heavy-tailed distributions. According to the characteristics of the heavy-tailed distributions, the evolution course of the geographical empirical distributions can be divided into three distinct phases, namely the power-law phase, the lognormal phase I, and the lognormal phase II. These three phases could serve as an indicator of the severity degree of the COVID-19 pandemic within an area. The empirical results suggest important intrinsic dynamics of a human infectious virus spread in the human interconnected physical complex network. The findings extend previous empirical studies and could provide more strict constraints for current mathematical and physical modeling studies, such as the SIR model and its variants based on the theory of complex networks.
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Affiliation(s)
- Peng Liu
- School of Information, Xi’an University of Finance and Economics, Xi’an, Shaanxi, P. R. China
| | - Yanyan Zheng
- School of Management, Xi’an Polytechnic University, Xi’an, Shaanxi, P. R. China
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9
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Sheng A, Su Q, Li A, Wang L, Plotkin JB. Constructing temporal networks with bursty activity patterns. Nat Commun 2023; 14:7311. [PMID: 37951967 PMCID: PMC10640578 DOI: 10.1038/s41467-023-42868-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time, namely the duration between successive pairwise interactions. Empirical studies have found inter-event time distributions that are heavy-tailed, for both physical and digital interactions. But it is difficult to construct theoretical models of time-varying activity on a network that reproduce the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures any desired target inter-event time distributions for individual nodes and links, provided the distributions fulfill a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions.
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Affiliation(s)
- Anzhi Sheng
- Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China
- Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qi Su
- Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
- Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai, 200240, China
| | - Aming Li
- Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| | - Long Wang
- Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.
- Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China.
| | - Joshua B Plotkin
- Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Mathematical Biology, University of Pennsylvania, Philadelphia, PA, 19014, USA.
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10
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Chen Y, Xu H, Chen X(M, Gao Z. A multi-scale unified model of human mobility in urban agglomerations. PATTERNS (NEW YORK, N.Y.) 2023; 4:100862. [PMID: 38035194 PMCID: PMC10682749 DOI: 10.1016/j.patter.2023.100862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/01/2023] [Accepted: 09/19/2023] [Indexed: 12/02/2023]
Abstract
Understanding human mobility patterns is vital for the coordinated development of cities in urban agglomerations. Existing mobility models can capture single-scale travel behavior within or between cities, but the unified modeling of multi-scale human mobility in urban agglomerations is still analytically and computationally intractable. In this study, by simulating people's mental representations of physical space, we decompose and model the human travel choice process as a cascaded multi-class classification problem. Our multi-scale unified model, built upon cascaded deep neural networks, can predict human mobility in world-class urban agglomerations with thousands of regions. By incorporating individual memory features and population attractiveness features extracted by a graph generative adversarial network, our model can simultaneously predict multi-scale individual and population mobility patterns within urban agglomerations. Our model serves as an exemplar framework for reproducing universal-scale laws of human mobility across various spatial scales, providing vital decision support for urban settings of urban agglomerations.
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Affiliation(s)
- Yong Chen
- Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
| | - Haoge Xu
- Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
| | - Xiqun (Michael) Chen
- Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
- Zhejiang University/University of Illinois Urbana-Champaign (ZJU-UIUC) Institute, Haining 314400, China
| | - Ziyou Gao
- School of Systems Science, Beijing Jiaotong University, Beijing 100044, China
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11
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Raulo A, Rojas A, Kröger B, Laaksonen A, Orta CL, Nurmio S, Peltoniemi M, Lahti L, Žliobaitė I. What are patterns of rise and decline? ROYAL SOCIETY OPEN SCIENCE 2023; 10:230052. [PMID: 38026026 PMCID: PMC10646453 DOI: 10.1098/rsos.230052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023]
Abstract
The notions of change, such as birth, death, growth, evolution and longevity, extend across reality, including biological, cultural and societal phenomena. Patterns of change describe how success and composition of every entity, from species to societies, vary across time. Languages develop into new languages, music and fashion continuously evolve, economies rise and decline, ecological and societal crises come and go. A common way to perceive and analyse change processes is through patterns of rise and decline, the ubiquitous, often distinctively unimodal trajectories describing life histories of various entities. These patterns come in different shapes and are measured according to varying definitions. Depending on how they are measured, patterns of rise and decline can reveal, emphasize, mask or obscure important dynamics in natural and cultural phenomena. Importantly, the variations of how dynamics are measured can be vast, making it impossible to directly compare patterns of rise and decline across fields of science. Standardized analysis of these patterns has the potential to uncover important but overlooked commonalities across natural phenomena and potentially help us catch the onset of dramatic shifts in entities' state, from catastrophic crashes in success to gradual emergence of new entities. We provide a framework for standardized recognizing, characterizing and comparing patterns of change by combining understanding of dynamics across fields of science. Our toolkit aims at enhancing understanding of the most general tendencies of change, through two complementary perspectives: dynamics of emergence and dynamics of success. We gather comparable cases and data from different research fields and summarize open research questions that can help us understand the universal principles, perception-biases and field-specific tendencies in patterns of rise and decline of entities in nature.
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Affiliation(s)
- Aura Raulo
- Department of Computing, University of Turku, Turku, Finland
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
| | - Alexis Rojas
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Björn Kröger
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
| | - Antti Laaksonen
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Carlos Lamuela Orta
- Mobility Research Group, VTT Technical Research Centre of Finland, Espoo, Uusimaa, Finland
| | - Silva Nurmio
- Department of Languages, University of Helsinki, Helsinki, Finland
| | - Mirva Peltoniemi
- Department of Industrial Engineering and Management, Tampere University, 33014 Tampere, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Indrė Žliobaitė
- Department of Computer Science, University of Helsinki, Helsinki, Finland
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
- Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
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12
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Liu Z, Liu C, Mostafavi A. Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:15511-15522. [PMID: 37791816 PMCID: PMC10862537 DOI: 10.1021/acs.est.3c04691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/05/2023]
Abstract
Standard environmental hazard exposure assessment methods have been primarily based on residential places, neglecting individuals' hazard exposures due to activities outside home neighborhood and underestimating peoples' overall hazard exposures. To address this limitation, this study proposes a novel mobility-based index for the hazard exposure evaluation. Using large-scale human mobility data, we quantify the extent of population dwell time in high environmental hazard places in 239 US counties for three environmental hazards. We explore how human mobility extends the reach of environmental hazards and leads to the emergence of latent exposure for populations living outside high-hazard areas. Notably, neglect of mobility can lead to over 10% underestimation of hazard exposures. The interplay of spatial clustering in high-hazard regions and human movement trends creates "environmental hazard traps." Poor and ethnic minority residents disproportionately face multiple types of environmental hazards. This data-driven evidence supports the severity of these injustices. We also studied latent exposure arising from visits outside residents' home areas, revealing millions of the population having 5 to 10% of daily activities occur in high-exposure zones. Despite living in perceived safe areas, human mobility could expose millions of residents to different hazards. These findings provide crucial insights for targeted policies to mitigate these severe environmental injustices.
