Reduction in hospitalisations for acute gastroenteritis-associated childhood seizures since introduction of rotavirus vaccination: a time-series and change-point analysis of hospital admissions in England

IntroductionThe incidence of severe childhood diarrhoea has fallen substantially following the introduction of rotavirus vaccine in the UK in July 2013. Since children with rotavirus infection may experience febrile and afebrile seizures, we evaluated the impact of rotavirus vaccination on seizure hospitalisations in children in England.MethodsUsing data from Hospital Episode Statistics, we employed interrupted time-series analyses to assess changes in monthly hospital admissions for seizures among children aged <5 years from July 2000 to June 2017. Outcome measures comprised all seizures and febrile seizures, with and without a co-diagnosis of acute gastroenteritis (AGE). Models were adjusted for pneumococcal conjugate vaccine (PCV) introduction. Change-point analysis was used to independently identify step-changes in the time-series.ResultsAmong hospitalised children aged <5 years, the incidence of any seizures and febrile seizures with AGE decreased post-vaccine introduction by 23% (95% CI: 11% to 33%) and 31% (95% CI: 19% to 41%), respectively. For febrile seizures with AGE, a single change-point was identified in July 2013 (95% CI: June 2013 to December 2013). Reductions in seizure incidence were higher during the rotavirus season (49%, 95% CI: 37% to 58%) compared with out-of-season (13%, 95% CI: −4 to 28%) and showed no relation to PCV introduction. There were small reductions in any seizures with any co-diagnosis (4%, 95% CI: 0% to 8%) and in febrile seizures with any co-diagnosis (10%, 95% CI: 2% to 16%).ConclusionRotavirus vaccination has reduced hospitalisations for seizures associated with AGE in England, providing additional evidence of population-level impact of rotavirus vaccination on seizure incidence in high-income countries.

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Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

Oceanological and Hydrobiological Studies ◽

10.1515/ohs-2020-0021 ◽

2020 ◽

Vol 49 (3) ◽

pp. 230-246 ◽

Cited By ~ 1

Author(s):

Gökhan Arslan ◽

Semih Kale ◽

Adem Yavuz Sönmez

Keyword(s):

Time Series ◽

Trend Analysis ◽

Change Point ◽

Moving Average ◽

Arima Model ◽

Change Point Analysis ◽

Point Analysis ◽

Monthly Streamflow ◽

Model Time Series ◽

Streamflow Trend

AbstractThe objective of this paper is to determine the trend and to estimate the streamflow of the Gökırmak River. The possible trend of the streamflow was forecasted using an autoregressive integrated moving average (ARIMA) model. Time series and trend analyses were performed using monthly streamflow data for the period between 1999 and 2014. Pettitt’s change point analysis was employed to detect the time of change for historical streamflow time series. Kendall’s tau and Spearman’s rho tests were also conducted. The results of the change point analysis determined the change point as 2008. The time series analysis showed that the streamflow of the river had a decreasing trend from the past to the present. Results of the trend analysis forecasted a decreasing trend for the streamflow in the future. The decreasing trend in the streamflow may be related to climate change. This paper provides preliminary knowledge of the streamflow trend for the Gökırmak River.

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A CHANGE POINT ANALYSIS OF THE IMPACT OF “ENVIRONMENTAL FEDERALISM” ON AGGREGATE AIR QUALITY IN THE UNITED STATES: 1940-98

Economic Inquiry ◽

10.1093/ei/cbj006 ◽

2006 ◽

Vol 44 (1) ◽

pp. 109-120 ◽

Cited By ~ 14

Author(s):

THOMAS B. FOMBY ◽

LIMIN LIN

Keyword(s):

United States ◽

Air Quality ◽

Change Point ◽

The United States ◽

Change Point Analysis ◽

Environmental Federalism ◽

Point Analysis ◽

The Impact

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Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment (Preprint)

10.2196/preprints.11357 ◽

2018 ◽

Author(s):

Amir Talaei-Khoei ◽

James M Wilson ◽

Seyed-Farzan Kazemi

Keyword(s):

Time Series ◽

Change Point ◽

Influenza A ◽

Time Series Prediction ◽

Viral Meningitis ◽

Change Point Analysis ◽

Northern Nevada ◽

Point Analysis ◽

Syncytial Virus ◽

And Performance

BACKGROUND The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. OBJECTIVE This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. METHODS The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. RESULTS Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. CONCLUSIONS The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.

