scholarly journals Socioeconomic differences and persistent segregation of Italian territories during COVID-19 pandemic

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giovanni Bonaccorsi ◽  
Francesco Pierri ◽  
Francesco Scotti ◽  
Andrea Flori ◽  
Francesco Manaresi ◽  
...  
Keyword(s):  
Human Mobility ◽  
Economic Consequences ◽  
Massive Data ◽  
Mobility Patterns ◽  
Data Set ◽  
Economic Segregation ◽  
Massive Data Set ◽  
Learning Techniques ◽  
Quantile Regression Analysis ◽  
Administrative Units

AbstractLockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic.

scholarly journals The promiscuous and highly mobile resistome of a superbug

2021 ◽  
Author(s):  
Ismael Hernández-González ◽  
Valeria Mateo-Estrada ◽  
Santiago Castillo-Ramírez
Keyword(s):  
Population Dynamics ◽  
Gene Content ◽  
Massive Data ◽  
Nosocomial Pathogen ◽  
Data Set ◽  
Massive Data Set ◽  
The Individual ◽  
Global Threat ◽  
And Control ◽  
Global Population

AbstractAntimicrobial resistance (AR) is a major global threat to public health. Understanding the population dynamics of AR is critical to restrain and control this issue. However, no study has provided a global picture of the resistome of Acinetobacter baumannii, a very important nosocomial pathogen. Here we analyze 1450+ genomes (covering > 40 countries and > 4 decades) to infer the global population dynamics of the resistome of this species. We show that gene flow and horizontal transfer have driven the dissemination of AR genes in A. baumannii. We found considerable variation in AR gene content across lineages. Although the individual AR gene histories have been affected by recombination, the AR gene content has been shaped by the phylogeny. Furthermore, many AR genes have been transferred to other well-known pathogens, such as Pseudomonas aeruginosa or Klebsiella pneumoniae. Finally, despite using this massive data set, we were not able to sample the whole diversity of AR genes, which suggests that this species has an open resistome. Ours results highlight the high mobilization risk of AR genes between important pathogens. On a broader perspective, this study gives a framework for an emerging perspective (resistome-centric) on the genome epidemiology (and surveillance) of bacterial pathogens.

scholarly journals Mobility-based real-time economic monitoring amid the COVID-19 pandemic

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessandro Spelta ◽  
Paolo Pagnottoni
Keyword(s):  
Real Time ◽  
Industrial Production ◽  
Human Mobility ◽  
Substitution Effect ◽  
Economic Consequences ◽  
Economic Losses ◽  
Real Economy ◽  
Mobility Patterns ◽  
Economic Activities ◽  
The Real

AbstractMobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries’ economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities.

scholarly journals Similarity of TIMSS Math and Science Achievement of Nations

2001 ◽  
Vol 9 ◽  
pp. 33 ◽  
Author(s):  
Algirdas Zabulionis
Keyword(s):  
Science Achievement ◽  
Educational Achievement ◽  
International Association ◽  
Massive Data ◽  
Science Study ◽  
Data Set ◽  
Test Items ◽  
Massive Data Set ◽  
Math And Science ◽  
Mathematics And Science

In 1991-97, the International Association for the Evaluation of Educational Achievement (IEA) undertook a Third International Mathematics and Science Study (TIMSS) in which data about the mathematics and science achievement of the thirteen year-old students in more than 40 countries were collected. These data provided the opportunity to search for patterns of students' answers to the test items: which group of items was relatively more difficult (or more easy) for the students from a particular country (or group of countries). Using this massive data set an attempt was made to measure the similarities among country profiles of how students responded to the test items.

