scholarly journals HUMAN MOBILITY DURING THE COVID-19 PANDEMIC IN WESTERN BALKAN COUNTRIES

2021 ◽  
Author(s):  
Srđan Damjanović ◽  
◽  
Predrag Katanić ◽  
Vesna Petrović ◽  
◽  
...  
Keyword(s):  
Human Mobility ◽  
Daily Basis ◽  
Disease Spread ◽  
Handheld Devices ◽  
The Balkans ◽  
Freedom Of Movement ◽  
Mobility Data ◽  
The World ◽  
History Of ◽  
Chinese Province

At the end of 2019, a new coronavirus appeared in the Chinese province of Wuhan, causing the appearance of the disease COVID-19. The disease spread very quickly to other countries in the world, including the Balkans. The governments of many countries have decided to combat the spread of the COVID-19 virus in the community through social distancing measures. Decisions to ban the movement of people were easy to make, but they were very difficult to implement and enforce in practice. Some of the countries monitored their citizens through various applications installed on smartphones. This led to criticism by many NGOs, as they felt that this violated basic human rights of freedom of movement and privacy. Some lawsuits were even filed in the courts because the citizens felt that they were denied rights guaranteed by the respective constitution. Google uses the ability to monitor all those citizens around the world on a daily basis who use smartphones or handheld devices, which provide the option to record the "location history" of the users. This is possible for them, since most people have voluntarily agreed to this option on their devices. In early 2020, Google began publishing global mobility data on a daily basis through a report called “Community Mobility Reports”. The report shows the percentage change in human activity at six grouped locations. Data obtained in the reference days before the outbreak of the COVID-19 pandemic are used as a basis for comparison. In this paper, we studied the dynamics of human mobility during the COVID-19 pandemic in 7 countries of the Balkans: Bosnia and Herzegovina, Serbia, Croatia, North Macedonia, Bulgaria, Greece, and Romania. For Montenegro and Albania Google did not provide data on human mobility. We present the processed data graphically. For all examined countries, we statistically analyzed the obtained data and presented them in a table.

scholarly journals Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Urban Areas ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Epidemic Spread ◽  
Long Distance ◽  
Mobility Data ◽  
The World ◽  
Travel Restrictions ◽  
Long Distance Movement

AbstractIn response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences.

COVID-19 Pandemic: Experiences in China and Implications for its Prevention and Treatment Worldwide

2020 ◽  
Vol 20 (6) ◽  
pp. 410-416 ◽  
Author(s):  
Ning-Ning Liu ◽  
Jing-Cong Tan ◽  
Jingquan Li ◽  
Shenghui Li ◽  
Yong Cai ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
International Community ◽  
Disease Spread ◽  
Global Dynamics ◽  
The World ◽  
Prevention And Treatment ◽  
History Of

The outbreak of COVID-19 due to SARS-CoV-2 originally emerged in Wuhan in December 2019. As of March 22, 2020, the disease spread to 186 countries, with at least 305,275 confirmed cases. Although there has been a decline in the spread of the disease in China, the prevalence of COVID-19 around the world remains serious despite containment efforts undertaken by national authorities and the international community. In this article, we systematically review the brief history of COVID-19 and its epidemic and clinical characteristics, highlighting the strategies used to control and prevent the disease in China, which may help other countries respond to the outbreak. This pandemic emphasizes the need to be constantly alert to shifts in both the global dynamics and the contexts of individual countries, making sure that all are aware of which approaches are successful for the prevention, containment and treatment of new diseases, and being flexible enough to adapt the responses accordingly.

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 Universal predictability of mobility patterns in cities

2014 ◽  
Vol 11 (100) ◽  
pp. 20140834 ◽  
Author(s):  
Xiao-Yong Yan ◽  
Chen Zhao ◽  
Ying Fan ◽  
Zengru Di ◽  
Wen-Xu Wang
Keyword(s):  
Spatial Scales ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Universal Prediction ◽  
Potential Applications ◽  
Mobility Behaviour ◽  
History Of ◽  
Different Characteristics ◽  
The City

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.

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 Public Mobility Data Enables COVID-19 Forecasting and Management at Local and Global Scales

2020 ◽  
Author(s):  
Cornelia Ilin ◽  
Sébastien Annan-Phan ◽  
Xiao Hui Tai ◽  
Shikhar Mehra ◽  
Solomon Hsiang ◽  
...  
Keyword(s):  
Statistical Models ◽  
Human Mobility ◽  
The United States ◽  
Decision Makers ◽  
Disease Dynamics ◽  
Epidemiological Models ◽  
National Data ◽  
Mobility Data ◽  
The World ◽  
Pharmaceutical Interventions

AbstractPolicymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility — collected by Google, Facebook, and other providers — can be used to evaluate the effectiveness of non-pharmaceutical interventions and forecast the spread of COVID-19. This approach relies on simple and transparent statistical models, and involves minimal assumptions about disease dynamics. We demonstrate the effectiveness of this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world.SummaryBackgroundPolicymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. In some contexts, decision-makers have access to sophisticated epidemiological models and detailed case data. However, a large number of decisions, particularly in low-income and vulnerable communities, are being made with limited or no modeling support. We examine how public human mobility data can be combined with simple statistical models to provide near real-time feedback on non-pharmaceutical policy interventions. Our objective is to provide a simple framework that can be easily implemented and adapted by local decision-makers.MethodsWe develop simple statistical models to measure the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19 at local, state, and national levels. The method integrates concepts from econometrics and machine learning, and relies only upon publicly available data on human mobility. The approach does not require explicit epidemiological modeling, and involves minimal assumptions about disease dynamics. We evaluate this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world.FindingsWe find that NPIs are associated with significant reductions in human mobility, and that changes in mobility can be used to forecast COVID-19 infections. The first set of results show the impact of NPIs on human mobility at all geographic scales. While different policies have different effects on different populations, we observed total reductions in mobility between 40 and 84 percent. The second set of results indicate that — even in the absence of other epidemiological information — mobility data substantially improves 10-day case rates forecasts at the county (20.75% error, US), state (21.82 % error, US), and global (15.24% error) level. Finally, for example, country-level results suggest that a shelter-in-place policy targeting a 10% increase in the amount of time spent at home would decrease the propagation of new cases by 32% by the end of a 10 day period.InterpretationIn rapidly evolving disease outbreaks, decision-makers do not always have immediate access to sophisticated epidemiological models. In such cases, valuable insight can still be derived from simple statistic models and readily-available public data. These models can be quickly fit with a population’s own data and updated over time, thereby capturing social and epidemiological dynamics that are unique to a specific locality or time period. Our results suggest that this approach can effectively support decision-making from local (e.g., city) to national scales.

Halil İnalcık 1916? - 2016

2017 ◽  
Vol 51 (1) ◽  
pp. 163-165
Author(s):  
Linda T. Darling
Keyword(s):  
Early Modern ◽  
Ottoman Empire ◽  
North Africa ◽  
World History ◽  
The Balkans ◽  
Modern Period ◽  
The Us ◽  
The World ◽  
History Of ◽  
Crucial Part

Halil İnalcık was born in Istanbul, the capital of the Ottoman Empire, into a refugee family, probably in 1916 (he did not know his birthday; in Turkey he adopted 29 May, in the US 4 July). He died at age 100 in Ankara on 25 July 2016, as the premier Ottoman historian in the world. To quote one of his students, “Professor İnalcık transformed the field of Ottoman studies from an obscure and exotic subfield into one of the leading historical disciplines that covers the history of the greater Middle East and North Africa as well as the Balkans from the late medieval to the modern period. He set the tone of debate and critical inquiry from the early modern to the modern period.” Born an Ottoman, he made Ottoman studies a crucial part of world history.

Introduction

2020 ◽  
pp. 1-13
Author(s):  
Luca Scholz
Keyword(s):  
Roman Empire ◽  
Early Modern ◽  
Human Mobility ◽  
Political Authority ◽  
Holy Roman Empire ◽  
Free Movement ◽  
Old Regime ◽  
Freedom Of Movement ◽  
History Of ◽  
New Perspective

In the politically dense and fractured landscape of the Holy Roman Empire, safe conduct provided a key framework for negotiating freedom of movement and its restriction. The introduction sets out how the book uses safe conduct to approach the Holy Roman Empire in a spatial rather than diachronic perspective. It describes how authorities in the Empire restricted, promoted, and channelled different forms of mobility for political, fiscal, and symbolic reasons. Spatially these efforts were rarely concentrated at borders, which challenges anachronistic assumptions about the functioning of early modern borders. Conflicts around the enclosure of movement led to controversial debates around the rulers’ right to interfere with human mobility, adding a new chapter to the history of free movement. Drawing on manuscript, visual, and printed sources as well as self-designed maps, this book offers a new perspective on the unstable relation of political authority and human mobility in the heartlands of old-regime Europe.

Casinos in Asia: legalizing gambling in the Chinese province of Hainan, the ideological obstacle

2016 ◽  
Vol 10 (1) ◽  
pp. 1-13
Author(s):  
Sebastien Goulard
Keyword(s):  
Literature Review ◽  
Provincial Government ◽  
Religious Movements ◽  
Gaming Industry ◽  
Similar Role ◽  
Western Countries ◽  
The World ◽  
History Of ◽  
Chinese Province ◽  
New Policies

The successful liberalization of the gaming industry in Macau has incited other Asian destinations to reconsider their position on gambling. For the last ten years, new gaming destinations have emerged in Asia. China has still not yet legalized this industry, but since last year, new policies authorizing sport lotteries have been introduced in the province of Hainan. The objective of this measure is to diversify tourism in that province and make Hainan more attractive for visitors. In case of success, this policy could be expanded to other gaming activities and casinos would be legalized in Hainan, as advocated by the provincial government; but legalizing casinos in Hainan may face several obstacles.This paper aims at showing that the success of Asian casino destinations mainly results from China’s ban on gambling. This study will focus on the history of gambling in China, and the reasons for the continuation of gambling prohibition in China. The author will argue that the ideological opposition plays a similar role as religious movements in Western countries. The paper will be illustrated by a literature review analyzing previous examples in other parts of the world. 

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