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Change-point analysis ofVP/VSratio time-series using a trans-dimensional McMC algorithm: applied to the Alto Tiberina Near Fault Observatory seismic network (Northern Apennines, Italy)

Geophysical Journal International ◽

10.1093/gji/ggz078 ◽

2019 ◽

Vol 217 (2) ◽

pp. 1217-1231 ◽

Cited By ~ 2

Author(s):

Giulio Poggiali ◽

Lauro Chiaraluce ◽

Raffaele Di Stefano ◽

Nicola Piana Agostinetti

Keyword(s):

Time Series ◽

Change Point ◽

Northern Apennines ◽

Seismic Network ◽

Change Point Analysis ◽

Mcmc Algorithm ◽

Near Fault ◽

Point Analysis

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Bayesian change-point analysis in hydrometeorological time series. Part 1. The normal model revisited

Journal of Hydrology ◽

10.1016/s0022-1694(00)00270-5 ◽

2000 ◽

Vol 235 (3-4) ◽

pp. 221-241 ◽

Cited By ~ 111

Author(s):

L. Perreault ◽

J. Bernier ◽

B. Bobée ◽

E. Parent

Keyword(s):

Time Series ◽

Change Point ◽

Change Point Analysis ◽

Normal Model ◽

Point Analysis

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Change point analysis of imprecise time series

Fuzzy Sets and Systems ◽

10.1016/j.fss.2013.03.001 ◽

2013 ◽

Vol 225 ◽

pp. 23-38 ◽

Cited By ~ 12

Author(s):

Carmela Cappelli ◽

Pierpaolo D’Urso ◽

Francesca Di Iorio

Keyword(s):

Time Series ◽

Change Point ◽

Change Point Analysis ◽

Point Analysis

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Spatio-Temporal Impact of Self-Financed Rotavirus Vaccination on Rotavirus and Acute Gastroenteritis Hospitalisations in the Valencia Region, Spain.

10.21203/rs.3.rs-15736/v3 ◽

2020 ◽

Author(s):

Mónica López-Lacort ◽

Alejandro Orrico-Sánchez ◽

Miguel Ángel Martínez-Beneito ◽

Cintia Muñoz-Quiles ◽

Javier Díez-Domingo

Keyword(s):

Acute Gastroenteritis ◽

Hospital Admissions ◽

Vaccine Coverage ◽

Population Based ◽

Space Time ◽

Real World Data ◽

Substantial Impact ◽

Rotavirus Vaccination ◽

Spatio Temporal ◽

The Impact

Abstract Background: Several studies have shown a substantial impact of Rotavirus (RV) vaccination on the burden of RV and all-cause acute gastroenteritis (AGE). However, the results of most impact studies could be confused by a dynamic and complex space-time process. Therefore, there is a need to analyse the impact of RV vaccination on RV and AGE hospitalisations in a space-time framework to detect geographical-time patterns while avoiding the potential confusion caused by population inequalities in the impact estimations.Methods: A retrospective population-based study using real-world data from the Valencia Region was performed among children aged less than 3 years old in the period 2005-2016. A Bayesian spatio-temporal model was constructed to analyse RV and AGE hospitalisations and to estimate the vaccination impact measured in averted hospitalisations. Results: We found important spatio-temporal patterns in RV and AGE hospitalisations, RV vaccination coverage and in their associated adverted hospitalisations. Overall, ~1866 hospital admissions for RV were averted by RV vaccination during 2007–2016. Despite the low-medium vaccine coverage (~50%) in 2015-2016, relevant 36% and 20% reductions were estimated in RV and AGE hospitalisations respectively.Conclusions: The introduction of the RV vaccines has substantially reduced the number of RV hospitalisations, averting ~1866 admissions during 2007-2016 which were space and time dependent. This study improves the methodologies commonly used to estimate the RV vaccine impact and their interpretation.

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Change-Point Analysis of Tropical Night Occurrences for Five Major Cities in Republic of Korea

Advances in Meteorology ◽

10.1155/2015/801981 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Myoung-Seok Suh ◽

Chansoo Kim

Keyword(s):

Time Series ◽

Change Point ◽

Time Series Data ◽

Republic Of Korea ◽

Change Point Analysis ◽

Series Data ◽

Daily Minimum Temperature ◽

Rapid Urbanization ◽

Tropical Night ◽

Point Analysis

Bayesian change-point analysis is applied to detect a change-point in the occurrences of tropical night (TN) days in the 50-year time series data for five major cities in Republic of Korea. A TN day is simply defined as a day when the daily minimum temperature is greater than 25∘C. A Bayesian analysis is performed for detecting a change-point at an unknown time point in the TN day frequency time series, which is modeled by an independent Poisson random variable. The results showed that a single change occurred around 1993 for three cities (Seoul, Incheon, and Daegu). However, when we excluded the extraordinary year, 1994, a single change occurred around 1993 only in Seoul and Daegu. The average number of TN days in Seoul and Daegu increased significantly, by more than 150%, after the change-point year. The abrupt increase in TN day frequency in two cities over Republic of Korea around 1993 may be related to the significant decadal change in the East Asian summer monsoon around the mid 1990s and to rapid urbanization.

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Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Information ◽

10.3390/info12070274 ◽

2021 ◽

Vol 12 (7) ◽

pp. 274

Author(s):

Ourania Theodosiadou ◽

Kyriaki Pantelidou ◽

Nikolaos Bastas ◽

Despoina Chatzakou ◽

Theodora Tsikrika ◽

...

Keyword(s):

Time Series ◽

Change Point ◽

Hate Speech ◽

Change Point Detection ◽

Change Points ◽

Change Point Analysis ◽

Point Analysis ◽

Depth Analysis ◽

Textual Data ◽

Point Detection

Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness.

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