scholarly journals Understanding components of mobility during the COVID-19 pandemic

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Peter Edsberg Møllgaard ◽  
Sune Lehmann ◽  
Laura Alessandretti
Keyword(s):  
Disease Surveillance ◽  
Data Science ◽  
Mobile Network ◽  
Travel Behaviour ◽  
Mobility Patterns ◽  
Infectious Disease Surveillance ◽  
Disease Propagation ◽  
Different Types ◽  
Travel Restrictions ◽  
Mobile Network Operators

Travel restrictions have proven to be an effective strategy to control the spread of the COVID-19 epidemics, in part because they help delay disease propagation across territories. The question, however, as to how different types of travel behaviour, from commuting to holiday-related travel, contribute to the spread of infectious diseases remains open. Here, we address this issue by using factorization techniques to decompose the temporal network describing mobility flows throughout 2020 into interpretable components. Our results are based on two mobility datasets: the first is gathered from Danish mobile network operators; the second originates from the Facebook Data-For-Good project. We find that mobility patterns can be described as the aggregation of three mobility network components roughly corresponding to travel during workdays, weekends and holidays, respectively. We show that, across datasets, in periods of strict travel restrictions the component corresponding to workday travel decreases dramatically. Instead, the weekend component, increases. Finally, we study how each type of mobility (workday, weekend and holiday) contributes to epidemics spreading, by measuring how the effective distance, which quantifies how quickly a disease can travel between any two municipalities, changes across network components. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals The hidden potential of call detail records in The Gambia

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Ayumi Arai ◽  
Erwin Knippenberg ◽  
Moritz Meyer ◽  
Apichon Witayangkurn
Keyword(s):  
Rural Areas ◽  
Best Practice ◽  
Mobile Network ◽  
Lessons Learned ◽  
Population Mobility ◽  
Mobility Patterns ◽  
End User ◽  
Aggregated Data ◽  
Data Pipeline ◽  
Mobile Network Operators

Abstract Aggregated data from mobile network operators (MNOs) can provide snapshots of population mobility patterns in real time, generating valuable insights when other more traditional data sources are unavailable or out-of-date. The COVID-19 pandemic has highlighted the value of remotely-collected, high-frequency, localized data in inferring the economic impact of shocks to inform decision-making. However, proper protocols must be put in place to ensure end-to-end user-confidentiality and compliance with international best practice. We demonstrate how to build such a data pipeline, channeling data from MNOs through the national regulator to the analytical users, who in turn produce policy-relevant insights. The aggregated indicators analyzed offer a detailed snapshot of the decrease in mobility and increased out-migration from urban to rural areas during the COVID-19 lockdown. Recommendations based on lessons learned from this process can inform engagements with other regulators in creating data pipelines to inform policy-making.

scholarly journals Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
James P. Gleeson ◽  
Thomas Brendan Murphy ◽  
Joseph D. O’Brien ◽  
Nial Friel ◽  
Norma Bargary ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Data Science ◽  
Statistical Modelling ◽  
Population Based ◽  
Advisory Group ◽  
Time Varying ◽  
Infectious Disease Surveillance ◽  
Spline Fitting ◽  
Effective Contact ◽  
History Of

We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals Extending 5G Capacity Planning Through Advanced Subscriber Behavior-Centric Clustering

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1385
Author(s):  
Gonçalves ◽  
Sebastião ◽  
Souto ◽  
Correia
Keyword(s):  
Resource Management ◽  
Capacity Planning ◽  
Data Science ◽  
Planning Process ◽  
Network Planning ◽  
Mobile Network ◽  
Network Capacity ◽  
5G Networks ◽  
Planning Processes ◽  
Mobile Network Operators

This work focuses on providing enhanced capacity planning and resource management for 5G networks through bridging data science concepts with usual network planning processes. For this purpose, we propose using a subscriber-centric clustering approach, based on subscribers’ behavior, leading to the concept of intelligent 5G networks, ultimately resulting in relevant advantages and improvements to the cellular planning process. Such advanced data-science-related techniques provide powerful insights into subscribers’ characteristics that can be extremely useful for mobile network operators. We demonstrate the advantages of using such techniques, focusing on the particular case of subscribers’ behavior, which has not yet been the subject of relevant studies. In this sense, we extend previously developed work, contributing further by showing that by applying advanced clustering, two new behavioral clusters appear, whose traffic generation and capacity demand profiles are very relevant for network planning and resource management and, therefore, should be taken into account by mobile network operators. As far as we are aware, for network, capacity, and resource management planning processes, it is the first time that such groups have been considered. We also contribute by demonstrating that there are extensive advantages for both operators and subscribers by performing advanced subscriber clustering and analytics.