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Affiliation(s)
- Zhewei Liu
- UrbanResilience.AI Lab, Zachry
Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Chenyue Liu
- UrbanResilience.AI Lab, Zachry
Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Ali Mostafavi
- UrbanResilience.AI Lab, Zachry
Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas 77843, United States
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13
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Collyer BS, Truscott JE, Mwandawiro CS, Njenga SM, Anderson RM. How important is the spatial movement of people in attempts to eliminate the transmission of human helminth infections by mass drug administration? Philos Trans R Soc Lond B Biol Sci 2023; 378:20220273. [PMID: 37598700 PMCID: PMC10440163 DOI: 10.1098/rstb.2022.0273] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 04/02/2023] [Indexed: 08/22/2023] Open
Abstract
Human mobility contributes to the spatial dynamics of many infectious diseases, and understanding these dynamics helps us to determine the most effective ways to intervene and plan surveillance. In this paper, we describe a novel transmission model for the spatial dynamics of hookworm, a parasitic worm which is a common infection across sub-Saharan Africa, East Asia and the Pacific islands. We fit our model, with and without mobility, to data obtained from a sub-county in Kenya, and validate the model's predictions against the decline in prevalence observed over the course of a clustered randomized control trial evaluating methods of administering mass chemotherapy. We find that our model which incorporates human mobility is able to reproduce the observed patterns in decline of prevalence during the TUMIKIA trial, and additionally, that the widespread bounce-back of infection may be possible over many years, depending on the rates of people movement between villages. The results have important implications for the design of mass chemotherapy programmes for the elimination of human helminth transmission. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.
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Affiliation(s)
- Benjamin S. Collyer
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London W2 1PG, UK
| | - James E. Truscott
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London W2 1PG, UK
| | | | - Sammy M. Njenga
- Eastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute, Nairobi, Kenya
| | - Roy M. Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, London W2 1PG, UK
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14
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Heine C, O'Keeffe KP, Santi P, Yan L, Ratti C. Travel distance, frequency of return, and the spread of disease. Sci Rep 2023; 13:14064. [PMID: 37640718 PMCID: PMC10462643 DOI: 10.1038/s41598-023-38840-0] [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: 01/17/2023] [Accepted: 07/16/2023] [Indexed: 08/31/2023] Open
Abstract
Human mobility is a key driver of infectious disease spread. Recent literature has uncovered a clear pattern underlying the complexity of human mobility in cities: [Formula: see text], the product of distance traveled r and frequency of return f per user to a given location, is invariant across space. This paper asks whether the invariant [Formula: see text] also serves as a driver for epidemic spread, so that the risk associated with human movement can be modeled by a unifying variable [Formula: see text]. We use two large-scale datasets of individual human mobility to show that there is in fact a simple relation between r and f and both speed and spatial dispersion of disease spread. This discovery could assist in modeling spread of disease and inform travel policies in future epidemics-based not only on travel distance r but also on frequency of return f.
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Affiliation(s)
- Cate Heine
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Kevin P O'Keeffe
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Paolo Santi
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Istituto di Informatica e Telematica del CNR, Pisa, Italy
| | - Li Yan
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Carlo Ratti
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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15
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Tang J, Zhao P, Gong Z, Zhao H, Huang F, Li J, Chen Z, Yu L, Chen J. Resilience patterns of human mobility in response to extreme urban floods. Natl Sci Rev 2023; 10:nwad097. [PMID: 37389148 PMCID: PMC10306362 DOI: 10.1093/nsr/nwad097] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/31/2023] [Accepted: 04/10/2023] [Indexed: 07/01/2023] Open
Abstract
Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented '720' Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing, resilience patterns of human mobility (namely, 'reverse bathtub', 'ever-increasing' and 'ever-decreasing' patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people's gender or age. In view of the common association between travel behaviors and travelers' socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies.
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Affiliation(s)
- Junqing Tang
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
- Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | | | - Zhaoya Gong
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
- Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Hongbo Zhao
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization, Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China
| | - Fengjue Huang
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jiaying Li
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Zhihe Chen
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Ling Yu
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jun Chen
- Key National Geomatics Center of China, Beijing 100830, China
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16
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Gambetta D, Mauro G, Pappalardo L. Mobility constraints in segregation models. Sci Rep 2023; 13:12087. [PMID: 37495661 PMCID: PMC10372033 DOI: 10.1038/s41598-023-38519-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/10/2023] [Indexed: 07/28/2023] Open
Abstract
Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.
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Affiliation(s)
- Daniele Gambetta
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.
- University of Pisa, Pisa, Italy.
| | - Giovanni Mauro
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.
- University of Pisa, Pisa, Italy.
- IMT School for Advanced Studies, Lucca, Italy.
| | - Luca Pappalardo
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy.
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17
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Pappalardo L, Manley E, Sekara V, Alessandretti L. Future directions in human mobility science. NATURE COMPUTATIONAL SCIENCE 2023; 3:588-600. [PMID: 38177737 DOI: 10.1038/s43588-023-00469-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 05/11/2023] [Indexed: 01/06/2024]
Abstract
We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.
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Affiliation(s)
- Luca Pappalardo
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy
| | - Ed Manley
- School of Geography, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
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18
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Wang S, Huang X, She B, Li Z. Diverged landscape of restaurant recovery from the COVID-19 pandemic in the United States. iScience 2023; 26:106811. [PMID: 37197592 PMCID: PMC10156630 DOI: 10.1016/j.isci.2023.106811] [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: 10/10/2022] [Revised: 02/22/2023] [Accepted: 05/01/2023] [Indexed: 05/19/2023] Open
Abstract
The COVID-19 pandemic has imposed catastrophic impacts on the restaurant industry as a crucial socioeconomic sector that contributes to the global economy. However, the understanding of how the restaurant industry was recovered from COVID-19 remains underexplored. This study constructs a spatially explicit evaluation of the effect of COVID-19 on the restaurant industry in the US, drawing on the attributes of +200,000 restaurants from Yelp and +600 million individual-level restaurant visitations provided by SafeGraph from 1st January 2019 to 31st December 2021. We produce quantitative evidence of lost restaurant visitations and revenue amid the pandemic, the changes in the customers' origins, and the retained visitation law of human mobility-the number of restaurant visitations decreases as the inverse square of their travel distances-though such a distance-decay effect becomes marginal at the later pandemic. Our findings support policy makers to monitor economic relief and design place-based policies for economic recovery.
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Affiliation(s)
- Siqin Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD, Australia
- Graduate School of Interdisciplinary Information Studies, University of Tokyo, Tokyo, Japan
- School of Science, Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC, Australia
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, AR, USA
| | - Bing She
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
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19
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Guardabascio B, Brogi F, Benassi F. Measuring human mobility in times of trouble: an investigation of the mobility of European populations during COVID-19 using big data. QUALITY & QUANTITY 2023:1-19. [PMID: 37359960 PMCID: PMC10182752 DOI: 10.1007/s11135-023-01678-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/27/2023] [Indexed: 06/28/2023]
Abstract
Spatial mobility is a distinctive feature of human history and has important repercussions in many aspects of societies. Spatial mobility has always been a subject of interest in many disciplines, even if only mobility observable from traditional sources, namely migration (internal and international) and more recently commuting, is generally studied. However, it is the other forms of mobility, that is, the temporary forms of mobility, that most interest today's societies and, thanks to new data sources, can now be observed and measured. This contribution provides an empirical and data-driven reflection on human mobility during the COVID pandemic crisis. The paper has two main aims: (a) to develop a new index for measuring the attrition in mobility due to the restrictions adopted by governments in order to contain the spread of COVID-19. The robustness of the proposed index is checked by comparing it with the Oxford Stringency Index. The second goal is (b) to test if and how digital footprints (Google data in our case) can be used to measure human mobility. The study considers Italy and all the other European countries. The results show, on the one hand, that the Mobility Restriction Index (MRI) works quite well and, on the other, the sensitivity, in the short term, of human mobility to exogenous shocks and intervention policies; however, the results also show an inner tendency, in the middle term, to return to previous behaviours.
