scholarly journals Human mobility and COVID-19 initial dynamics

2020 ◽  
Vol 101 (3) ◽  
pp. 1901-1919 ◽  
Author(s):  
Stefano Maria Iacus ◽  
Carlos Santamaria ◽  
Francesco Sermi ◽  
Spyros Spyratos ◽  
Dario Tarchi ◽  
...  
Keyword(s):  
Coming Out ◽  
Human Mobility ◽  
Mobile Network ◽  
Systematic Analysis ◽  
Mobile Data ◽  
Eu Member States ◽  
Mobility Data ◽  
Internal Mobility ◽  
The Impact ◽  
Excess Deaths

Abstract Countries in Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked mobile network operators to share on a voluntarily basis anonymised and aggregate mobile data to improve the quality of modelling and forecasting for the pandemic at EU level. In fact, mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level, respectively. Using this information together with anonymised and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14–20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating the best data-driven approaches for coming out of confinement and mostly in building future scenarios in case of new outbreaks.

scholarly journals Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaobin Wang ◽  
Yun Tong ◽  
Yupeng Fan ◽  
Haimeng Liu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Impact Analysis ◽  
Human Mobility ◽  
Economic Activities ◽  
Underdeveloped Countries ◽  
Mobility Data ◽  
Long Run ◽  
Level Of Economic Development ◽  
Human World ◽  
Dynamic Relationships ◽  
The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

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.

Sponsored Data: Smarter Data Pricing with Incomplete Information

2021 ◽  
Author(s):  
Xiaowei Mei ◽  
Hsing Kenneth Cheng ◽  
Subhajyoti Bandyopadhyay ◽  
Liangfei Qiu ◽  
Lai Wei
Keyword(s):  
Consumer Surplus ◽  
Mobile Network ◽  
Complete Information ◽  
Consumer Welfare ◽  
High Definition ◽  
Mobile Data ◽  
Data Intensive ◽  
Data Consumption ◽  
New Business ◽  
The Impact

With the development of data-intensive internet services, the world has witnessed explosive growth in mobile data consumption during the last couple of years. The upcoming generation of 5G-capable phones and networks will continue and even accelerate that process. At the same time, consumers are becoming more conscious about their data consumption because their monthly caps of mobile data plans can be easily exhausted by premium content, such as high-definition videos and virtual-reality games. In response, the mobile network operators (MNOs) have proposed a new business model, the so-called sponsored data plans, to subsidize consumers by transferring at least part of the data bills from consumers to content providers. Although industry practitioners claim that sponsored data plans increase consumer welfare, our analysis reveals that the impact of sponsored data on consumer surplus depends crucially on whether the MNO has complete information of the consumers’ valuation of mobile data. Our analysis helps provide a clearer picture of the impact of sponsored data on consumer surplus while reconciling the conflicting views from scholars, digital rights groups, and the network carriers.

scholarly journals The Impact of Remittances of Ukrainian Migrants on the Stabilisation of the Economy During the Covid Crisis

2021 ◽  
pp. 99-114
Author(s):  
Iryna Gerlach ◽  
Lilia Ukraynets
Keyword(s):  
Migrant Workers ◽  
Foreign Currency ◽  
Human Mobility ◽  
Labour Migration ◽  
Human Potential ◽  
Eu Member States ◽  
Poverty And Inequality ◽  
International Labour Migration ◽  
Migrant Remittances ◽  
The Impact

International labour migration is a global process that has affected the populations of all countries and continents. Today, migrants face a new additional barrier – the COVID-19 pandemic, which has drastically affected all forms of human mobility. Today, labour-based migration from Ukraine is directed mainly to EU Member States. The main motives for international labour migration is the potential of earning money, financial security of the family etc. Accordingly, the consequence of international migration is the inflow of money into the country of migrant origin. Remittances from migrant workers are an important source of income for Ukraine. Despite the COVID-19 pandemic, there has been an increase in migrant remittances, contributing to the development of households, human potential, the reduction of poverty and inequality, and the inflow of foreign currency. This article proposes an econometric model of the impact of migrant remittances on the stabilisation of the country’s economy during the pandemic. As a result of the calculations, it was found that the income from people working abroad initially reduces the deviation of GDP from the equilibrium trend, but over time there is a clear procyclical impact. This feature is favourable for the Ukrainian economy and may mean that migrant transfers in times of crisis help to overcome the effects of global economic downturns.

scholarly journals The effect of human mobility and control measures on the COVID-19 epidemic in China

