scholarly journals Functional time series analysis of age-specific fertility rates: visualizing the change in the age-pattern of fertility in India

2016 ◽  
Vol 4 (2) ◽  
pp. 79
Author(s):  
Mallika Deb ◽  
Tapan Kumar Chakrabarty
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Rural Women ◽  
Age Groups ◽  
Reproductive Age ◽  
Data Series ◽  
Fertility Rates ◽  
Registration System ◽  
Series Analysis ◽  
Functional Time Series

Functional Time Series Analysis (FTSA) is carried out in this article to uncover the temporal variations in the age pattern of fertility in India. Attempt is made to find whether there is any typical age pattern in the nation’s fertility across the reproductive age groups. If so, how do we characterize the role of changing age pattern of fertility across reproductive age groups in the nation’s fertility transition? We have used region-specific (rural-urban) and country level data series on Age-Specific Fertility Rates (ASFRs) available from Sample Registration System (SRS), India during 1971-2013. Findings of this study are very impressive. It is observed that the youngest age group of women in 15-19 years has contributed to the maximum decline in fertility with a substantially accelerated pace during the period of study. The major changes in fertility rates among Indian women dominated by the rural representation occur at the ages after 30. Further, the study also suggests that the future course of demographic transition in India from third phase to the fourth phase of replacement fertility would depend on the degree and pace of decline among the rural women aged below 30 years.

scholarly journals Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tanja Charles ◽  
Matthias Eckardt ◽  
Basel Karo ◽  
Walter Haas ◽  
Stefan Kröger
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Disease Onset ◽  
Age Groups ◽  
Age Group ◽  
Pulmonary Tb ◽  
Series Analysis ◽  
Disease Site ◽  
Extra Pulmonary

Abstract Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.

scholarly journals The Impact of an Inactivated Hepatitis A Vaccine with One Dose in Brazil: A Retrospective Time-Series

Vaccines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 407
Author(s):  
Ana Luiza Bierrenbach ◽  
Yoonyoung Choi ◽  
Paula de Mendonça Batista ◽  
Fernando Brandão Serra ◽  
Cintia Irene Parellada ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Hepatitis A ◽  
National Level ◽  
Age Groups ◽  
High Rate ◽  
Series Analysis ◽  
Annual Reduction ◽  
The Impact ◽  
Vaccination Period

Background: In 2014, a recommended one-dose of inactivated hepatitis A vaccine was included in the Brazilian National Immunization Program targeting children 12–24 months. This decision addressed the low to intermediate endemicity status of hepatitis A across Brazil and the high rate of infection in children and adolescents between 5 and 19 years old. The aim of the study was to conduct a time-series analysis on hepatitis A incidence across age groups and to assess the hepatitis A distribution throughout Brazilian geographic regions. Methods: An interrupted time-series analysis was performed to assess hepatitis A incidence rates before (2010–2013) and after (2015–2018) hepatitis A vaccine program implementation. The time-series analysis was stratified by age groups while a secondary analysis examined geographic distribution of hepatitis A cases. Results: Overall incidence of hepatitis A decreased from 3.19/100.000 in the pre-vaccine period to 0.87/100.000 (p = 0.022) post-vaccine introduction. Incidence rate reduction was higher among children aged 1-4 years old, with an annual reduction of 67.6% in the post-vaccination period against a 7.7% annual reduction in the pre-vaccination period (p < 0.001). Between 2015 and 2018, the vaccination program prevented 14,468 hepatitis A cases. Conclusion: Our study highlighted the positive impact of a recommended one-dose inactivated hepatitis A vaccine for 1–4-years-old in controlling hepatitis A at national level.

Criteria for Recognition of Wheel Skid of Trains Based on Time Series Analysis

2013 ◽  
Vol 440 ◽  
pp. 237-242
Author(s):  
Jun Bin Peng ◽  
Xiao Yi Hu ◽  
Yong Jun Liu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Characteristic Equation ◽  
Critical Value ◽  
Time Series Model ◽  
Data Series ◽  
Velocity Difference ◽  
Series Analysis ◽  
Greens Function ◽  
Order Determination

Current criteria to judge wheel skid of trains such as velocity difference often cannot recognize wheel skid timely and have no uniform critical value for different trains or railway lines. Aiming at the disadvantages, new criteria based on time series analysis are proposed. With appropriate method of order determination and parameter estimation, AR time series model is established for the data series of velocity difference. Then, Greens function and characteristic equation are constructed with the parameters of the model to determine wheel skid by the convergence state of Greens function or the value of characteristic equations roots. Simulation result shows that the two criteria based on time series model can recognize wheel skid earlier than velocity difference. Moreover, the roots of characteristic equation can also be used as a criterion with a uniform critical value under different application conditions.

