scholarly journals Seasonality of Influenza‐Like‐Illness and Acute Cardiovascular Events Are Related Regardless of Vaccine Effectiveness

2020 ◽  
Vol 9 (20) ◽  
Author(s):  
Erin R. Kulick ◽  
Michelle Canning ◽  
Neal S. Parikh ◽  
Mitchell S. V. Elkind ◽  
Amelia K. Boehme
Keyword(s):  
New York ◽  
Time Series ◽  
Influenza Vaccination ◽  
Correlation Functions ◽  
Cardiovascular Events ◽  
Cross Correlation ◽  
Moving Average ◽  
International Classification Of Diseases ◽  
Time Series Regression ◽  
Influenza Like Illness

Background Influenza has been identified as a trigger for stroke and myocardial infarction (MI) with prior studies demonstrating that influenza vaccination may decrease risk of stroke and MI. Methods and Results We used data from the New York Department of Health Statewide Planning and Research Cooperative System to evaluate whether annual variability in influenza vaccination effectiveness (VE) would be associated with cardiovascular events. Daily and monthly counts of outpatient and inpatient visits for influenza‐like illness (ILI), stroke, and MI were identified using International Classification of Diseases, Ninth Revision ( ICD‐9 ) codes; VE data for each year are publicly available. We identified pertinent lags between ILI, stroke, and MI using prewhitening cross‐correlation functions and applied them to autoregressive integrated moving average time series regression models. Time series forecasting systems assessed correlations among ILI, stroke, and MI, and the effect of VE on these relationships. Cross‐correlation functions indicated stroke events increased 1 month after increases in ILI rates; MIs increased immediately. Accounting for seasonality and lag, peaks in ILI rates were significantly related to peaks in stroke ( P =0.04) and MI ( P =0.01). Time forecasting analyses indicated no relationship between VE and cardiovascular events. Conclusions We identified that seasonality of cardiovascular events may be associated with seasonality in ILI, though VE did not modify this relationship.

scholarly journals A Time-Series Analysis of Clostridium difficile and Its Seasonal Association with Influenza

2010 ◽  
Vol 31 (4) ◽  
pp. 382-387 ◽  
Author(s):  
Philip M. Polgreen ◽  
Ming Yang ◽  
Lucas C. Bohnett ◽  
Joseph E. Cavanaugh
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Linear Trend ◽  
International Classification Of Diseases ◽  
Series Data ◽  
Series Analysis ◽  
Classification Of Diseases

Objective.To characterize the temporal progression of the monthly incidence of Clostridium difficile infections (CDIs) and to determine whether the incidence of CDI is related to the incidence of seasonal influenza.Design.A retrospective study of patients in the Nationwide Inpatient Sample during the period from 1998 through 2005.Methods.We identified all hospitalizations with a primary or secondary diagnosis of CDI with use of International Classification of Diseases, 9th Revision, Clinical Modification codes, and we did the same for influenza. The incidence of CDI was modeled as an autoregression about a linear trend. To investigate the association of CDI with influenza, we compared national and regional CDI and influenza series data and calculated cross-correlation functions with data that had been prewhitened (filtered to remove temporal patterns common to both series). To estimate the burden of seasonal CDI, we developed a proportional measure of seasonal CDI.Results.Time-series analysis of the monthly number of CDI cases reveals a distinct positive linear trend and a clear pattern of seasonal variation (R2 = 0.98). The cross-correlation functions indicate that influenza activity precedes CDI activity on both a national and regional basis. The average burden of seasonal (ie, winter) CDI is 23%.Conclusions.The epidemiologic characteristics of CDI follow a pattern that is seasonal and associated with influenza, which is likely due to antimicrobial use during influenza seasons. Approximately 23% of average monthly CDI during the peak 3 winter months could be eliminated if CDI remained at summer levels.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

scholarly journals Fluctuations in influenza-like illness epidemics and suicide mortality: A time-series regression of 13-year mortality data in South Korea

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244596
Author(s):  
Sun Jae Jung ◽  
Sung-Shil Lim ◽  
Jin-Ha Yoon
Keyword(s):  
Time Series ◽  
South Korea ◽  
Influenza A ◽  
Suicide Mortality ◽  
Mortality Data ◽  
Time Series Regression ◽  
Influenza Like Illness ◽  
Pdm09 Virus ◽  
Influenza A H1n1 ◽  
Novel Influenza A

Aims We explored the association between influenza epidemic and suicide mortality rates in a large population using a time-series regression of 13-year mortality data in South Korea. Methods Weekly suicide mortalities and influenza-like illness (ILI) were analyzed using time series regression. Regression coefficient for suicide mortality based on percentage change of ILI was calculated using a quasi-Poisson regression. Non-linear distributed lag models with quadratic function up to 24 weeks were constructed. Results The association between ILI and suicide mortality increased significantly up to 8 weeks post-influenza diagnosis. A significant positive association between ILI and suicide mortality was observed from 2009, when a novel influenza A(H1N1)pdm09 virus provoked a worldwide pandemic. No meaningful association between these factors was observed before 2009. Conclusion There was a significant positive relationship between ILI and suicide mortality after 2009, when a novel influenza A(H1N1)pdm09 virus provoked a worldwide pandemic.

scholarly journals Frequency-phase analysis of resting-state functional MRI

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Gadi Goelman ◽  
Rotem Dan ◽  
Filip Růžička ◽  
Ondrej Bezdicek ◽  
Evžen Růžička ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Mri ◽  
Correlation Functions ◽  
Resting State ◽  
Cross Correlation ◽  
Time Lag ◽  
Unified Framework ◽  
Functional Connections ◽  
Original Time ◽  
Phase Differences

Abstract We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience.

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 Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

2021 ◽  
Vol 4 (1) ◽  
pp. 57
Author(s):  
Tito Tatag Prakoso ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Series Data ◽  
Gregorian Calendar ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Past Data

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous  (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

Some Aspects of Asymptotic Theory with Applications to Time Series Models

1995 ◽  
Vol 11 (5) ◽  
pp. 818-887 ◽  
Author(s):  
P. Jeganathan
Keyword(s):  
New York ◽  
Time Series ◽  
Linear Time ◽  
Adaptive Estimation ◽  
Nonlinear Models ◽  
Unit Roots ◽  
Recent Book ◽  
Time Series Models ◽  
Likelihood Ratios ◽  
Time Series Regression

The primary purpose of this paper is to review a very few results on some basic elements of large sample theory in a restricted structural framework, as described in detail in the recent book by LeCam and Yang (1990, Asymptotics in Statistics: Some Basic Concepts. New York: Springer), and to illustrate how the asymptotic inference problems associated with a wide variety of time series regression models fit into such a structural framework. The models illustrated include many linear time series models, including cointegrated models and autoregressive models with unit roots that are of wide current interest. The general treatment also includes nonlinear models, including what have become known as ARCH models. The possibility of replacing the density of the error variables of such models by an estimate of it (adaptive estimation) based on the observations is also considered.Under the framework in which the asymptotic problems are treated, only the approximating structure of the likelihood ratios of the observations, together with auxiliary estimates of the parameters, will be required. Such approximating structures are available under quite general assumptions, such as that the Fisher information of the common density of the error variables is finite and nonsingular, and the more specific assumptions, such as Gaussianity, are not required. In addition, the construction and the form of inference procedures will not involve any additional complications in the non-Gaussian situations because the approximating quadratic structure actually will reduce the problems to the situations similar to those involved in the Gaussian cases.

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