scholarly journals How well can we estimate immigration trends using Google data?

2020 ◽  
Author(s):  
Philippe Wanner
Keyword(s):  
Big Data ◽  
Current Level ◽  
Data Sources ◽  
Access To Information ◽  
Short Term ◽  
Predictive Capacity ◽  
Correct Anticipation ◽  
Study Results ◽  
Alternative Approaches ◽  
Migration Flows

Abstract For a country to efficiently monitor international migration, quick access to information on migration flows is helpful. However, traditional data sources fail to provide immediate information on migration flows and do not facilitate the correct anticipation of these flows in the short term. To tackle this issue, this paper evaluates the predictive capacity of big data to estimate the current level or to predict short-term flows. The results show that Google Trends can provide information that reflects the attractiveness of Switzerland for to immigrants from different countries and predict, to some extent, current and future (short-term) migration flows of adults arriving from Spain or Italy. However, the predictions appear not to be satisfactory for other flows (from France and Germany). Additional studies based on alternative approaches are needed to validate or overturn our study results.

scholarly journals Poverty and soil conservation efforts among smallholder farmers in the central highlands of Ethiopia

2014 ◽  
Vol 9 (2) ◽  
pp. 244-261 ◽  
Author(s):  
C Yirga ◽  
RM Hassan
Keyword(s):  
Land Tenure ◽  
Soil Conservation ◽  
Soil Degradation ◽  
Smallholder Farmers ◽  
Policy Implications ◽  
Access To Information ◽  
Short Term ◽  
Study Results ◽  
Central Highlands

This study explores the influence of incidence of poverty and plot-level perception of soil degradation, on soil conservation behaviour of small subsistence farmers in the central highlands of Ethiopia. The study results confirm that poverty in assets significantly reduces the probability of soil-conservation efforts as measured by use of stone/soil bund structures in the highlands of Ethiopia. Perception of soil degradation, public assistance with sharing initial costs of constructing soil-conservation structures, improved security of land tenure and farmers’ education and access to information on soil degradation are essential for farmers making long-term investment in conserving soil resources. On the other hand, improved access to short-term credit for the purchase of inorganic fertilizers acts as a disincentive for long-term conservation practices, an important trade-off with serious policy implications that should be carefully evaluated.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

Mapping the United Nations Fundamental Principles of Official Statistics against new and big data sources

2021 ◽  
Vol 37 (1) ◽  
pp. 161-169
Author(s):  
Dominik Rozkrut ◽  
Olga Świerkot-Strużewska ◽  
Gemma Van Halderen
Keyword(s):  
Big Data ◽  
Public Information ◽  
Fundamental Principle ◽  
Data Sources ◽  
Official Statistics ◽  
Development Agenda ◽  
Data Gaps ◽  
Data Source ◽  
Exciting Time ◽  
Statistical Systems

Never has there been a more exciting time to be an official statistician. The data revolution is responding to the demands of the CoVID-19 pandemic and a complex sustainable development agenda to improve how data is produced and used, to close data gaps to prevent discrimination, to build capacity and data literacy, to modernize data collection systems and to liberate data to promote transparency and accountability. But can all data be liberated in the production and communication of official statistics? This paper explores the UN Fundamental Principles of Official Statistics in the context of eight new and big data sources. The paper concludes each data source can be used for the production of official statistics in adherence with the Fundamental Principles and argues these data sources should be used if National Statistical Systems are to adhere to the first Fundamental Principle of compiling and making available official statistics that honor citizen’s entitlement to public information.

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach

scholarly journals KIDNEY INJURY MOLECULE-1 AS AN EARLY AMIKACIN-INDUCED NEPHROTOXICITY MARKER IN PATIENTS WITH SEPSIS HOSPITALIZED IN THE INTENSIVE CARE UNIT

2019 ◽  
pp. 277-279
Author(s):  
TRISNI UNTARI DEWI ◽  
INSTIATY . ◽  
RUDIANTO SEDONO ◽  
GESTINA ALISKA ◽  
MUHAMMAD KHIFZHON AZWAR ◽  
...  
Keyword(s):  
Intensive Care ◽  
Kidney Injury ◽  
Term Treatment ◽  
Short Term ◽  
Short Term Treatment ◽  
Safe Level ◽  
Study Results ◽  
Early Biomarker ◽  
Kidney Injury Molecule 1 ◽  
Trough Plasma

Objective: This study sought to determine the correlation between trough plasma amikacin concentrations and urinary normalized kidney injurymolecule-1 (KIM-1) concentrations as an early biomarker of nephrotoxicity in patients with sepsis who are hospitalized in an intensive care unit.Methods: In this pilot study, 12 patients with sepsis were treated with amikacin 1000 mg/day between May 2015 and September 2015. The correlationbetween trough plasma amikacin concentrations measured after the third dose and the elevation of urinary normalized KIM-1 concentrations afterthe third amikacin dose relative to the first/second dose was evaluated.Results: In total, three patients had trough plasma amikacin concentrations exceeding the safe level (>10 μg/ml). Furthermore, eight patientsdisplayed higher normalized KIM-1 concentrations after third dose than after the first/second dose; however, there was no correlation betweentrough amikacin concentrations and the elevation of urinary normalized KIM-1 concentrations (r=0.3, p=0.3).Conclusion: The study results illustrated that short-term treatment with an amikacin dose of 1000 mg/day was generally safe in patients with sepsis.

scholarly journals Big Data sources and methods for social and economic analyses

2018 ◽  
Vol 130 ◽  
pp. 99-113 ◽  
Author(s):  
Desamparados Blazquez ◽  
Josep Domenech
Keyword(s):  
Big Data ◽  
Data Sources ◽  
Economic Analyses
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