NONLINEAR TIME-SERIES ANALYSIS OF THE GREEK EXCHANGE-RATE MARKET

2000 ◽  
Vol 10 (07) ◽  
pp. 1729-1758 ◽  
Author(s):  
A. S. ANDREOU ◽  
G. PAVLIDES ◽  
A. KARYTINOS
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Degrees Of Freedom ◽  
Nonlinear Dynamical Systems ◽  
Deterministic Chaos ◽  
Data Series ◽  
Nonlinear Dynamical ◽  
Series Analysis ◽  
Low Dimensional ◽  
Bds Test

Using concepts from the theory of chaos and nonlinear dynamical systems, a time-series analysis is performed on four major currencies against the Greek Drachma. The R/S analysis provided evidence for fractality due to noisy chaos in only two of the data series, while the BDS test showed that all four systems exhibit nonlinearity. Correlation dimension and related tests, as well as Lyapunov exponents, gave consistent results, which did not rule out the possibility of deterministic chaos for the two possibly fractal series, rejecting though the occurrence of a simple low-dimensional attractor, while the other two series seemed to have followed a behavior close to that of a random signal. SVD analysis, used to filter away noise, strongly supported the above findings and provided reliable evidence for the existence of an underlying system with a limited number of degrees-of-freedom only for those series found to exhibit fractality, while it revealed a noise domination over the remaining two. These results were further confirmed through a forecasting attempt using artificial neural networks.

scholarly journals Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bin Xu ◽  
Jiayuan Li ◽  
Mengqiao Wang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Goodness Of Fit ◽  
Intervention Strategy ◽  
Age Groups ◽  
Arima Models ◽  
Optimal Model ◽  
Series Analysis ◽  
Infectious Disease Reporting ◽  
Significant Difference

Abstract Background To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends. Method Using official surveillance data from publicly accessible database of the national infectious disease reporting system, we described long-term patterns of incidence and death in AIDS/HIV, analyzed age group and regional epidemic characteristics, and established Autoregressive Integrated Moving Average (ARIMA) models for time series analysis. Result The incidence and death of AIDS/HIV have increased rapidly from 2004 to 2017, with significant difference regarding age groups and provincial regions (a few provinces appear as hot spots). With goodness-of-fit criteria and using data from 2004 to 2015, ARIMA (0,1,3) × (2,0,0), ARIMA (3,1,0) × (1,0,1), and ARIMA (0,1,2) × (2,0,0) were chosen as the optimal model for the incidence of AIDS, HIV, and combined; ARIMA (0,1,3) × (1,0,0) was chosen as the optimal model for the death of AIDS, HIV, and combined. ARIMA models robustly predicted the incidence and death of AIDS/HIV in 2016 and 2017. Conclusion A focused intervention strategy targeting specific regions and age groups is essential for the prevention and control of AIDS/HIV. ARIMA models function as data-driven and evidence-based methods to forecast the trends of infectious diseases and formulate public health policies.

Time-Series Analysis of Behavioural Data: An Update

1987 ◽  
Vol 4 (4) ◽  
pp. 40-45 ◽  
Author(s):  
Christopher F. Sharpley
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Model Identification ◽  
Interrupted Time Series ◽  
Data Series ◽  
Interrupted Time Series Analysis ◽  
Behaviour Analysis ◽  
Series Analysis ◽  
Applied Behaviour Analysis ◽  
Recent Developments

Some recent developments in the use of interrupted time-series analysis (ITSA) are described with particular reference to the detection of effects with short data series such as those often encountered in applied behaviour analysis. The necessity to perform the sometimes troublesome model-identification procedure is questioned, and the likely incidence of Type 1 and 2 errors is discussed. Conclusions are drawn to suggest that ITSA may be safely applied to data that are typical of those collected in applied behaviour analysis.

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