scholarly journals Use of ESSENCE APIs to Support Flexible Analysis and Reporting

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Aaron Kite-Powell ◽  
Wayne Loschen
Keyword(s):  
Time Series ◽  
User Interfaces ◽  
Disease Surveillance ◽  
Time Series Data ◽  
Application Programming Interface ◽  
Series Data ◽  
Control Mechanisms ◽  
Report Generation ◽  
Aggregated Data ◽  
Using Data

ObjectiveTo describe and provide examples of the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) application programming interface (API) as a part of disease surveillance workflows.IntroductionThe ESSENCE application supports users’ interactive analysis of data by clicking through menus in a user interface (UI), and provides multiple types of user defined data visualization options, including various charts and graphs, tables of statistical alerts, table builder functionality, spatial mapping, and report generation. However, no UI supports all potential analysis and visualization requirements. Rapidly accessing data processed through ESSENCE using existing access control mechanisms, but de-coupled from the UI, supports innovative analyses, visualizations and reporting of these data using other tools.MethodsThe ESSENCE API gives users the ability to query ESSENCE data and functionality via a Representational State Transfer (REST) web API designed to use HTTPs protocol. As with logging into the ESSENCE application normally, use of the API also requires users to authenticate with their username and password by including it in the code. This makes programmatic interfaces with the application possible whereby a tool or program makes a request to the API endpoint and the API returns the result of the query in a structured form. The ESSENCE API is a collection of endpoints that return different sets of data, including ESSENCE time series graphs, time series data, data details data, aggregated data created using the table builder functionality, number of unique facilities or regions (i.e. counties) reporting for a query, and results from the detector algorithms and alert list. All of the query parameter information is stored in the API URL, which the user can create programmatically or by first creating their query from within ESSENCE, and then clicking the “API URL” to generate the necessary URL. API results are generally available in both json and csv formats.ResultsEpidemiologists in the CDC NSSP have developed R code that uses these APIs to create customized Rmarkdown reports and visualizations not possible within the ESSENCE application, as well as to automate extraction of data from ESSENCE to support routine reporting for other CDC program areas (e.g., influenza-like illness, and suspected opioid encounters). Anecdotally, some Sites utilize the API to populate publically facing dashboards with aggregated data from ESSENCE. Programmatic access to processed ESSENCE data via the APIs also supports easily sharable exploratory analysis and visualization that can serve as a sandbox for testing new methods for future inclusion within ESSENCE.ConclusionsThe development and use of the ESSENCE APIs in public health surveillance will support more efficient and timely access to machine-readable data de-coupled from point and click user interfaces, and has the potential to spur new and innovative ways of using data that has traditionally been less programmatically accessible to users. New tools and programs can leverage the data in web or mobile applications, traditional reports, and more easily integrate disparate data sources for comprehensive surveillance.

scholarly journals Enhanced accuracy of rainfall–runoff modeling with wavelet transform

2012 ◽  
Vol 15 (2) ◽  
pp. 392-404 ◽  
Author(s):  
Chien-ming Chou
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Response Function ◽  
Time Series Data ◽  
Series Data ◽  
Rainfall Time Series ◽  
Calibration Data ◽  
Validation Data ◽  
Original Scale ◽  
Using Data

Wavelet transform (WT) is typically used to decompose time series data for only one hydrological feature at a time. This study applied WT for simultaneous decomposition of rainfall and runoff time series data. For the calibration data, the decomposed rainfall and runoff time series calibrate the subsystem response function using the least squares (LS) method at each scale. For the validation data, the decomposed rainfall time series are convoluted with the estimated subsystem response function to obtain the estimated runoff at each scale. The estimated runoff at the original scale can be obtained by wavelet reconstruction. The efficacy of the proposed method is evaluated in two case studies of the Feng-Hua Bridge and Wu-Tu watershed. The analytic results confirm that the proposed wavelet-based method slightly outperforms the conventional method of using data only at the original scale. The results also show that the runoff hydrograph estimated by using the proposed method is smoother than that obtained using a single scale.

scholarly journals Identifying and Exploiting Diurnal Motifs in Wind Generation Time Series Data

2015 ◽  
Vol 29 (02) ◽  
pp. 1550012 ◽  
Author(s):  
Ya Ju Fan ◽  
Chandrika Kamath
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Historical Data ◽  
Wind Farms ◽  
Series Data ◽  
Wind Generation ◽  
Control Room ◽  
Additional Information ◽  
Diurnal Patterns ◽  
Using Data

Wind energy is scheduled on the power grid using 0–6 h ahead forecasts generated from computer simulations or historical data. When the forecasts are inaccurate, control room operators use their expertise, as well as the actual generation from previous days, to estimate the amount of energy to schedule. However, this is a challenge, and it would be useful for the operators to have additional information they can exploit to make better informed decisions. In this paper, we use techniques from time series analysis to determine if there are motifs, or frequently occurring diurnal patterns in wind generation data. We compare two different representations of the data and four different ways of identifying the number of motifs. Using data from wind farms in Tehachapi Pass and mid-Columbia Basin, we describe our findings and discuss how these motifs can be used to guide scheduling decisions.