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Affiliation(s)
| | - Federico Brogi
- Italian National Institute of Statistics (ISTAT), Rome, Italy
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20
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Rapti Z, Cuevas-Maraver J, Kontou E, Liu S, Drossinos Y, Kevrekidis PG, Barmann M, Chen QY, Kevrekidis GA. The Role of Mobility in the Dynamics of the COVID-19 Epidemic in Andalusia. Bull Math Biol 2023; 85:54. [PMID: 37166513 PMCID: PMC10173246 DOI: 10.1007/s11538-023-01152-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 03/28/2023] [Indexed: 05/12/2023]
Abstract
Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time-dependent data, but also discussing the comparison for our case example with a gravity model, as well as with the dynamics in the absence of mobility. Our main finding is that mobility is key toward a quantitative understanding of the emergence of the second wave of the pandemic and that the most accurate way to capture it involves dynamic (rather than static) inclusion of time-dependent mobility matrices based on cell-phone data. Alternatives bearing no mobility are unable to capture the trends revealed by the data in the context of the metapopulation model considered herein.
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Affiliation(s)
- Z Rapti
- Department of Mathematics and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Champaign, IL, USA.
| | - J Cuevas-Maraver
- Grupo de Física No Lineal, Departamento de Física Aplicada I, Universidad de Sevilla, Escuela Politécnica Superior, C/ Virgen de Africa, 7, 41011, Seville, Spain
- Instituto de Matemáticas de la Universidad de Sevilla (IMUS), Edificio Celestino Mutis, Avda. Reina Mercedes s/n, 41012, Seville, Spain
| | - E Kontou
- Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - S Liu
- Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Y Drossinos
- Thermal Hydraulics and Multiphase Flow Laboratory, Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, N.C.S.R. "Demokritos", 15341, Agia Paraskevi, Greece
| | - P G Kevrekidis
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003-4515, USA
| | - M Barmann
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003-4515, USA
| | - Q-Y Chen
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003-4515, USA
| | - G A Kevrekidis
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA
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21
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Chen K, Jiang X, Li Y, Zhou R. A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility. NONLINEAR DYNAMICS 2023; 111:1-17. [PMID: 37361002 PMCID: PMC10148626 DOI: 10.1007/s11071-023-08489-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/20/2023] [Indexed: 06/28/2023]
Abstract
The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. Supplementary Information The online version contains supplementary material available at 10.1007/s11071-023-08489-5.
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Affiliation(s)
- Kejie Chen
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Xiaomo Jiang
- Provincial Key Lab of Digital Twin for Industrial Equipment, Dalian, 116024 China
- School of Energy and Power Engineering, Dalian, 116024 China
| | - Yanqing Li
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Rongxin Zhou
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
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22
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Willcock S, Hooftman DA, Neugarten RA, Chaplin-Kramer R, Barredo JI, Hickler T, Kindermann G, Lewis AR, Lindeskog M, Martínez-López J, Bullock JM. Model ensembles of ecosystem services fill global certainty and capacity gaps. SCIENCE ADVANCES 2023; 9:eadf5492. [PMID: 37027474 PMCID: PMC10081842 DOI: 10.1126/sciadv.adf5492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
Sustaining ecosystem services (ES) critical to human well-being is hindered by many practitioners lacking access to ES models ("the capacity gap") or knowledge of the accuracy of available models ("the certainty gap"), especially in the world's poorer regions. We developed ensembles of multiple models at an unprecedented global scale for five ES of high policy relevance. Ensembles were 2 to 14% more accurate than individual models. Ensemble accuracy was not correlated with proxies for research capacity, indicating that accuracy is distributed equitably across the globe and that countries less able to research ES suffer no accuracy penalty. By making these ES ensembles and associated accuracy estimates freely available, we provide globally consistent ES information that can support policy and decision-making in regions with low data availability or low capacity for implementing complex ES models. Thus, we hope to reduce the capacity and certainty gaps impeding local- to global-scale movement toward ES sustainability.
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Affiliation(s)
- Simon Willcock
- Net Zero and Resilient Farming, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK
- School of Natural Sciences, Bangor University, Bangor, Gwenydd LL57 2DG, UK
| | - Danny A. P. Hooftman
- Lactuca: Environmental Data Analyses and Modelling, Diemen, Netherlands
- UK Centre for Ecology and Hydrology, Wallingford OX10 8BB, UK
| | - Rachel A. Neugarten
- Department of Natural Resources and Environment, Cornell University, 226 Mann Drive, Ithaca, NY 14853, USA
- Conservation International, 2100 Crystal Drive #600, Arlington, VA 22202, USA
- Cornell Lab of Ornithology, Cornell University, 159 Sapsucker Woods Rd, Ithaca, NY 14850, USA
| | - Rebecca Chaplin-Kramer
- Global Science, Word Wildlife Fund, 131 Steuart Street, San Francisco, CA 94105, USA
- Institute on the Environment, University of Minnesota, 1954 Buford Ave, St. Paul, MN, 55108, USA
- Natural Capital Project, Stanford University, 327 Campus Drive, Stanford, CA, 94305, USA
| | | | - Thomas Hickler
- Senckenberg Biodiversity and Climate Research Centre, Frankfurt, Germany
- Institute of Physical Geography, Goethe-University, Altenhöferallee 1, 60438 Frankfurt am Main, Germany
| | - Georg Kindermann
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Amy R. Lewis
- School of Natural Sciences, Bangor University, Bangor, Gwenydd LL57 2DG, UK
| | - Mats Lindeskog
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
| | - Javier Martínez-López
- Department of Ecology, University of Granada, Avda. del Mediterráneo s/n, E-18006 Granada, Spain
- Instituto Interuniversitario de Investigación del Sistema Tierra en Andalucía (IISTA), Universidad de Granada, Avda. del Mediterráneo s/n, E-18006 Granada, Spain
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23
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Hua L, Ran R, Ni Z. Are the epidemic prevention facilities effective? How cities should choose epidemic prevention facilities: Taking Wuhan as an example. Front Public Health 2023; 11:1125301. [PMID: 37064702 PMCID: PMC10097902 DOI: 10.3389/fpubh.2023.1125301] [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: 12/16/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
The COVID-19 pandemic highlighted the limitations of urban public health emergency response capabilities. Taking Wuhan as an example, this study used breakpoint regression, kernel density analysis, overlay analysis, and accessibility analysis from Stata and ArcGIS, and divided epidemic prevention facilities into the basic epidemic prevention facilities (hospitals), and the emergency epidemic prevention facilities (mobile cabin hospitals) for further analysis. The results showed that over 70% of the basic epidemic prevention facilities in Wuhan were located in high density population areas. On the contrary, most of the emergency epidemic prevention facilities were located in low density population areas. The local treatment effect of the implementation of the emergency epidemic prevention facility policy is about 1, indicating that there was a significant impact of emergency epidemic prevention facilities on outbreak control, which passed the bandwidth test. What’s more, the analysis of the accessibility of residential points revealed that more than 67.3% of people from the residential points could arrive at the epidemic prevention facilities within 15 min, and only 0.1% of them took more than 20 min to arrive. Therefore, the epidemic prevention facilities can effectively curb the spread of the epidemic, and people from residential areas can quickly get there. This study summarized the spatial characteristics of epidemic prevention facilities in Wuhan and analyzed the importance of them, thus providing a new perspective for future research on upgrading the city’s comprehensive disaster prevention system.