Research on MapReduce-Based Rocchio Relevance Feedback in Massive Information Filtering

2014 ◽  
Vol 962-965 ◽  
pp. 2712-2715
Author(s):  
Wen Chuan Yang ◽  
Zhi Dong Shang ◽  
Zhi Cheng Zhang
Keyword(s):  
Relevance Feedback ◽  
Information Filtering ◽  
Classification Algorithms ◽  
Massive Data ◽  
Data Set ◽  
Massive Data Set ◽  
Hadoop Cluster ◽  
Feedback Algorithm ◽  
Massive Information ◽  
Mapreduce Paradigm

Traditional text classification algorithms have vital impact on information filtering. However, their performances were confined to a large extent in terms of the massive data set. This paper proposes an approach using MapReduce-based Rocchio relevance feedback algorithm, which improved the traditional Rocchio algorithm in the MapReduce paradigm, to resolve the problem of massive information filtering. The experiments on Hadoop cluster showed an effective improvement in performance by using the new method.

scholarly journals The effect of mobility restrictions on the SARS-CoV-2 diffusion during the first wave: what are the impacts in Sweden, USA, France and Colombia

2021 ◽  
Author(s):  
Telle Olivier ◽  
Samuel Benkimoun ◽  
Richard Paul
Keyword(s):  
Social Media ◽  
Human Mobility ◽  
Great Majority ◽  
Control Measures ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Usa ◽  
Administrative Units ◽  
Individual Scale ◽  
The Individual

ResuméCombined with sanitation and social distancing measures, control of human mobility has quickly been targeted as a major leverage to contain the spread of SARS-CoV-2 in a great majority of countries worldwide. The extent to which such measures were successful, however, is uncertain (Gibbs et al. 2020; Kraemer et al. 2020). Very few studies are quantifying the relation between mobility, lockdown strategies and the diffusion of the virus in different countries. Using the anonymised data collected by one of the major social media platforms (Facebook) combined with spatial and temporal Covid-19 data, the objective of this research is to understand how mobility patterns and SARS-CoV-2 diffusion during the first wave are connected in four different countries: the west coast of the USA, Colombia, Sweden and France. Our analyses suggest a relatively modest impact of lockdown on the spread of the virus at the national scale. Despite a varying impact of lockdown on mobility reduction in these countries (83% in France and Colombia, 55% in USA, 10% in Sweden), no country successfully implemented control measures to stem the spread of the virus. As observed in Hubei (Chinazzi et al. 2020), it is likely that the virus had already spread very widely prior to lockdown; the number of affected administrative units in all countries was already very high at the time of lockdown despite the low testing levels. The second conclusion is that the integration of mobility data considerably improved the epidemiological model (as revealed by the QAIC). If inter-individual contact is a fundamental element in the study of the spread of infectious diseases, it is also the case at the level of administrative units. However, this relational dimension is little understood beyond the individual scale mostly due to the lack of mobility data at this scale. Fortunately, these types of data are getting increasingly provided by social media or mobile operators, and they can be used to help administrations to observe changes in movement patterns and/or to better locate where to implement disease control measures such as vaccination (Pollina & Busvine 2020; Pullano et al. 2020; Romm et al. 2020).

Detection for Approximately Duplicate Records Based on Fuzzy Comprehensive Evaluation

2013 ◽  
Vol 397-400 ◽  
pp. 2464-2468
Author(s):  
Li Juan Zhou ◽  
Zhe Xiao
Keyword(s):  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Massive Data ◽  
Data Set ◽  
Comprehensive Evaluation Method ◽  
Attribute Weight ◽  
Massive Data Set ◽  
Composition Factors

To solve the problem of attribute weight determination in the approximately duplicate records, we put forward a method based on fuzzy comprehensive evaluation to get attribute weight in data set. We first perform an analysis of the composition factors of attribute. Then we carry out an evaluation of their rank. Finally, we make a determination of the attribute weight using the fuzzy comprehensive evaluation method, on the basis of which the approximately duplicate records are detected. Theoretical analysis and experimental results show that the method can objectively determine all attributes weight, and effectively detect the approximately duplicate records in massive data set.