scholarly journals Peramalan Data Runtun Waktu menggunakan Model Hybrid Time Series Regression – Autoregressive Integrated Moving Average

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Melisa Arumsari ◽  
◽  
Andrea Dani ◽  
Keyword(s):  
Time Series ◽  
Hybrid Method ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Regression ◽  
Accurate Forecast ◽  
Using Data ◽  
The Individual ◽  
Time Series Data Analysis

Forecasting is a method used to estimate or predict a value in the future using data from the past. With the development of methods in time series data analysis, a hybrid method was developed in which a combination of several models was carried out in order to produce a more accurate forecast. The purpose of this study was to determine whether the TSR-ARIMA hybrid method has a better level of accuracy than the individual TSR method so that more accurate forecasting results are obtained. The data in this study are monthly data on the number of passengers on American airlines for the period January 1949 to December 1960. Based on the analysis, the TSR-ARIMA hybrid method produces a MAPE of 3,061% and the TSR method produces an MAPE of 7,902%.

scholarly journals Eliciting Disease Data from Wikipedia Articles

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Geoffrey Fairchild ◽  
Lalindra De Silva ◽  
Sara Y. Del Valle ◽  
Alberto M. Segre
Keyword(s):  
Time Series ◽  
Open Source ◽  
Disease Surveillance ◽  
Time Series Data ◽  
Ground Truth ◽  
Series Data ◽  
Disease Detection ◽  
Surveillance Systems ◽  
Ground Truth Data ◽  
Named Entity

Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. This study presents the use of Wikipedia article content in this sphere.  We demonstrate how a named-entity recognizer can be trained to tag case, death, and hospitalization counts in the article text. We also show that there are detailed time series data that are consistently updated that closely align with ground truth data.  We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.

scholarly journals A Fuzzy C-Means-Based Algorithm for the Surveillance of Dengue Cases Distribution in Local Communities

2021 ◽  
Author(s):  
Jozelle C. Addawe ◽  
Jaime D.L. Caro ◽  
Richelle Ann B. Juayong
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Local Communities ◽  
Health Impacts ◽  
Series Data ◽  
Targeted Intervention ◽  
Fuzzy C Means ◽  
Disease Occurrence ◽  
Using Data ◽  
Clustering Data

The analysis of disease occurrence over the smallest unit of a region is critical in designing data-driven and targeted intervention plans to reduce health impacts in the population and prevent spread of disease. This study aims to characterize groups of local communities that exhibit the same temporal patterns in dengue occurrence using the Fuzzy C-means (FCM) algorithm for clustering spatiotemporal data and investigate its performance in clustering data on dengue cases aggregated yearly, monthly and weekly. In particular, this study investigates similar patterns of Dengue cases in 129 barangays of Baguio City, Philippines recorded over a period of 9 years. Results have shown that the FCM has promising results in grouping together time series data of barangays when using data that is aggregated weekly.

scholarly journals Data Mining Techniques using Time Series Research

2019 ◽  
Vol 8 (2S11) ◽  
pp. 121-129
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Research Field ◽  
Series Data ◽  
Primary Role ◽  
Useful Knowledge ◽  
Time Series Data Mining ◽  
Using Data

As time-series data are eventually large the discovery of knowledge from these massive data seems to be a challenge issue. The similarity measure plays a primary role in time series data mining, which improves the accuracy of data mining task. Time series data mining is used to mine all useful knowledge from the profile of data. Obviously, we have a potential to perform these works, but it leads to a vague crisis. This paper involves a survey regarding time series technique and its related issues like challenges, preprocessing methods, pattern mining and rule discovery using data mining. Streaming of data is one of the difficult tasks that should be managed over time. Thus, this paper can provide a basic and prominent knowledge about time series in data mining research field.

scholarly journals MSMBuilder: Statistical Models for Biomolecular Dynamics

2016 ◽  
Author(s):  
Matthew P. Harrigan ◽  
Mohammad M. Sultan ◽  
Carlos X. Hernández ◽  
Brooke E. Husic ◽  
Peter Eastman ◽  
...  
Keyword(s):  
Time Series ◽  
Learning Community ◽  
Statistical Models ◽  
Time Series Data ◽  
Markov Models ◽  
Application Programming Interface ◽  
Careful Consideration ◽  
Series Data ◽  
Biomolecular Dynamics ◽  
State Models

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov State Models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models (HMMs) and time-structure based independent component analysis (tICA). MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python API (application programming interface). MSMBuilder is developed with careful consideration for compatibility with the broader machine-learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics but is just as applicable to other computational or experimental time-series measurements. http://msmbuilder.org

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