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24
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Makris N, Moghimi G, Godat E, Vu T. Mechanical analogue for cities. ROYAL SOCIETY OPEN SCIENCE 2023; 10:220943. [PMID: 36908989 PMCID: PMC9993048 DOI: 10.1098/rsos.220943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Motivated from the increasing need to develop a science-based, predictive understanding of the dynamics and response of cities when subjected to natural hazards, in this paper, we apply concepts from statistical mechanics and microrheology to develop mechanical analogues for cities with predictive capabilities. We envision a city to be a matrix where cell-phone users are driven by the city's economy and other incentives while using the collection of its infrastructure networks in a similar way that thermally driven Brownian particles are moving within a complex viscoelastic material. Mean-square displacements of thousands of cell-phone users are computed from GPS location data to establish the creep compliance and the resulting impulse response function of a city. The derivation of these time-response functions allows the synthesis of simple mechanical analogues that model satisfactorily the city's behaviour under normal conditions. Our study concentrates on predicting the response of cities to acute shocks (natural hazards) that are approximated with a rectangular pulse; and we show that the derived solid-like mechanical networks predict that cities revert immediately to their pre-event response suggesting an inherent resilience. Our findings are in remarkable good agreement with the recorded response of the Dallas metroplex following the February 2021 North American winter storm.
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Affiliation(s)
- Nicos Makris
- Department of Civil and Environmental Engineering, OIT, Southern Methodist University, Dallas, TX 75276, USA
| | - Gholamreza Moghimi
- Department of Civil and Environmental Engineering, OIT, Southern Methodist University, Dallas, TX 75276, USA
| | - Eric Godat
- Data Science and Research Services, OIT, Southern Methodist University, Dallas, TX 75276, USA
| | - Tue Vu
- Data Science and Research Services, OIT, Southern Methodist University, Dallas, TX 75276, USA
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25
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Galesic M, Barkoczi D, Berdahl AM, Biro D, Carbone G, Giannoccaro I, Goldstone RL, Gonzalez C, Kandler A, Kao AB, Kendal R, Kline M, Lee E, Massari GF, Mesoudi A, Olsson H, Pescetelli N, Sloman SJ, Smaldino PE, Stein DL. Beyond collective intelligence: Collective adaptation. J R Soc Interface 2023; 20:20220736. [PMID: 36946092 PMCID: PMC10031425 DOI: 10.1098/rsif.2022.0736] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
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Affiliation(s)
- Mirta Galesic
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Complexity Science Hub Vienna, 1080 Vienna, Austria
- Vermont Complex Systems Center, University of Vermont, Burlington, VM 05405, USA
| | | | - Andrew M. Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
| | - Dora Biro
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Giuseppe Carbone
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Ilaria Giannoccaro
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Robert L. Goldstone
- Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Cleotilde Gonzalez
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Anne Kandler
- Department of Mathematics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Albert B. Kao
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Biology Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Rachel Kendal
- Centre for Coevolution of Biology and Culture, Durham University, Anthropology Department, Durham, DH1 3LE, UK
| | - Michelle Kline
- Centre for Culture and Evolution, Division of Psychology, Brunel University London, Uxbridge, UB8 3PH, UK
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, Republic of Korea
| | | | - Alex Mesoudi
- Department of Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK
| | | | | | - Sabina J. Sloman
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK
| | - Paul E. Smaldino
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Cognitive and Information Sciences, University of California, Merced, CA 95343, USA
| | - Daniel L. Stein
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
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26
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A maximum entropy approach for the modelling of car-sharing parking dynamics. Sci Rep 2023; 13:2993. [PMID: 36810881 PMCID: PMC9945450 DOI: 10.1038/s41598-023-30134-9] [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: 10/03/2022] [Accepted: 02/16/2023] [Indexed: 02/23/2023] Open
Abstract
The science of cities is a relatively new and interdisciplinary topic aimed at studying and characterizing the collective processes that shape the growth and dynamics of urban populations. Amongst other open problems, the forecast of mobility trends in urban spaces is a lively research topic that aims at assisting the design and implementation of efficient transportation policies and inclusive urban planning. To this end, many Machine-Learning models have been put forward to predict mobility patterns. However, most of them are not interpretable -as they build on complex hidden representations of the system configurations- or do not allow for model inspection, thus limiting our understanding of the underlying mechanisms driving the citizen's daily routines. Here, we tackle this problem by building a fully interpretable statistical model that, incorporating only the minimum number of constraints, can predict different phenomena arising in the city. Using data on the movements of car-sharing vehicles in several Italian cities, we infer a model using the Maximum Entropy (MaxEnt) principle. The model allows for an accurate spatio-temporal prediction of car-sharing vehicles' presence in different city areas and, thanks to its simple yet general formulation, to precisely perform anomaly detection (e.g., detect strikes and bad weather conditions from car-sharing data only). We compare the forecasting capabilities of our model with different state-of-the-art models explicitly made for time-series forecasting: SARIMA models and Deep Learning Models. We find that MaxEnt models are highly predictive, outperforming SARIMAs while having similar performances of deep Neural Networks - but with advantages of being more interpretable, more flexibile-i.e., they can be applied to different tasks- and being computationally efficient. Our results show that statistical inference might play a fundamental role in building robust and general models describing urban systems phenomena.
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27
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Yang C, Zhao S. Scaling of Chinese urban CO 2 emissions and multiple dimensions of city size. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159502. [PMID: 36265639 DOI: 10.1016/j.scitotenv.2022.159502] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Cities are both the primary cause of global climate change and the key to the mitigation agenda. China's unprecedented urbanization has paralleled a growth in energy demand and urban areas have emerged as the crux of CO2 emissions reduction in China. There is a crucial need for policymakers to understand how CO2 emissions scale with city size and adopt economies of scale (cost savings) for mitigation, particularly through a multidimensional lens of city size. This study reveals a set of scaling relations between urban scope 1 CO2 emissions and five dimensions of city size in 340 Chinese cities, including population (POP), built-up area (BA), building height (BH), specific built-up area (SBA), and built-up volume (BV). The findings show that CO2 emissions in Chinese cities scale linearly with POP and BA but sublinearly with BA, SBA, and BV, and more diverse regimes exist across various geographic zones, population hierarchies, administrative hierarchies, and governance contexts. The prevalent sublinear scaling regime between CO2 emissions and SBA and BV demonstrates the potential importance of optimizing the vertical built-up landscapes for establishing a zero‑carbon society. Furthermore, the top 10 % and bottom 10 % performance of individual cities in emissions identified by the Scale-Adjusted Metropolitan Indicator (SAMI) (the smaller the better) highlights the imprints of the socioeconomic context (e.g., Low Carbon City Initiative) on the scaling of CO2 emissions in Chinese cities, which is critical for developing decarbonization strategies. Our multidimensional analysis can assist in the local-tailored low-carbon development of Chinese cities.
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Affiliation(s)
- Chen Yang
- College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
| | - Shuqing Zhao
- College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China.
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28
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Mercier A, Scarpino S, Moore C. Effective resistance against pandemics: Mobility network sparsification for high-fidelity epidemic simulations. PLoS Comput Biol 2022; 18:e1010650. [PMID: 36413581 PMCID: PMC9681106 DOI: 10.1371/journal.pcbi.1010650] [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: 03/02/2022] [Accepted: 10/12/2022] [Indexed: 11/23/2022] Open
Abstract
Network science has increasingly become central to the field of epidemiology and our ability to respond to infectious disease threats. However, many networks derived from modern datasets are not just large, but dense, with a high ratio of edges to nodes. This includes human mobility networks where most locations have a large number of links to many other locations. Simulating large-scale epidemics requires substantial computational resources and in many cases is practically infeasible. One way to reduce the computational cost of simulating epidemics on these networks is sparsification, where a representative subset of edges is selected based on some measure of their importance. We test several sparsification strategies, ranging from naive thresholding to random sampling of edges, on mobility data from the U.S. Following recent work in computer science, we find that the most accurate approach uses the effective resistances of edges, which prioritizes edges that are the only efficient way to travel between their endpoints. The resulting sparse network preserves many aspects of the behavior of an SIR model, including both global quantities, like the epidemic size, and local details of stochastic events, including the probability each node becomes infected and its distribution of arrival times. This holds even when the sparse network preserves fewer than 10% of the edges of the original network. In addition to its practical utility, this method helps illuminate which links of a weighted, undirected network are most important to disease spread.
