The use of temporally aggregated data in modeling and testing a variance change in a time series

2021 ◽  
pp. 1-18
Author(s):  
Bu Hyoung Lee ◽  
William W. S. Wei
Keyword(s):  
Time Series ◽  
Aggregated Data ◽  
Variance Change

On a model for a time series of cross-sections

1986 ◽  
Vol 23 (A) ◽  
pp. 113-125 ◽  
Author(s):  
P. M. Robinson
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Sections ◽  
Standard Form ◽  
Cross Sectional ◽  
Aggregated Data ◽  
Section Data ◽  
Infinite Population ◽  
The Cross ◽  
Observation Times

Dynamic stationary models for mixed time series and cross-section data are studied. The models are of simple, standard form except that the unknown coefficients are not assumed constant over the cross-section; instead, each cross-sectional unit draws a parameter set from an infinite population. The models are framed in continuous time, which facilitates the handling of irregularly-spaced series, and observation times that vary over the cross-section, and covers also standard cases in which observations at the same regularly-spaced times are available for each unit. A variety of issues are considered, in particular stationarity and distributional questions, inference about the parameter distributions, and the behaviour of cross-sectionally aggregated data.

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 Number of hospitalizations for stroke before and during the new Coronavirus pandemic in Salvador-BA

2021 ◽  
Author(s):  
Juliana Pugas Paim Lima ◽  
Ian Vítor Moura Giudice de Argôllo ◽  
Caroline Silva Calixto Dourado ◽  
Rodrigo Libertador Dourado Campos Werneck
Keyword(s):  
Time Series ◽  
Information System ◽  
Social Isolation ◽  
Neurological Deficit ◽  
Hospital Information System ◽  
Direct Relationship ◽  
Medical Attention ◽  
Significant Drop ◽  
Aggregated Data ◽  
Prevention And Treatment

Introduction: Salvador was deeply affected by Sars-CoV-2. Because of the fear of contamination, many stopped seeking medical attention for the prevention and treatment of serious illnesses, such as stroke. This is defined as the appearance of a sudden neurological deficit caused by an alteration in the blood vessels of the CNS, being classified as ischemic and hemorrhagic. Objectives: To describe a relationship between the number of strokes hospitalizations from January / 2019 to March / 2020, which preceded the new Coronavirus pandemic, and the period of social isolation, from March / 2020 to October / 2020. Methods: The study was done with aggregated data, being longitudinal observational (time series) based on data collected in the SUS Hospital Information System (SIH / SUS) available at DATASUS. Results: A peak of hospitalizations for stroke is observed in April / 2019, with 350 cases. During the same year, hospitalizations fluctuated between 200 and 300 cases. At the beginning of 2020, this number remained on schedule, with an average of 250 hospitalizations. As of April, however, there is a fall, with May / 2020 reaching the lowest number of hospitalizations recorded in the entire period studied. This deviation is also observed in the remaining months of 2020. Conclusion: The pandemic may have a direct relationship with the number of hospitalizations in Salvador. Due to fear of contamination, there was a significant drop in the number of consultations, increasing the likelihood of deaths from stroke.

On a model for a time series of cross-sections

1986 ◽  
Vol 23 (A) ◽  
pp. 113-125
Author(s):  
P. M. Robinson
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Sections ◽  
Standard Form ◽  
Cross Sectional ◽  
Aggregated Data ◽  
Section Data ◽  
Infinite Population ◽  
The Cross ◽  
Observation Times

Dynamic stationary models for mixed time series and cross-section data are studied. The models are of simple, standard form except that the unknown coefficients are not assumed constant over the cross-section; instead, each cross-sectional unit draws a parameter set from an infinite population. The models are framed in continuous time, which facilitates the handling of irregularly-spaced series, and observation times that vary over the cross-section, and covers also standard cases in which observations at the same regularly-spaced times are available for each unit. A variety of issues are considered, in particular stationarity and distributional questions, inference about the parameter distributions, and the behaviour of cross-sectionally aggregated data.