scholarly journals Unequal impact and spatial aggregation distort COVID-19 growth rates

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Keith Burghardt ◽  
Siyi Guo ◽  
Kristina Lerman
Keyword(s):  
Growth Rates ◽  
Hot Spots ◽  
Disease Surveillance ◽  
Data Science ◽  
Spatial Scales ◽  
Spatial Aggregation ◽  
Mitigation Strategies ◽  
Policy Makers ◽  
Infectious Disease Surveillance ◽  
Know How

The COVID-19 pandemic has posed unprecedented challenges to public health world-wide. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how the disease is spreading in their communities. Here we analyse confirmed infections and deaths over multiple geographic scales to show that COVID-19’s impact is highly unequal: many regions have nearly zero infections, while others are hot spots. We attribute the effect to a Reed–Hughes-like mechanism in which the disease arrives to regions at different times and grows exponentially at different rates. Faster growing regions correspond to hot spots that dominate spatially aggregated statistics, thereby skewing growth rates at larger spatial scales. Finally, we use these analyses to show that, across multiple spatial scales, the growth rate of COVID-19 has slowed down with each surge. These results demonstrate a trade-off when estimating growth rates: while spatial aggregation lowers noise, it can increase bias. Public policy and epidemic modelling should be aware of, and aim to address, this distortion. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals Data science approaches to infectious disease surveillance

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Qingpeng Zhang
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Data Science ◽  
Swine Flu ◽  
Human Beings ◽  
Theme Issue ◽  
Infectious Disease Surveillance ◽  
Introductory Article

Novel data science approaches are needed to confront large-scale infectious disease epidemics such as COVID-19, human immunodeficiency viruses, African swine flu and Ebola. Human beings are now equipped with richer data and more advanced data analytics methodologies, many of which have become available only in the last decade. The theme issue Data Science Approaches to Infectious Diseases Surveillance reports the latest interdisciplinary research on developing novel data science methodologies to capitalize on the rich ‘big data’ of human behaviours to confront infectious diseases, with a particular focus on combating the ongoing COVID-19 pandemic. Compared to conventional public health research, articles in this issue present innovative data science approaches that were not possible without the growing human behaviour data and the recent advances in information and communications technology. This issue has 12 research papers and one review paper from a strong lineup of contributors from multiple disciplines, including data science, computer science, computational social sciences, applied maths, statistics, physics and public health. This introductory article provides a brief overview of the issue and discusses the future of this emerging field. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

scholarly journals On the use of data from multiple mobile network operators in Europe to fight COVID-19

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Michele Vespe ◽  
Stefano Maria Iacus ◽  
Carlos Santamaria ◽  
Francesco Sermi ◽  
Spyridon Spyratos
Keyword(s):  
Data Privacy ◽  
Mobile Network ◽  
Fundamental Rights ◽  
Mobility Patterns ◽  
Use Of Data ◽  
Mobile Positioning ◽  
Containment Measures ◽  
Anonymized Data ◽  
Government Initiative ◽  
Mobile Network Operators

Abstract The rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information regarding collective mobility patterns to inform de-escalation strategies as well as to provide forecasting capacity for re-escalation policies aiming at addressing further waves of the virus. Such information can be extracted using aggregate anonymized data from innovative sources such as mobile positioning data. This paper presents lessons learnt and results of a unique Business-to-Government initiative between several mobile network operators in Europe and the European Commission. Mobile positioning data have supported policy-makers and practitioners with evidence and data-driven knowledge to understand and predict the spread of the disease, the effectiveness of the containment measures, their socio-economic impacts while feeding scenarios at European Union scale and in a comparable way across countries. The challenges of these data sharing initiative are not limited to data quality, harmonization, and comparability across countries, however important they are. Equally essential aspects that need to be addressed from the onset are related to data privacy, security, fundamental rights, and commercial sensitivity.

scholarly journals A global assessment of the impact of school closure in reducing COVID-19 spread

2021 ◽  
Vol 380 (2214) ◽  
Author(s):  
Joseph T. Wu ◽  
Shujiang Mei ◽  
Sihui Luo ◽  
Kathy Leung ◽  
Di Liu ◽  
...  
Keyword(s):  
School Children ◽  
Disease Surveillance ◽  
Data Science ◽  
The Elderly ◽  
School Closure ◽  
Clinical Severity ◽  
School Closures ◽  
Infectious Disease Surveillance ◽  
The Impact ◽  
Academic Year

Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19–59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19. This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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