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Affiliation(s)
- Alexander Mercier
- Department of Mathematics & Statistics, University of South Florida, Tampa, Florida, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail:
| | - Samuel Scarpino
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- Pandemic Prevention Institute, The Rockefeller Foundation, Washington, D.C., United States of America
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Cristopher Moore
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
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Du J, Ye X, Newman G, Retchless D. Network Science-based Urban Forecast Dashboard. ARIC 2022 : PROCEEDINGS OF THE 5TH ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ADVANCES IN RESILIENT AND INTELLIGENT CITIES (ARIC 2022) : 1ST NOV 2022, SEATTLE, WA, USA. ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ADVANCES IN RESILIENT AND IN... 2022; 2022:7-10. [PMID: 38098514 PMCID: PMC10719900 DOI: 10.1145/3557916.3567822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
The urban environment is a highly dynamic and complex system. Urban dynamics in this complex system is largely reflected by the movement of people to and from Places of Interest (POIs) in the urban area. To better understand and plan for the city's various scenarios, there is a need to forecast urban dynamic conditions in terms of the possible movements of people across POIs. However, such predictions are not easy because an interdependent and living system is hard to forecast. In addition, the commuting and shopping of individuals in urban environments will show distinct patterns at various stages of disasters as compared to normal situations. This paper presents a network science-based urban forecast dashboard, in order to monitor urban events and identify the interdependencies that characterize urban dynamics. Behind the dashboard is a deep learning model that incorporates the network dynamics between POIs. The dashboard powers the prediction of urban dynamics from a network science perspective. This research calls for a unified framework to model the flow and network in the city. The dashboard visualizes how network science and urban science can mutually benefit from each other.
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Affiliation(s)
- Jiaxin Du
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
| | - Galen Newman
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
| | - David Retchless
- Department of Marine and Coastal Environmental Science Texas A&M University at Galveston, Galveston, Texas
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30
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Liu P, Zheng Y. Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic. PHYSICA A 2022; 603:127837. [PMID: 35783919 PMCID: PMC9233890 DOI: 10.1016/j.physa.2022.127837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/09/2022] [Indexed: 05/04/2023]
Abstract
This work systematically conducts a data analysis based on the numbers of both cumulative and daily confirmed COVID-19 cases and deaths in a time span through April 2020 to June 2022 for over 200 countries around the world. Such research feature aims to reveal the temporal and spatial evolution of the country-level distribution observed in COVID-19 pandemic, and obtains some interesting results as follows. (1) The distributions of the numbers for cumulative confirmed cases and deaths obey power-law in early stages of COVID-19 and stretched exponential function in subsequent course. (2) The distributions of the numbers for daily confirmed cases and deaths obey power-law in early and late stages of COVID-19 and stretched exponential function in middle stages. The crossover region between power-law and stretched exponential behavior seems to depend on the evolution of "infection" event and "death" event. Such observation implies a kind of important symmetry related to the dynamics process of COVID-19 spreading. (3) The distributions of the normalized numbers for each metric show a temporal scaling behavior in 2-year period, and are well described by stretched exponential function. The observation of power-law and stretched exponential behavior in such country-level distributions suggests underlying intrinsic dynamics of a virus spreading process in human interconnected society. And thus it is important for understanding and mathematically modeling the COVID-19 pandemic.
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Affiliation(s)
- Peng Liu
- School of Information, Xi'an University of Finance and Economics, Xi'an 710100, Shaanxi, PR China
| | - Yanyan Zheng
- School of Management, Xi'an Polytechnic University, Xi'an 710048, Shaanxi, PR China
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31
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Fang Z, Wang G, Yang Y, Zhang F, Wang Y, Zhang D. A long-term travel delay measurement study based on multi-modal human mobility data. Sci Rep 2022; 12:15988. [PMID: 36163340 PMCID: PMC9510763 DOI: 10.1038/s41598-022-19394-z] [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: 04/29/2022] [Accepted: 08/29/2022] [Indexed: 12/03/2022] Open
Abstract
Understanding human mobility is of great significance for sustainable transportation planning. Long-term travel delay change is a key metric to measure human mobility evolution in cities. However, it is challenging to quantify the long-term travel delay because it happens in different modalities, e.g., subway, taxi, bus, and personal cars, with implicated coupling. More importantly, the data for long-term multi-modal delay modeling is challenging to obtain in practice. As a result, the existing travel delay measurements mainly focus on either single-modal system or short-term mobility patterns, which cannot reveal the long-term travel dynamics and the impact among multi-modal systems. In this paper, we perform a travel delay measurement study to quantify and understand long-term multi-modal travel delay. Our measurement study utilizes a 5-year dataset of 8 million residents from 2013 to 2017 including a subway system with 3 million daily passengers, a 15 thousand taxi system, a 10 thousand personal car system, and a 13 thousand bus system in the Chinese city Shenzhen. We share new observations as follows: (1) the aboveground system has a higher delay increase overall than that of the underground system but the increase of it is slow down; (2) the underground system infrastructure upgrades decreases the aboveground system travel delay increase in contrast to the increase the underground system travel delay caused by the aboveground system infrastructure upgrades; (3) the travel delays of the underground system decreases in the higher population region and during the peak hours.
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Affiliation(s)
- Zhihan Fang
- Department of Computer Science, Rutgers University, Piscataway, NJ, 08854-8019, USA
| | - Guang Wang
- Department of Computer Science, Florida State University, Tallahassee, FL, 32306, USA
| | - Yu Yang
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA
| | - Fan Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, 518055, People's Republic of China
| | - Yang Wang
- University of Science and Technology of China, No. 96, JinZhai Road, Hefei, 230026, Anhui, People's Republic of China.
| | - Desheng Zhang
- Department of Computer Science, Rutgers University, Piscataway, NJ, 08854-8019, USA.
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32
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Phillips K, Stanley K, Fuller D. A theory-based model of cumulative activity. Sci Rep 2022; 12:15635. [PMID: 36115875 PMCID: PMC9482623 DOI: 10.1038/s41598-022-18982-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/23/2022] [Indexed: 11/24/2022] Open
Abstract
Energy expenditure can be used to examine the health of individuals and the impact of environmental factors on physical activity. One of the more common ways to quantify energy expenditure is to process accelerometer data into some unit of measurement for this expenditure, such as Actigraph activity counts, and bin those measures into physical activity levels. However, accepted thresholds can vary between demographics, and some units of energy measurements do not currently have agreed upon thresholds. We present an approach which computes unique thresholds for each individual, using piecewise exponential functions to model the characteristics of their overall physical activity patterns corresponding to well established sedentary, light, moderate and vigorous activity levels from the literature. Models are fit using existing piecewise fitting techniques and software. Most participants’ activity intensity profile is exceptionally well modeled as piecewise exponential decay. Using this model, we find emergent groupings of participant behavior and categorize individuals into non-vigorous, consistent, moderately active, or extremely active activity intensity profiles. In the supplemental materials, we demonstrate that the parameters of the model correlate with demographics of age, household size, and level of education, inform behavior change under COVID lockdown, and are reasonably robust to signal frequency.
