scholarly journals Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

2021 ◽  
Author(s):  
Zakaria Mehrab ◽  
Mandy L. Wilson ◽  
Serina Y Chang ◽  
Galen Harrison ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Real Time ◽  
Rural Areas ◽  
Census Data ◽  
Heterogeneous Data ◽  
Vaccine Uptake ◽  
Data Driven ◽  
Vaccination Site ◽  
Demographic Groups ◽  
Fine Grained ◽  
Us Census

The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings.

scholarly journals Rapid Multi-Dimensional Impact Assessment of Floods

2020 ◽  
Vol 12 (10) ◽  
pp. 4246 ◽  
Author(s):  
David Pastor-Escuredo ◽  
Yolanda Torres ◽  
María Martínez-Torres ◽  
Pedro J. Zufiria
Keyword(s):  
Social Media ◽  
Impact Assessment ◽  
Rural Areas ◽  
Disaster Response ◽  
Heterogeneous Data ◽  
Data Driven ◽  
Sensor Data ◽  
Mobile Phone Data ◽  
Aggregated Data ◽  
The Impact

Natural disasters affect hundreds of millions of people worldwide every year. The impact assessment of a disaster is key to improve the response and mitigate how a natural hazard turns into a social disaster. An actionable quantification of impact must be integratively multi-dimensional. We propose a rapid impact assessment framework that comprises detailed geographical and temporal landmarks as well as the potential socio-economic magnitude of the disaster based on heterogeneous data sources: Environment sensor data, social media, remote sensing, digital topography, and mobile phone data. As dynamics of floods greatly vary depending on their causes, the framework may support different phases of decision-making during the disaster management cycle. To evaluate its usability and scope, we explored four flooding cases with variable conditions. The results show that social media proxies provide a robust identification with daily granularity even when rainfall detectors fail. The detection also provides information of the magnitude of the flood, which is potentially useful for planning. Network analysis was applied to the social media to extract patterns of social effects after the flood. This analysis showed significant variability in the obtained proxies, which encourages the scaling of schemes to comparatively characterize patterns across many floods with different contexts and cultural factors. This framework is presented as a module of a larger data-driven system designed to be the basis for responsive and more resilient systems in urban and rural areas. The impact-driven approach presented may facilitate public–private collaboration and data sharing by providing real-time evidence with aggregated data to support the requests of private data with higher granularity, which is the current most important limitation in implementing fully data-driven systems for disaster response from both local and international actors.

scholarly journals DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

2020 ◽  
Author(s):  
Alexander Rodríguez ◽  
Anika Tabassum ◽  
Jiaming Cui ◽  
Jiajia Xie ◽  
Javen Ho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Noisy Data ◽  
Sparse Data ◽  
Heterogeneous Data ◽  
Exploratory Analysis ◽  
Data Driven ◽  
Short Term ◽  
Learning Framework ◽  
Disease Exposure

AbstractHow do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCovid, an operational deep learning framework designed for real-time COVID-19 forecasting. Deep-Covid works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

Why Online Retail Sales are Much Larger than US Census Data Report

2016 ◽  
Author(s):  
David S. Evans ◽  
Scott R. Murray ◽  
Richard Schmalensee
Keyword(s):  
Census Data ◽  
Retail Sales ◽  
Online Retail ◽  
Us Census

Design & Real-time Implementation of MIDO Converter Based PV Water Pumping System with BES

2020 ◽  
Vol 14 (2) ◽  
pp. 194-204
Author(s):  
Anuradha Tomar
Keyword(s):  
Real Time ◽  
Rural Areas ◽  
Maximum Power ◽  
Pump System ◽  
Partial Shading ◽  
Power Extraction ◽  
Pumping System ◽  
Point Tracking ◽  
Water Pumping ◽  
Battery Energy

Background: Despite so many developments, most of the farmers in the rural areas are still dependent on rainwater, rivers or water wells, for irrigation, drinking water etc. The main reason behind such dependency is non-connectivity with the National grid and thus unavailability of electricity. To extract the maximum power from solar photovoltaic (SPV) based system, implementation of Maximum Power Point Tracking (MPPT) is mandatory. PV power is intermittent in nature. Variation in the irradiation level due to partial shading or mismatching phenomena leads to the development of modular DC-DC converters. Methods: A stand-alone Multi-Input Dual-Output (MIDO) DC-DC converter based SPV system, is installed at a farm; surrounded with plants for water pumping with stable flow (not pulsating) along with battery energy storage (BES) for lighting. The proposed work has two main objectives; first to maximize the available PV power under shadowing and mismatching condition in case of series/ parallel connected PV modules and second is to improve the utilization of available PV energy with dual loads connected to it. Implementation of proposed MIDO converter along with BES addresses these objectives. First, MIDO controller ensures the MPPT operation of the SPV system to extract maximum power even under partial shading condition and second, controls the power supplied to the motor-pump system and BES. The proposed system is simulated in MATLAB/ SIMULINK environment. Real-time experimental readings under natural sun irradiance through hardware set-up are also taken under dynamic field conditions to validate the performance. Results and Conclusion: The inherent advantage of individual MPPT of each PV source in MIDO configuration, under varying shadow patterns due to surrounding plants and trees is added to common DC bus and therefore provides a better impact on PV power extraction as compared to conventional PV based water pumping system. Multi-outputs at different supply voltages is another flag of MIDO system. Both these aspects are implemented and working successfully at 92.75% efficiency.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals IoT-enabled directed acyclic graph in spark cluster

