scholarly journals Interplay between population density and mobility in determining the spread of epidemics in cities

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Surendra Hazarie ◽  
David Soriano-Paños ◽  
Alex Arenas ◽  
Jesús Gómez-Gardeñes ◽  
Gourab Ghoshal
Keyword(s):  
Population Density ◽  
Urban Areas ◽  
Human Mobility ◽  
The United States ◽  
Mitigation Strategies ◽  
Mobility Patterns ◽  
Epidemic Spreading ◽  
Optimal Control Strategy ◽  
Global Trend ◽  
Data Driven Approach

AbstractThe increasing agglomeration of people in dense urban areas coupled with the existence of efficient modes of transportation connecting such centers, make cities particularly vulnerable to the spread of epidemics. Here we develop a data-driven approach combines with a meta-population modeling to capture the interplay between population density, mobility and epidemic spreading. We study 163 cities, chosen from four different continents, and report a global trend where the epidemic risk induced by human mobility increases consistently in those cities where mobility flows are predominantly between high population density centers. We apply our framework to the spread of SARS-CoV-2 in the United States, providing a plausible explanation for the observed heterogeneity in the spreading process across cities. Based on this insight, we propose realistic mitigation strategies (less severe than lockdowns), based on modifying the mobility in cities. Our results suggest that an optimal control strategy involves an asymmetric policy that restricts flows entering the most vulnerable areas but allowing residents to continue their usual mobility patterns.

scholarly journals Interplay between intra-urban population density and mobility in determining the spread of epidemics

2021 ◽  
Author(s):  
Surendra Hazarie ◽  
David Soriano Panos ◽  
Alex Arenas ◽  
Jesus Gomez-Gardenes ◽  
Gourab Ghoshal
Keyword(s):  
Population Density ◽  
Urban Areas ◽  
Human Mobility ◽  
The United States ◽  
Mitigation Strategies ◽  
High Population Density ◽  
Mobility Patterns ◽  
Optimal Control Strategy ◽  
Spreading Process ◽  
Contagious Diseases

Abstract In this work, we address the connection between population density centers in urban areas, and the nature of human flows between such centers, in shaping the vulnerability to the onset of contagious diseases. A study of 163 cities, chosen from four different continents reveals a universal trend, whereby the risk induced by human mobility increases in those cities where mobility flows are predominantly between high population density centers. We apply our formalism to the spread of SARS-COV-2 in the United States, providing a plausible explanation for the observed heterogeneity in the spreading process across cities. Armed with this insight, we propose realistic mitigation strategies (less severe than lockdowns), based on modifying the mobility in cities. Our results suggest that an optimal control strategy involves an asymmetric policy that restricts flows entering the most vulnerable areas but allowing residents to continue their usual mobility patterns.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies

2020 ◽  
Author(s):  
Miguel Ribeiro ◽  
Nuno Nunes ◽  
Valentina Nisi ◽  
Johannes Schöning
Keyword(s):  
Public Transportation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Tracking System ◽  
Human Mobility ◽  
Transportation Systems ◽  
Mobility Patterns ◽  
Systematic Analysis ◽  
Long Term Study

Abstract In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

scholarly journals Using Twitter to Analyze the Effect of Hurricanes on Human Mobility Patterns

Urban Science ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 87 ◽  
Author(s):  
Ahmed Ahmouda ◽  
Hartwig H. Hochmair ◽  
Sreten Cvetojevic
Keyword(s):  
Human Mobility ◽  
Local Level ◽  
Mean Square Displacement ◽  
The United States ◽  
The Body ◽  
Activity Space ◽  
Radius Of Gyration ◽  
Mobility Patterns ◽  
Geographical Regions ◽  
Urbanized Areas

Understanding human mobility patterns becomes essential in crisis management and response. This study analyzes the effect of two hurricanes in the United States on human mobility patterns, more specifically on trip distance (displacement), radius of gyration, and mean square displacement, using Twitter data. The study examines three geographical regions which include urbanized areas (Houston, Texas; Miami-Dade County, Florida) and both rural and urbanized areas (North and South Carolina) affected by hurricanes Matthew (2016) and Harvey (2017). Comparison of movement patterns before, during, and after each hurricane shows that displacement and activity space decreased during the events in the regions. Part of this decline can be potentially tied to observed lower tweet numbers around supply facilities during hurricanes, when many of them are closed, as well as to numerous flooded and blocked roads reported in the affected regions. Furthermore, it is shown that displacement patterns can be modeled through a truncated power-law before, during, and after the analyzed hurricanes, which demonstrates the resilience of human mobility behavior in this regard. Analysis of hashtag use in the three study areas indicates that Twitter contributors post about the events primarily during the hurricane landfall and to some extent also during hurricane preparation. This increase in hurricane-related Twitter topics and decrease in activity space provides a tie between changed travel behavior in affected areas and user perception of hurricanes in the Twitter community. Overall, this study adds to the body of knowledge that connects human mobility to natural crises at the local level. It suggests that governmental and rescue operations need to respond to and be prepared for reduced mobility of residents in affected regions during natural crisis events.

