scholarly journals Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haotian Wang ◽  
Abhirup Ghosh ◽  
Jiaxin Ding ◽  
Rik Sarkar ◽  
Jie Gao
Keyword(s):  
Small Group ◽  
Infection Rate ◽  
Large Scale ◽  
Network Effects ◽  
Human Mobility ◽  
Social Activity ◽  
Activity Levels ◽  
Mobility Data ◽  
Human Movements ◽  
The World

AbstractMajor interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.

scholarly journals Emergence of Urban Growth Patterns from Human Movements

2021 ◽  
Author(s):  
Fengli Xu ◽  
Yong Li ◽  
Depeng Jin ◽  
Jianhua Lu ◽  
Chaoming Song
Keyword(s):  
Urban Growth ◽  
Large Scale ◽  
Scaling Laws ◽  
Human Mobility ◽  
Urban Morphology ◽  
Growth Patterns ◽  
Spatial Correlations ◽  
Mobility Data ◽  
Human Movements ◽  
Correlated Percolation

Abstract Cities grow in a bottom-up manner, leading to fractal-like urban morphology characterized by scaling laws. Correlated percolation has succeeded in modeling urban geometries by imposing strong spatial correlations. However, the origin of such correlations remains largely unknown. Very recently, our understanding of human movements has been revolutionized thanks to the increasing availability of large-scale human mobility data. This paper designs a novel computational urban growth model that offers a micro-foundation in human mobility behavior. We compare the proposed model with three empirical datasets, which evidences that strong social couplings and long-memory effect are two fundamental principles responsible for the mystical spatial correlations. The model not only accounts for the empirically observed scaling laws, but also allows us to understand the city evolution dynamically.

scholarly journals Tracking the global reduction of marine traffic during the COVID-19 pandemic

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David March ◽  
Kristian Metcalfe ◽  
Joaquin Tintoré ◽  
Brendan J. Godley
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Western Mediterranean ◽  
Western Mediterranean Sea ◽  
Marine Traffic ◽  
Human Movements ◽  
Exclusive Economic Zones ◽  
Global Impacts ◽  
Economic Zones

AbstractThe COVID-19 pandemic has resulted in unparalleled global impacts on human mobility. In the ocean, ship-based activities are thought to have been impacted due to severe restrictions on human movements and changes in consumption. Here, we quantify and map global change in marine traffic during the first half of 2020. There were decreases in 70.2% of Exclusive Economic Zones but changes varied spatially and temporally in alignment with confinement measures. Global declines peaked in April, with a reduction in traffic occupancy of 1.4% and decreases found across 54.8% of the sampling units. Passenger vessels presented more marked and longer lasting decreases. A regional assessment in the Western Mediterranean Sea gave further insights regarding the pace of recovery and long-term changes. Our approach provides guidance for large-scale monitoring of the progress and potential effects of COVID-19 on vessel traffic that may subsequently influence the blue economy and ocean health.

scholarly journals Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

2021 ◽  
Vol 10 (2) ◽  
pp. 73
Author(s):  
Raquel Pérez-Arnal ◽  
David Conesa ◽  
Sergio Alvarez-Napagao ◽  
Toyotaro Suzumura ◽  
Martí Català ◽  
...  
Keyword(s):  
Mobile Phones ◽  
Human Mobility ◽  
The Other ◽  
Data Sources ◽  
Experimental Setting ◽  
Mobile Data ◽  
Restriction Patterns ◽  
Mobility Data ◽  
The World ◽  
Proper Interpretation

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

scholarly journals Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

2021 ◽  
Vol 4 ◽  
Author(s):  
A. Potgieter ◽  
I. N. Fabris-Rotelli ◽  
Z. Kimmie ◽  
N. Dudeni-Tlhone ◽  
J. P. Holloway ◽  
...  
Keyword(s):  
Human Mobility ◽  
Data Sources ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The World ◽  
Spatial Weight ◽  
Data Source ◽  
Reduced Mobility ◽  
Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

scholarly journals Research on Collective Human Mobility in Shanghai Based on Cell Phone Data

2020 ◽  
Vol 9 (1) ◽  
pp. 44-62
Author(s):  
Xiyuan Ren ◽  
De Wang
Keyword(s):  
Temporal Variation ◽  
High Frequency ◽  
Cell Phone ◽  
Large Scale ◽  
Human Mobility ◽  
Indicator System ◽  
Data Sources ◽  
Census Tracts ◽  
Aggregate Distribution ◽  
Human Movements

The high-frequency mobility of a massive population has caused an enormous influence on the urban internal structure, which is unable to be described by traditional data sources. While recent advances in location-based technologies provides new opportunities for researchers to understand daily human movements and the structure as a whole. The article aims to explore human spatial movements and their aggregate distribution in Shanghai using large-scale cell phone data. The trajectory of each individual is extracted from cell phone data after data cleansing. Then, an indicator system which includes mobility intensity, mobility stability, influential range, and temporal variation is developed to describe collective human mobility features in census tracts scale. Finally, spatial elements are extracted using the indicator system and the structure of human mobility in Shanghai is discussed.

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 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 An Effective Prediction on COVID-19 Prevalence for India and Japan using Fbprophet Model

2021 ◽  
pp. 16-28
Author(s):  
Md. Mehedi Rahman Rana ◽  
Farjana Rahman ◽  
Jabed Al Faysal ◽  
Md. Anisur Rahman
Keyword(s):  
Time Series ◽  
Infection Rate ◽  
Satisfactory Result ◽  
Death Rate ◽  
Large Scale ◽  
Time Series Forecasting ◽  
Economic Life ◽  
Substantial Influence ◽  
The World

Coronavirus has become a significant concern for the whole world. It has had a substantial influence on our social and economic life. The infection rate is rapidly increasing at every moment throughout the world. At present, predicting coronavirus has become one of the challenging issues for us. As the pace of COVID-19 detection increases, so does the death rate. This research predicts the number of coronavirus detection and deaths using Fbprophet, a tool designed to assist in performing time series forecasting at a large scale. Two major affected countries, India and Japan, have been taken into consideration in our approach.  Using the prophet model, a prediction is performed on the number of total cases, new cases, total deaths and new deaths. This model works considerably well, and it has given a satisfactory result that may help the authority in taking early and appropriate decisions depending on the predicted COVID situation.

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