Understanding the Utilization Characteristics of Bicycle-Sharing Systems in Underdeveloped Cities: A Case Study in Xuchang City, China

2017 ◽  
Vol 2634 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Yang Yang ◽  
Tiezhu Li ◽  
Tao Zhang ◽  
Wanyu Yang
Keyword(s):  
Massive Data ◽  
Data Set ◽  
Massive Data Set ◽  
Smart Card Data ◽  
Intercept Survey ◽  
Degree Of Satisfaction ◽  
Bicycle Sharing System ◽  
Survey Questionnaires

In recent years, a growing number of cities in China have successively rolled out bicycle-sharing systems to facilitate bicycle use, including not only metropolises but also some underdeveloped cities with populations of less than 1 million. One of those underdeveloped cities, Xuchang, launched its bicycle-sharing system in 2014. This service provides a convenient way for members to cycle for some of their short trips. Interest in the bicycle-sharing systems of metropolises is growing rapidly; however, studies on underdeveloped cities are still limited. This study investigated the factors influencing the adoption of a bicycle-sharing system in Xuchang, by analyzing massive smart card data from July 2014 to mid-April 2015 and 500 intercept survey questionnaires in April 2015. Different questions were ready for members and nonmembers in the questionnaires and the statistical results show the characteristics of users of the Xuchang bicycle-sharing system, including demographic characteristics, travel habits, and degree of satisfaction. Moreover, the space–time distribution characteristics of the Xuchang bicycle-sharing system were analyzed by dividing a massive data set into three groups: weekdays, weekends, and holidays. Results showed that compared with the clearly defined role of “resolve the last-kilometer problem” in a metropolis, bicycle-sharing in underdeveloped cities acts as an alternative way of transportation rather than a transfer traffic mode. Results also showed that bicycle-sharing systems gained more popularity in underdeveloped cities than in metropolises because of the smaller extent of egression, resident travel habits, the traffic environment, and so on.

scholarly journals Human mobility data from the BBC Pandemic project

2021 ◽  
Author(s):  
Andrew JK Conlan ◽  
Petra Klepac ◽  
Adam J Kucharski ◽  
Stephen Kissler ◽  
Maria L Tang ◽  
...  
Keyword(s):  
Human Mobility ◽  
Data Sets ◽  
Mobility Patterns ◽  
Administrative District ◽  
Data Set ◽  
Public Science ◽  
Mobility Data ◽  
The United Kingdom ◽  
Television Documentary ◽  
Gps Trajectories

AbstractWe present human mobility data for the United Kingdom collected from the “BBC Pandemic”, a public science project linked to the BBC Four television documentary of the same name. Mobile phone GPS trajectories submitted by users and collected over a 24 hour period were aggregated to construct anonymised origin-destination flux matrices at the local administrative district (LAD). We use these data to explore how mobility patterns change with age and employment status - unique stratifications that are not available from other publicly and privately held mobility data sets. We validate the consistency of the aggregated BBC mobility data set against census workflow data and illustrate how the systematic differences in mobility rates with age affect the risk and pattern of transmission between regions with 18-30 year old’s contributing the greatest risk of transmission to adjacent regions, but older 60-100 years playing the most important role in more remote low-density locations.

scholarly journals Prediction and optimization of sharing bikes queuing model in grid of Geohash coding

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1250-1266
Author(s):  
Kui Yu ◽  
Changyue Qu
Keyword(s):  
Support Vector Machine ◽  
Human Mobility ◽  
Expectation Maximization Algorithm ◽  
Support Vector ◽  
Mobility Patterns ◽  
Queuing Model ◽  
Data Set ◽  
Queuing Models ◽  
Imbalance Problem ◽  
Bike Sharing

Dockless bike-sharing systems provide parking anywhere feature and environment-friendly approach for commuter. It is booming all over the world. Different from dockless bike-sharing systems, for example, previous studies focus on rental mode and docking stations planning. Yet, due to the fact that human mobility patterns of temporal and geographic lead to bike imbalance problem, we modeled human mobility patterns, predicted bike usage, and optimized management of the bike-sharing service. First, we proposed adaptive Geohash-grid clustering to classify bike flow patterns. For simplicity and rapid modeling, we defined three queuing models: over-demand, self-balance, and over-supply. Second, we improved adaptive Geohash-grid clustering-support vector machine algorithm to recognize self-balance pattern. Third, based on the result of adaptive Geohash-grid clustering-support vector machine, we proposed Markov state prediction model and Poisson mixture model expectation-maximization algorithm. Based on data set from Mobike and OFO, we conduct experiments to evaluate models. Results show that our models offer better prediction and optimization performance.

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