Food Insecurity in the US During the Pandemic: What Can We Learn from Real-Time Data?

2021 ◽  
Author(s):  
Sara Ayllón ◽  
Samuel Lado
Keyword(s):  
Food Insecurity ◽  
Real Time ◽  
Time Data ◽  
The Us ◽  
Real Time Data

Monetary Policy and Real-Time Data: The Case of Europe, Asia and the US

2006 ◽  
pp. 165-182
Author(s):  
Franz Seitz ◽  
Christina Gerberding ◽  
Andreas Worms
Keyword(s):  
Monetary Policy ◽  
Real Time ◽  
Time Data ◽  
The Us ◽  
Real Time Data

scholarly journals Internet Data Distribution – extending real-time data sharing throughout the Americas

2006 ◽  
Vol 8 ◽  
pp. 91-95 ◽  
Author(s):  
T. Yoksas ◽  
W. Gambi de Almeida ◽  
D. Garrana Coelho ◽  
V. Castro Leon ◽  
T. Spangler
Keyword(s):  
Real Time ◽  
Data Distribution ◽  
Primary Source ◽  
Time Data ◽  
Local Data ◽  
Science Data ◽  
The Us ◽  
Real Time Data ◽  
Reliable Mechanism ◽  
The University

Abstract. The Unidata Program Center (Unidata) of the University Corporation of Atmospheric Research (UCAR) is involved in three international collaborations whose goals are extension of real-time data delivery-to and sharing-of locally held datasets-by educational institutions throughout the Americas. These efforts are based on the use of Unidata's Internet Data Distribution (IDD) system which is built on top of its proven Local Data Manager Version 6 (LDM-6) technology. The Unidata IDD is an event-driven network of cooperating Unidata LDM servers that distributes discipline-neutral data products in near real-time over wide-area networks. The IDD, a collaboration of over 150 mostly North American institutions of higher education, has been the primary source of real-time atmospheric science data for the US university community for over a decade,. In addition to providing a highly reliable mechanism for delivering real-time data, the IDD allows users to easily share locally held datasets.

scholarly journals On the forecastability of food insecurity

2021 ◽  
Author(s):  
Pietro Foini ◽  
Michele Tizzoni ◽  
Daniela Paolotti ◽  
Elisa Omodei
Keyword(s):  
At Risk ◽  
Food Insecurity ◽  
Real Time ◽  
Food Consumption ◽  
Boosted Regression Trees ◽  
Extreme Weather Events ◽  
Forecasting Model ◽  
Time Data ◽  
Real Time Data ◽  
The Future

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, the proposed forecasting model makes it possible to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data
Sign in / Sign up

Export Citation Format

Share Document