scholarly journals Trend of pneumonia diagnosis in emergency departments as a COVID-19 surveillance system: a time series study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044388
Author(s):  
Rossella Murtas ◽  
Adriano Decarli ◽  
Antonio Giampiero Russo
Keyword(s):  
Time Series ◽  
Emergency Departments ◽  
Low Cost ◽  
Warning System ◽  
Aggregated Data ◽  
Lombardy Region ◽  
System A ◽  
Cost Strategy ◽  
Low Cost Strategy ◽  
Influenza Outbreaks

ObjectiveIn Italy, the first diagnosis of COVID-19 was confirmed on 20 February 2020 in the Lombardy region. Given the rapid spread of the infection in the population, it was suggested that in Europe, and specifically in Italy, the virus had already been present in the last months of 2019. In this paper, we aim to evaluate the hypothesis on the early presence of the virus in Italy by analysing data on trends of access to emergency departments (EDs) of subjects with a diagnosis of pneumonia during the 2015–2020 period.DesignTime series cohort study.SettingWe collected data on visits due to pneumonia between 1 October 2015 and 31 May 2020 in all EDs of the Agency for Health Protection of Milan (ATS of Milan). Trend in the winter of 2019–2020 was compared with those in the previous 4 years in order to identify unexpected signals potentially associated with the occurrence of the pandemic. Aggregated data were analysed using a Poisson regression model adjusted for seasonality and influenza outbreaks.Primary outcome measures Daily pneumonia-related visits in EDs.Results In the studied period, we observed 105 651 pneumonia-related ED visits. Compared with the expected, a lower occurrence was observed in January 2020, while an excess of pneumonia visits started in the province of Lodi on 21 February 2020, and almost 10 days later was observed in the remaining territory of the ATS of Milan. Overall, the peak in excess was found on 17 March 2020 (369 excess visits compared with previous years, 95% CI 353 to 383) and ended in May 2020, the administrative end of the Italian lockdown.Conclusions An early warning system based on routinely collected administrative data could be a feasible and low-cost strategy to monitor the actual situation of the virus spread both at local and national levels.

scholarly journals Accelerating progress on effective tobacco tax policies in Montenegro

2020 ◽  
Vol 29 (Suppl 5) ◽  
pp. s293-s299
Author(s):  
Ana Mugosa ◽  
Mirjana Cizmovic ◽  
Tanja Lakovic ◽  
Milenko Popovic
Keyword(s):  
Time Series ◽  
Price Elasticity ◽  
Low Income ◽  
Direct Impact ◽  
Demand Model ◽  
Middle Income ◽  
Aggregated Data ◽  
Distributed Lag ◽  
Tobacco Tax ◽  
Short Run

BackgroundThe objective of this study is to estimate the sensitivity of cigarette quantity demanded to price and income changes in Montenegro.Data and methodsThe sensitivity of cigarette quantity demanded to price and income changes was estimated using micro and macro analysis. Micro analysis implied the use of Deaton’s model on Household Budget Survey data (2006–2017). In macro analysis, conventional static demand model is applied using error correction and autoregressive distributed lag time series methodology on annual time series aggregated data (2001–2017).ResultsThe same results were obtained using micro and macro analysis which contributes to the objectivity of the conducted research. Results derived from the Deaton’s model indicate a negative price elasticity of cigarettes in the range between −0.62 and −0.80 (conditional and unconditional), while in macro model estimated price elasticity is in that range and equals −0.68. Simulation results confirm the efficiency of excise tax policy changes, having an evident decrease in consumption and increase of public revenues.ConclusionAnalysis of the tobacco market and regulatory environment suggests that the increase of excise and other taxes on tobacco have an important direct impact on the reduction of cigarettes and other tobacco products consumption. Our estimates of long and short-run price elasticity show that direct impact is strong and very much in accordance with the results obtained so far for other low-income and middle-income countries. This paper gives a contribution to the analysis of price elasticity of demand for cigarettes, which was for the first time conducted in Montenegro.

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