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Jahwan Koo ◽  
Nawab Muhammad Faseeh Qureshi ◽  
Isma Farah Siddiqui ◽  
Asad Abbas ◽  
Ali Kashif Bashir
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Directed Acyclic Graph ◽  
Random Access ◽  
Heterogeneous Data ◽  
The Body ◽  
Computing Environment ◽  
Time Data ◽  
Acyclic Graph ◽  
Sensory Data

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

scholarly journals How Does Administrative Data Compare with Census Data for the Measurement of Internal Migration? The Case of Northern Ireland, 2001–2011

2021 ◽  
Author(s):  
Brian Foley ◽  
Tony Champion ◽  
Ian Shuttleworth
Keyword(s):  
Administrative Data ◽  
Internal Migration ◽  
Census Data ◽  
Local Population ◽  
Population Data ◽  
Population Estimates ◽  
Demographic Groups ◽  
Small Areas ◽  
Intercensal Period ◽  
Statistical Agencies

AbstractThe paper compares and contrasts internal migration measured by healthcard-based administrative data with census figures. This is useful because the collection of population data, its processing, and its dissemination by statistical agencies is becoming more reliant on administrative data. Statistical agencies already use healthcard data to make migration estimates and are increasingly confident about local population estimates from administrative sources. This analysis goes further than this work as it assesses how far healthcard data can produce reliable data products of the kind to which academics are accustomed. It does this by examining migration events versus transitions over a full intercensal period; population flows into and out of small areas; and the extent to which it produces microdata on migration equivalent to that in the census. It is shown that for most demographic groups and places healthcard data is an adequate substitute for census-based migration counts, the exceptions being for student households and younger people. However, census-like information is still needed to provide covariates for analysis and this will still be required whatever the future of the traditional census.

scholarly journals Inequalities in life expectancy by place of residence and its changes in Lithuania during 1990-2018

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Sauliune ◽  
O Mesceriakova-Veliuliene ◽  
R Kalediene
Keyword(s):  
Life Expectancy ◽  
Rural Areas ◽  
Place Of Residence ◽  
Entire Study ◽  
Demographic Groups ◽  
National Mortality ◽  
Health Related ◽  
Urban Rural ◽  
Notable Increase ◽  
Males And Females

Abstract Introduction Health inequalities have emerged as a big issue of public health in Lithuania. Recent studies have demonstrated increasing mortality differentials between different socio-demographic groups of the population. Urban/rural place of residence is related with a set of socio-economic characteristics, different access to material resources, presence or absence of social support, and attitudes to health-related behavior. The aim of the study To determine inequalities in life expectancy and its changes by place of residence (urban/rural) in Lithuania during 1990-2018. Methods Information on deaths and population numbers for the period of 1990-2018 was obtained from National Mortality Register and Population Register. Life expectancy for males and females of urban and rural populations was calculated using life tables. Changes in the magnitude of life expectancy inequalities by place of residence were assessed using rate differences (urban-rural); while trends in inequalities were estimated by conducting the Joinpoint regression analysis. Results Life expectancy among males and females was longer in urban compared to rural areas throughout the entire study period. Life expectancy increased statistically significantly for urban and rural males and females with the most notable increase for males, especially those living in rural areas (on average by 0.4% per year from 64.1 years in 1990 to 70.05 years in 2018). Inequalities in life expectancy by place of residence decreased statistically significantly among Lithuanian males from 3.48 years in 1990 to 1.39 years in 2018, while among females only the tendency of decrease was estimated. Conclusions Inequalities in life expectancy of males and females by place of residence decreased significantly in Lithuania throughout the period of 1990-2018, mainly due to positive changes in life expectancy among rural males. Key messages Inequalities in life expectancy of males and females by place of residence decreased significantly in Lithuania throughout the period of 1990-2018. Life expectancy increased for Lithuanian urban and rural males and females with the most notable increase for males, especially those living in rural areas.

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