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Abstract
A common feature of large-scale extreme events, such as pandemics, wildfires, and major storms is that, despite their differences in etiology and duration, they significantly change routine human movement patterns. Such changes, which can be major or minor in size and duration and which differ across contexts, affect both the consequences of the events and the ability of governments to mount effective responses. Based on naturally tracked, anonymized mobility behavior from over 90 million people in the United States, we document these mobility differences in space and over time in six large-scale crises, including wildfires, major tropical storms, winter freeze and pandemics. We introduce a model that effectively captures the high-dimensional heterogeneity in human mobility changes following large-scale extreme events. Across five different metrics and regardless of spatial resolution, the changes in human mobility behavior exhibit a consistent hyperbolic decline, a pattern we characterize as "spatiotemporal decay." When applied to the case of COVID-19, our model also uncovers significant disparities in mobility changes-individuals from wealthy areas not only reduce their mobility at higher rates at the start of the pandemic but also maintain the change longer. Residents from lower-income regions show a faster and greater hyperbolic decay, which we suggest may help account for different COVID-19 rates. Our model represents a powerful tool to understand and forecast mobility patterns post emergency, and thus to help produce more effective responses.
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34
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Monitoring supply networks from mobile phone data for estimating the systemic risk of an economy. Sci Rep 2022; 12:13347. [PMID: 35922453 PMCID: PMC9349293 DOI: 10.1038/s41598-022-13104-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 05/20/2022] [Indexed: 11/09/2022] Open
Abstract
Remarkably little is known about the structure, formation, and dynamics of supply- and production networks that form one foundation of society. Neither the resilience of these networks is known, nor do we have ways to systematically monitor their ongoing change. Systemic risk contributions of individual companies were hitherto not quantifiable since data on supply networks on the firm-level do not exist with the exception of a very few countries. Here we use telecommunication meta data to reconstruct nationwide firm-level supply networks in almost real-time. We find the probability of observing a supply-link, given the existence of a strong communication-link between two companies, to be about 90%. The so reconstructed supply networks allow us to reliably quantify the systemic risk of individual companies and thus obtain an estimate for a country’s economic resilience. We identify about 65 companies, from a broad range of company sizes and from 22 different industry sectors, that could potentially cause massive damages. The method can be used for objectively monitoring change in production processes which might become essential during the green transition.
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35
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Liu C, Yang Y, Chen B, Cui T, Shang F, Fan J, Li R. Revealing spatiotemporal interaction patterns behind complex cities. CHAOS (WOODBURY, N.Y.) 2022; 32:081105. [PMID: 36049958 DOI: 10.1063/5.0098132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
Cities are typical dynamic complex systems that connect people and facilitate interactions. Revealing general collective patterns behind spatiotemporal interactions between residents is crucial for various urban studies, of which we are still lacking a comprehensive understanding. Massive cellphone data enable us to construct interaction networks based on spatiotemporal co-occurrence of individuals. The rank-size distributions of dynamic population of locations in all unit time windows are stable, although people are almost constantly moving in cities and hot-spots that attract people are changing over time in a day. A larger city is of a stronger heterogeneity as indicated by a larger scaling exponent. After aggregating spatiotemporal interaction networks over consecutive time windows, we reveal a switching behavior of cities between two states. During the "active" state, the whole city is concentrated in fewer larger communities, while in the "inactive" state, people are scattered in smaller communities. Above discoveries are universal over three cities across continents. In addition, a city stays in an active state for a longer time when its population grows larger. Spatiotemporal interaction segregation can be well approximated by residential patterns only in smaller cities. In addition, we propose a temporal-population-weighted-opportunity model by integrating a time-dependent departure probability to make dynamic predictions on human mobility, which can reasonably well explain the observed patterns of spatiotemporal interactions in cities.
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Affiliation(s)
- Chenxin Liu
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yu Yang
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Bingsheng Chen
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianyu Cui
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Fan Shang
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jingfang Fan
- School of Systems Science/Institute of Nonequilibrium Systems, Beijing Normal University, Beijing 100875, China
| | - Ruiqi Li
- UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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36
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Zhang X, Lobinska G, Feldman M, Dekel E, Nowak MA, Pilpel Y, Pauzner Y, Barzel B, Pauzner A. A spatial vaccination strategy to reduce the risk of vaccine-resistant variants. PLoS Comput Biol 2022; 18:e1010391. [PMID: 35947602 PMCID: PMC9394842 DOI: 10.1371/journal.pcbi.1010391] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 08/22/2022] [Accepted: 07/14/2022] [Indexed: 11/18/2022] Open
Abstract
The COVID-19 pandemic demonstrated that the process of global vaccination against a novel virus can be a prolonged one. Social distancing measures, that are initially adopted to control the pandemic, are gradually relaxed as vaccination progresses and population immunity increases. The result is a prolonged period of high disease prevalence combined with a fitness advantage for vaccine-resistant variants, which together lead to a considerably increased probability for vaccine escape. A spatial vaccination strategy is proposed that has the potential to dramatically reduce this risk. Rather than dispersing the vaccination effort evenly throughout a country, distinct geographic regions of the country are sequentially vaccinated, quickly bringing each to effective herd immunity. Regions with high vaccination rates will then have low infection rates and vice versa. Since people primarily interact within their own region, spatial vaccination reduces the number of encounters between infected individuals (the source of mutations) and vaccinated individuals (who facilitate the spread of vaccine-resistant strains). Thus, spatial vaccination may help mitigate the global risk of vaccine-resistant variants.
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Affiliation(s)
- Xiyun Zhang
- Department of Physics, Jinan University, Guangzhou, China
| | - Gabriela Lobinska
- Department of Molecular Genetics, Weizmann Institute of Science, Israel
| | - Michal Feldman
- School of Computer Science and Center for Combating Pandemics, Tel Aviv University, Israel
| | - Eddie Dekel
- Department of Economics, Northwestern University, Illinois, United States of America, and School of Economics, Tel Aviv University, Israel
| | - Martin A. Nowak
- Department of Mathematics and Department of Organismic and Evolutionary Biology, Harvard University, Massachusetts, United States of America
| | - Yitzhak Pilpel
- Department of Molecular Genetics, Weizmann Institute of Science, Israel
| | | | - Baruch Barzel
- Department of Mathematics and Gonda Multidisciplinary Brain Research Center Bar-Ilan University, Israel, and Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Ady Pauzner
- School of Economics and Center for Combating Pandemics, Tel Aviv University, Israel
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37
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A Multiview Representation Learning Framework for Large-Scale Urban Road Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations are biased and cannot fully capture the complex traffic flow patterns that are attributed to human mobility in road networks. Moreover, real-world road networks usually contain millions of segments, which poses a great challenge regarding the memory usage and computational efficiency of existing methods. Consequently, we proposed a novel multiview representation learning framework for large-scale urban road networks to simultaneously preserve topological and human mobility information. First, the road network was modeled as a multigraph, and a multiview random walk method was developed to capture the structure function of the road network from a topology-aware graph and vehicle transfer pattern from a mobility-aware graph. In this process, a large-scale road network organization method was established to improve the random walk algorithm efficiency. Finally, word2vec was applied to learn representations based on sequences that were generated by the multiview random walk. In the experiment, two real-world datasets were used to demonstrate the superior performance of our framework through a comparative analysis.
