How the distance between regional and human mobility behavior affect the epidemic spreading

2018 ◽  
Vol 492 ◽  
pp. 1823-1830 ◽  
Author(s):  
Minna Wu ◽  
She Han ◽  
Mei Sun ◽  
Dun Han
Keyword(s):  
Human Mobility ◽  
Epidemic Spreading ◽  
Mobility Behavior

How much does human mobility behavior affect the COVID-19 infection spread?

2020 ◽  
Author(s):  
Shogo Isoda ◽  
Shogo Kawanaka ◽  
Yuki Matsuda ◽  
Hirohiko Suwa ◽  
Keiichi Yasumoto
Keyword(s):  
Human Mobility ◽  
Mobility Behavior ◽  
Infection Spread

scholarly journals Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Naoya Fujiwara ◽  
Takayuki Wada ◽  
Yoshihide Sekimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Social Contact ◽  
Mobility Patterns ◽  
State Of Emergency ◽  
Social Contacts ◽  
Policy Makers ◽  
Mobility Data ◽  
Contact Rates ◽  
Mobility Behavior

Abstract While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

scholarly journals Human mobility and time spent at destination: Impact on spatial epidemic spreading

2013 ◽  
Vol 338 ◽  
pp. 41-58 ◽  
Author(s):  
Chiara Poletto ◽  
Michele Tizzoni ◽  
Vittoria Colizza
Keyword(s):  
Human Mobility ◽  
Epidemic Spreading ◽  
Spatial Epidemic

Emergence of urban growth patterns from human mobility behavior

2021 ◽  
Author(s):  
Fengli Xu ◽  
Yong Li ◽  
Depeng Jin ◽  
Jianhua Lu ◽  
Chaoming Song
Keyword(s):  
Urban Growth ◽  
Human Mobility ◽  
Growth Patterns ◽  
Mobility Behavior

Epidemics and Human Mobility

2021 ◽  
Author(s):  
Theo Geisel
Keyword(s):  
Infectious Diseases ◽  
Recent Work ◽  
Statistical Data ◽  
Multiple Scales ◽  
Human Mobility ◽  
Traffic Networks ◽  
Epidemic Spreading ◽  
Endemic Diseases ◽  
And Control

<p>The severity of infectious diseases and epidemics increases drastically, when pathogens start being transmitted between humans, as thereby they can dispose of human traffic networks for their spreading. This can transform an epidemic into a worldwide threatening pandemic, as the current COVID-19 crisis has shown. Traffic networks exist on multiple scales and the spreading of pathogens exhibits superdiffusive properties. This talk will emphasize and analyze the key role of human mobility for the modeling, forecast, and control of epidemic spreading. A major problem is posed by the limited availability of statistical data on human mobility. Various proxies are now utilized since we suggested dollar bills as proxies for human moblity.  Recent work on endemic diseases in populations open to migration will be discussed. </p>

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.

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