FGVis: visual analytics of human mobility patterns and urban areas based on F-GloVe

2021 ◽  
Author(s):  
Jinghan Bai ◽  
Huijie Zhang ◽  
Dezhan Qu ◽  
Cheng Lv ◽  
Weizhang Shao
Keyword(s):  
Visual Analytics ◽  
Urban Areas ◽  
Human Mobility ◽  
Mobility Patterns

Simulating human mobility patterns in urban areas

2016 ◽  
Vol 62 ◽  
pp. 137-156 ◽  
Author(s):  
Karim Keramat Jahromi ◽  
Matteo Zignani ◽  
Sabrina Gaito ◽  
Gian Paolo Rossi
Keyword(s):  
Urban Areas ◽  
Human Mobility ◽  
Mobility Patterns

Impact of Human Mobility Trends on the Outbreak of COVID-19 Across Different States of India

2022 ◽  
pp. 241-255
Author(s):  
Swati Ahiirao ◽  
Shraddha Phansalkar ◽  
Nikhil Matta ◽  
Ketan Kotecha
Keyword(s):  
Public Health ◽  
Population Density ◽  
Statistical Methods ◽  
Death Rate ◽  
Human Mobility ◽  
Decisive Role ◽  
Mobility Patterns ◽  
Public Health Infrastructure ◽  
Health Infrastructure ◽  
Regulatory Bodies

The explosion of coronavirus has posed challenges to public health infrastructure in India. This pandemic can be contained with social distancing and isolation. The analysis of human mobility trends plays a decisive role in the spread of the pandemic. These movement patterns are extracted from Google COVID-19 Community Mobile Reports. These reports help to analyze the human mobility trends to various frequently visited places across different states of India. This work focuses on analyzing mobility trends in India and their effect on the spread of pandemic in terms of number of active cases and death rate. The mobility patterns, number of tests conducted, population density across different states in India are explored to understand their effect on the severity of epidemic. These features are correlated using statistical methods. This study lays the foundation in building a framework to contain the contributors for the spread of pandemics and provide insights to the regulatory bodies to strategize enforcing or revoking lockdown restrictions across regions in the country.

Discovery of Public Transportation Patterns Through the Use of Big Data Technologies for Urban Mobility

2019 ◽  
Author(s):  
Hugo Antunes ◽  
Paulo Figueiras ◽  
Ruben Costa ◽  
Joel Teixeira ◽  
Ricardo Jardim-Gonçalves
Keyword(s):  
Big Data ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Areas ◽  
Human Mobility ◽  
Transportation Network ◽  
Transport Network ◽  
Special Focus ◽  
Mobility Patterns ◽  
Big Data Technologies

Abstract Big cities show a wide public transport network that allows people to travel within the cities. However, with the overcrowding of big urban areas, the demand for new mobility strategies has increasing. Every day, citizens need to commute fast, easily and comfortable, which is not always easy due to the complexity of the public transport network. Therefore, this paper aims to explore the ability of Big Data technologies to cope with data collected from public transportation, by inferring automatically and continuously, complex mobility patterns about human mobility, in the form of insightful indicators (such as connections, transshipments or pendular movements), creating a new perspective in public transports data analytics. With special focus on the Lisbon public transport network, the challenge addressed by this work, is to analyze the demand and supply side of transportation network of Lisbon metropolitan area, considering ticketing data provided by the different transportation operators, which until now were essentially obtained through observation methods and surveys.

scholarly journals Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

2019 ◽  
Vol 11 (4) ◽  
pp. 92 ◽  
Author(s):  
Jürgen Hackl ◽  
Thibaut Dubernet
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Urban Setting ◽  
Disease Spread ◽  
Model Parameters ◽  
Epidemic Spreading ◽  
Agent Based ◽  
Spread Model ◽  
Spread Dynamics

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

scholarly journals A wedge strategy for mitigation of urban warming in future climate scenarios

2017 ◽  
Author(s):  
Lei Zhao ◽  
Xuhui Lee ◽  
Natalie Schultz
Keyword(s):  
Urban Areas ◽  
Climate Model ◽  
Green Roofs ◽  
The United States ◽  
Mitigation Strategies ◽  
Human Society ◽  
Urban Warming ◽  
Urban Heat ◽  
Almost All ◽  
Cool Roofs

Abstract. Heat stress is one of the most severe climate threats to the human society in a future warmer world. The situation is further compounded in urban areas by the urban heat island (UHI). Because the majority of world's population is projected to live in cities, there is a pressing need to find effective solutions for the high temperature problem. We use a climate model to investigate the effectiveness of urban heat mitigation strategies: cool roofs, street vegetation, green roofs, and reflective pavement. Our results show that by adopting highly-reflective roofs, almost all the cities in the United States and southern Canada are transformed into white oases at midday, with an average oasis effect of −3.4 K in the summer for the period 2071–2100, which offsets approximately 80 % of the greenhouse gas (GHG) warming projected for the same period under the RCP4.5 scenario. A UHI mitigation wedge consisting of cool roofs, street vegetation and reflective pavement has the potential to eliminate the daytime UHI plus the GHG warming.

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