2020 ◽  
Author(s):  
Moritz U.G. Kraemer ◽  
Chia-Hung Yang ◽  
Bernardo Gutierrez ◽  
Chieh-Hsi Wu ◽  
Brennan Klein ◽  
...  
Keyword(s):  
Human Mobility ◽  
Control Measures ◽  
Human Populations ◽  
Travel History ◽  
Mobility Data ◽  
Travel Restrictions ◽  
State Interventions ◽  
And Control ◽  
The Impact

AbstractThe ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

scholarly journals Human Mobility and Epidemic Evolution

2021 ◽  
Author(s):  
Fabio Vanni ◽  
David Lambert ◽  
Luigi Palatella ◽  
Paolo Grigolini
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Theoretical Framework ◽  
Infection Age ◽  
Mobility Data ◽  
Risk Of Disease ◽  
Age Structured ◽  
Using Data ◽  
Age Structured Model ◽  
The Impact

Abstract The CoViD-19 pandemic ceased to be describable by a susceptible-infected-recovered (SIR) model when lockdowns were enforced. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 (Sudden Acute Respiratory Syndrome Coronavirus 2) in terms of individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

scholarly journals On the use of aggregated human mobility data to estimate the reproduction number

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fabio Vanni ◽  
David Lambert ◽  
Luigi Palatella ◽  
Paolo Grigolini
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Theoretical Framework ◽  
Infection Age ◽  
Mobility Data ◽  
Risk Of Disease ◽  
Age Structured ◽  
Using Data ◽  
Age Structured Model ◽  
The Impact

AbstractThe reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

Modeling geographical spread of COVID-19 in India using network-based approach (Preprint)

2020 ◽  
Author(s):  
Ankush Kumar
Keyword(s):  
Human Mobility ◽  
Uttar Pradesh ◽  
Decisive Role ◽  
Northeast India ◽  
North India ◽  
Mobility Patterns ◽  
Large Country ◽  
Geographical Spread ◽  
Internal Mobility ◽  
The Impact

BACKGROUND COVID-19 pandemic is a global concern, due to its high spreading and alarming fatality rate. Mathematical models can play a decisive role in mitigating the spread and predicting the growth of the epidemic. India is a large country, with a highly variable inter-state mobility, and dynamically varying infection cases in different locations; thus, the existing models, based solely on the aspects of growth rates, or generalized network concepts, may not provide desired predictions. The internal mobility of a country must be considered, for accurate prediction. OBJECTIVE This study aims to propose a framework for predicting the geographical spread of COVID-19 based on human mobility, by incorporating migration and transport statistics. The motivation of the research is to identify the locations, which can be at higher level COVID -19 spread risk, during migrants transfer and transportation activities. METHODS We use reported COVID-19 cases, census migration data, and monthly airline data of passengers. RESULTS We discover that spreading depends on the spatial distribution of existing cases, human mobility patterns, and administrative decisions. In India, the mobility towards professional sites can surge incoming cases at Maharastra and Karnataka, while migration towards the native places can risk Uttar Pradesh and Bihar. We anticipate that the state Kerala, with one of the highest cases of COVID-19, may not receive significant incoming cases, while Karnataka and Haryana may receive the challenge of high incoming cases, with medium cases so far. Using airline passenger's data, we also estimate the number of potential incoming cases at various airports. The study predicts that the airports located in the region of north India are vulnerable, whereas in the northeast India and in some south India are relatively safe. CONCLUSIONS A model is developed for systematically understanding the effect of migration and transport on the spreading of COVID-19, and predetermining the hotspots on real time basis. Through the model, we identified the airports and states that are at higher level of COVID-19 risk. The study can guide policymakers in prior planning of transport and estimate the required medical and quarantine facilities to minimize the impact of COVID-19.

scholarly journals Evaluating the impacts of non-pharmaceutical interventions on the transmission dynamics of COVID-19 in Canada based on mobile network

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261424
Author(s):  
Ling Xue ◽  
Shuanglin Jing ◽  
Hao Wang
Keyword(s):  
Complex Dynamics ◽  
Human Mobility ◽  
Mobile Network ◽  
Mitigation Strategies ◽  
Transmission Dynamics ◽  
Mobility Data ◽  
Cross Correlation Analysis ◽  
Public Health Authorities ◽  
Canadian Provinces ◽  
Pharmaceutical Interventions

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada’s provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.

Sign in / Sign up

Export Citation Format

Share Document