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38
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Modeling Health Seeking Behavior Based on Location-Based Service Data: A Case Study of Shenzhen, China. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11050295] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Understanding residents’ health seeking behavior is crucial for the planning and utilization of healthcare resources. With the support of emerging location-based service (LBS) data, this study proposes a framework for inferring health seeking trips, measuring observed spatial accessibility to healthcare, and interpreting the determinants of health seeking behavior. Taking Shenzhen, China as a case study, a supply–demand ratio calculation method based on observed data is developed to explore basic patterns of health seeking, while health seeking behavior is described using a spatial analysis framework based on the Huff model. A total of 95,379 health seeking trips were identified, and their analysis revealed obvious differences between observed and potential spatial accessibility. In addition to the traditional distance decay effect and number of doctors, the results showed health seeking behavior to be determined by hospital characteristics such as hospital scale, service quality, and popularity. Furthermore, this study also identified differences in health seeking behavior between subgroups with different ages, incomes, and education levels. The findings highlight the need to incorporate actual health seeking behavior when measuring the spatial accessibility of healthcare and planning healthcare resources. The framework and methods proposed in this study can be applied to other contexts and other types of public facilities.
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39
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Benchmarking City Layouts—A Methodological Approach and an Accessibility Comparison between a Real City and the Garden City. SUSTAINABILITY 2022. [DOI: 10.3390/su14095029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article presents a comparative accessibility study between a real city and its redraft as a Garden City. The benchmarking methodology involves defining and evaluating a location-based accessibility indicator in a GIS environment for the city of Coimbra, Portugal, and for the same city laid out as a Garden City, with the same number of inhabitants, jobs, and similar number of urban facilities. The results are derived as maps and weighted average distances per inhabitant to the facilities and jobs, and show that, for the Garden City, average distances drop to around 500 m for urban facilities and 1500 m for the combination of facilities and jobs, making much of the city accessible by walking and practically the whole of it accessible by cycling, with positive impact on transport sustainability and accessibility equity. The methodology can be extended to other benchmarking indicators and city layouts, and the quantitative results it yields make a valuable contribution to the debate on the ideal layout of cities. Moreover, it gives directions on how to improve real cities to address current and future sustainability concerns.
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40
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Vilk O, Campos D, Méndez V, Lourie E, Nathan R, Assaf M. Phase Transition in a Non-Markovian Animal Exploration Model with Preferential Returns. PHYSICAL REVIEW LETTERS 2022; 128:148301. [PMID: 35476490 DOI: 10.1103/physrevlett.128.148301] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Abstract
We study a non-Markovian and nonstationary model of animal mobility incorporating both exploration and memory in the form of preferential returns. Exact results for the probability of visiting a given number of sites are derived and a practical WKB approximation to treat the nonstationary problem is developed. A mean-field version of this model, first suggested by Song et al., [Modelling the scaling properties of human mobility, Nat. Phys. 6, 818 (2010)NPAHAX1745-247310.1038/nphys1760] was shown to well describe human movement data. We show that our generalized model adequately describes empirical movement data of Egyptian fruit bats (Rousettus aegyptiacus) when accounting for interindividual variation in the population. We also study the probability of visiting any site a given number of times and derive a mean-field equation. Our analysis yields a remarkable phase transition occurring at preferential returns which scale linearly with past visits. Following empirical evidence, we suggest that this phase transition reflects a trade-off between extensive and intensive foraging modes.
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Affiliation(s)
- Ohad Vilk
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Movement Ecology Lab, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Daniel Campos
- Grup de Física Estadística, Dept. de Física, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain
| | - Vicenç Méndez
- Grup de Física Estadística, Dept. de Física, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain
| | - Emmanuel Lourie
- Movement Ecology Lab, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Ran Nathan
- Movement Ecology Lab, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Michael Assaf
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Institute for Physics and Astronomy, University of Potsdam, Potsdam 14476, Germany
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41
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Valgañón P, Soriano-Paños D, Arenas A, Gómez-Gardeñes J. Contagion-diffusion processes with recurrent mobility patterns of distinguishable agents. CHAOS (WOODBURY, N.Y.) 2022; 32:043102. [PMID: 35489866 DOI: 10.1063/5.0085532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
The analysis of contagion-diffusion processes in metapopulations is a powerful theoretical tool to study how mobility influences the spread of communicable diseases. Nevertheless, many metapopulation approaches use indistinguishable agents to alleviate analytical difficulties. Here, we address the impact that recurrent mobility patterns, and the spatial distribution of distinguishable agents, have on the unfolding of epidemics in large urban areas. We incorporate the distinguishable nature of agents regarding both their residence and their usual destination. The proposed model allows both a fast computation of the spatiotemporal pattern of the epidemic trajectory and the analytical calculation of the epidemic threshold. This threshold is found as the spectral radius of a mixing matrix encapsulating the residential distribution and the specific commuting patterns of agents. We prove that the simplification of indistinguishable individuals overestimates the value of the epidemic threshold.
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Affiliation(s)
- P Valgañón
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
| | - D Soriano-Paños
- Instituto Gulbenkian de Ciência (IGC), 2780-156 Oeiras, Portugal
| | - A Arenas
- Departament de Matemáticas i Enginyeria Informática, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - J Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
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Daily Human Mobility: A Reproduction Model and Insights from the Energy Concept. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11040219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Human movements have raised broad attention, and many models have been developed to reproduce them. However, most studies focus on reproducing the statistical properties of human mobility, such as the travel distance and the visiting frequency. In this paper, a two-step Markov Chain model is proposed to generate daily human movements, and spatial and spatiotemporal attributes of reproduced mobility are examined. In the first step, people’s statuses in the next time slot are conditioned on their previous travel patterns; and in the second step, individual location in such a slot is probabilistically determined based on his/her status. Our model successfully reproduces the spatial and spatiotemporal characteristics of human daily movements, and the result indicates that people’s future statuses can be inferred based on travel patterns they made, regardless of exactly where they have traveled, and when trips happen. We also revisit the energy concept, and show that the energy expenditure is stable over years. This idea is further used to predict the proportion of long-distance trips for each year, which gives insights into the probabilities of statuses in the next time slot. Finally, we interpret the constant energy expenditure as the constant ‘cost’ over years.
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43
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A Paradigm Shift for a Transition to Sustainable Urban Transport. SUSTAINABILITY 2022. [DOI: 10.3390/su14052853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The traffic-engineering methods of planning based on the predict-and-provide principle have self-enforcing effects of induced traffic and an unhealthy environment for humans as well as for the planet. The paper aims to demonstrate that such methods keep cities stuck in a sort of path dependency with transport technologies and urban environment and to find evidence that something is changing in theory, trends, and practice. A systematic and extensive literature review has been used to identify and understand the problems, to recognise the changes taking place, and to examine the solutions. The main findings are the causes of how these problems could have happened and continue to do so regardless of the huge negative effects and the recognition that a paradigm shift is emerging as the sum of methods and achievements developed by the community of academics, experts, practitioners, policymakers, and urban communities. The findings can have practical, effective implications as the determinants of a new transport policy paradigm that shows the way out of the trap of path dependency. The originality of the approach lies in having expanded and applied the concept of anomalies of the theory to the adverse effects of technologies and the mismatch between people and the modern urban environment. The new paradigm is already showing its practical effectiveness in solving real problems by adapting cities and technologies to human nature and developing a more holistic human-centric planning method.
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44
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Clonotype pattern in T-cell lymphomas map the cell of origin to immature lymphoid precursors. Blood Adv 2022; 6:2334-2345. [PMID: 35015812 PMCID: PMC9006294 DOI: 10.1182/bloodadvances.2021005884] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/07/2021] [Indexed: 11/20/2022] Open
Abstract
Mature T-cell lymphomas (TCLs) are rare, clinically heterogeneous hematologic cancers of high medical need. TCLs have inferior prognosis which is attributed to poor understanding of their pathogenesis. Based on phenotypic similarities between normal and neoplastic lymphocytes it has been assumed that TCLs develop in the periphery, directly from various subtypes of normal T-cells. To address the debated question of the cell of origin in TCLs we analyzed to identify the highly variable complementarity determining regions (CDR3) regions of T-cell receptor (TCR) to trace the clonal history of the T-cells. We have collected previously published whole genome -exome, and -transcriptome sequencing data from 574 TCL patients. TCR clonotypes were identified by de novo assembly of CDR3 regions of TCR γ, β and α. We have found that the vast majority of TCLs are clonotypically oligoclonal, although the pattern oligoclonality varied. Anaplastic large cell lymphoma was most diverse comprising multiple clonotypes of TCRγ, β and α whereas adult T-cell lymphoma/leukemia and peripheral T-cell lymphomas often showed monoclonality for TCRγ and β but had diverse TCRα clonotypes. These patterns of rearrangements indicated that TCLs are initiated at the level of the lymphoid precursor. In keeping with this hypothesis, TCR rearrangements in TCLs resembled the pattern seen in the human thymus showing biased usage of V and J segments of high combinatorial probability resulting in recurrent, "public" CDR3 sequences shared across unrelated patients and different clinical TCL entities. Clonotypically diverse initiating cells may seed target tissues being responsible for disease relapses after therapy.
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Marijuán PC, Navarro J. The biological information flow: From cell theory to a new evolutionary synthesis. Biosystems 2022; 213:104631. [DOI: 10.1016/j.biosystems.2022.104631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/19/2022] [Accepted: 01/23/2022] [Indexed: 12/19/2022]
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Luca M, Lepri B, Frias-Martinez E, Lutu A. Modeling international mobility using roaming cell phone traces during COVID-19 pandemic. EPJ DATA SCIENCE 2022; 11:22. [PMID: 35402140 PMCID: PMC8978511 DOI: 10.1140/epjds/s13688-022-00335-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/21/2022] [Indexed: 05/17/2023]
Abstract
Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.
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Affiliation(s)
- Massimiliano Luca
- Bruno Kessler Foundation, Trento, Italy
- Free University of Bolzano, Bolzano, Italy
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Yoon J, Jung WS, Kim H. COVID-19 confines recreational gatherings in Seoul to familiar, less crowded, and neighboring urban areas. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2022; 9:330. [PMID: 36187846 PMCID: PMC9510209 DOI: 10.1057/s41599-022-01349-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/08/2022] [Indexed: 05/06/2023]
Abstract
Recreational gatherings are sources of the spread of infectious diseases. Understanding the dynamics of recreational gatherings is essential to building effective public health policies but challenging as the interaction between people and recreational places is complex. Recreational activities are concentrated in a set of urban areas and establish a recreational hierarchy. In this hierarchy, higher-level regions attract more people than lower-level regions for recreational purposes. Here, using customers' motel booking records which are highly associated with recreational activities in Korea, we identify that recreational hierarchy, geographical distance, and attachment to a location are crucial factors of recreational gatherings in Seoul, Republic of Korea. Our analyses show that after the COVID-19 outbreak, people are more likely to visit familiar recreational places, avoid the highest level of the recreational hierarchy, and travel close distances. Interestingly, the recreational visitations were reduced not only in the highest but also in low-level regions. Urban areas at low levels of the recreational hierarchy were more severely affected by COVID-19 than urban areas at high and middle levels of the recreational hierarchy.
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Affiliation(s)
- Jisung Yoon
- Kellogg School of Management at Northwestern University, Evanston, IL 60208 USA
- Northwestern Institute on Complex Systems, Evanston, IL 60208 USA
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea
| | - Woo-Sung Jung
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea
- Department of Physics, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea
| | - Hyunuk Kim
- Department of Administrative Sciences, Metropolitan College, Boston University, Boston, MA 02215 USA
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Lucchini L, Centellegher S, Pappalardo L, Gallotti R, Privitera F, Lepri B, De Nadai M. Living in a pandemic: changes in mobility routines, social activity and adherence to COVID-19 protective measures. Sci Rep 2021; 11:24452. [PMID: 34961773 PMCID: PMC8712525 DOI: 10.1038/s41598-021-04139-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/08/2021] [Indexed: 11/20/2022] Open
Abstract
Non-Pharmaceutical Interventions (NPIs), aimed at reducing the diffusion of the COVID-19 pandemic, have dramatically influenced our everyday behaviour. In this work, we study how individuals adapted their daily movements and person-to-person contact patterns over time in response to the NPIs. We leverage longitudinal GPS mobility data of hundreds of thousands of anonymous individuals to empirically show and quantify the dramatic disruption in people's mobility habits and social behaviour. We find that local interventions did not just impact the number of visits to different venues but also how people experience them. Individuals spend less time in venues, preferring simpler and more predictable routines, also reducing person-to-person contacts. Moreover, we find that the individual patterns of visits are influenced by the strength of the NPIs policies, the local severity of the pandemic and a risk adaptation factor, which increases the people's mobility regardless of the stringency of interventions. Finally, despite the gradual recovery in visit patterns, we find that individuals continue to keep person-to-person contacts low. This apparent conflict hints that the evolution of policy adherence should be carefully addressed by policymakers, epidemiologists and mobility experts.
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Affiliation(s)
| | | | - Luca Pappalardo
- Institute of Information Science and Technologies, National Research Council (ISTI-CNR), Pisa, Italy
| | | | | | - Bruno Lepri
- Fondazione Bruno Kessler (FBK), Trento, Italy
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49
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Zhang S, Wang M, Yang Z, Zhang B. A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:13294. [PMID: 34948902 PMCID: PMC8704640 DOI: 10.3390/ijerph182413294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 11/24/2022]
Abstract
Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various "densities" were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the "densities" were actually an abstract reflection of the "contact" frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect "contact" frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional "densities". Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.
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Affiliation(s)
| | | | | | - Baolei Zhang
- College of Geography and Environment, Shandong Normal University, Jinan 250014, China; (S.Z.); (M.W.); (Z.Y.)
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50
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Salat H, Schläpfer M, Smoreda Z, Rubrichi S. Analysing the impact of electrification on rural attractiveness in Senegal with mobile phone data. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201898. [PMID: 34754490 PMCID: PMC8493192 DOI: 10.1098/rsos.201898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
Reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people's lives-food, education, healthcare. It spurs new economic opportunities, thus improving livelihoods. Using a comprehensive dataset of pseudonymized mobile phone records, we analyse the impact of electrification on attractiveness for rural areas in Senegal. We extract communication and mobility flows from call detail records and show that electrification is positively and specifically correlated with centrality measures within the communication network and with the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination and the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than centralizing electricity supplies to maximize the development of specifically targeted sites.
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Affiliation(s)
- Hadrien Salat
- Sociology and Economics of Networks and Services Department, Orange Innovation Research, 44 Avenue de la République, Châtillon 92320, France
- Future Cities Laboratory, Singapore-ETH Centre, ETH Zürich, 1 Create Way, CREATE Tower #06-01, Singapore 138602, Republic of Singapore
| | - Markus Schläpfer
- Future Cities Laboratory, Singapore-ETH Centre, ETH Zürich, 1 Create Way, CREATE Tower #06-01, Singapore 138602, Republic of Singapore
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
| | - Zbigniew Smoreda
- Sociology and Economics of Networks and Services Department, Orange Innovation Research, 44 Avenue de la République, Châtillon 92320, France
| | - Stefania Rubrichi
- Sociology and Economics of Networks and Services Department, Orange Innovation Research, 44 Avenue de la République, Châtillon 92320, France
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