On mixed PARMA modeling of epidemiological time series data

Kalligeris, Emmanouil-Nektarios; Karagrigoriou, Alex; Parpoula, Christina

doi:10.1080/23737484.2019.1644253

Cited by 7 publications

(8 citation statements)

References 26 publications

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“… X false( t false) = α 0 + α 1 t + γ 1 cos 0.2em false( 2 π t m false) + δ 1 sin 0.2em false( 2 π t m false) + ϕ 1 x t − 1 + ϕ 2 x t − 2 + ϵ t + λ 1 ϵ t − 1 + ω 1 mintemp; MXM11 represents a model incorporating Auto-Regressive Moving Average (ARMA) terms and random meteorological covariates in the model structure as described by Kalligeris et al 30 ;…”

Section: Empirical Studymentioning

confidence: 99%

A distribution-free control charting technique based on change-point analysis for detection of epidemics

Parpoula

2022

Stat Methods Med Res

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Worldwide, the detection of epidemics has been recognized as a continuing problem of crucial importance to public health surveillance. Various approaches for detecting and quantifying epidemics of infectious diseases in the recent literature are directly influenced by methods of Statistical Process Control (SPC). However, implementing SPC quality tools directly to the general health care monitoring problem, in a similar manner as in industrial quality control, is not feasible since many assumptions such as stationarity, known asymptotic distribution etc. are not met. Toward this end, in this paper, some of the open statistical research issues involved in this field are discussed, and a distribution-free control charting technique based on change-point analysis is applied and evaluated for detection of epidemics. The main tool in this methodology is the detection of unusual trends, in the sense that the beginning of an unusual trend marks a switch from a control state to an epidemic state. The in-control and out-of-control performance of the adapted control scheme from SPC is thoroughly investigated using Monte Carlo simulations, and the applied scheme is found to outperform its parametric and nonparametric competitors in many cases. Moreover, the empirical comparative study provides evidence that the adapted change-point detection scheme has several appealing properties compared to the current practice for detection of epidemics.

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Section: Empirical Studymentioning

confidence: 99%

A distribution-free control charting technique based on change-point analysis for detection of epidemics

Parpoula

2022

Stat Methods Med Res

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“…Then, the algorithm of Kalligeris et al. 13 was executed, with respect to Akaike information criterion (AIC) and analysis of variance (ANOVA) comparisons, for several models with trend, periodicity, AR, and MA terms as well as the average minimum weekly temperature, which was the only covariate identified as significant among a number of possibly significant meteorological variables considered. The aforementioned periodic-type auto-regressive moving average (ARMA) models with covariates are denoted by M ij , were i = 1,2,3,4 corresponds to linear (1), quadratic (2), cubic (3), and quartic (4) trend, respectively and j = 1,2,3 corresponds to annual (1), 6-month (2), and 3-month (3) periodicity, respectively.…”

Section: Changepoint Detection Periodic Arma Modeling and Estimatmentioning

confidence: 99%

“…Kalligeris et al. 13 extended the methodology based on Serfling-type modeling as discussed in literature, 14,15 via incorporating auto-regressive moving average (ARMA) terms and meteorological random covariates into the model structure. The methodology based on Serfling-type and/or ARMA modeling although satisfactory for the modeling of the baseline part of the series, does not take into consideration the behavior of the extreme part of the series, resulting in an unsatisfactory model for prediction purposes.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the methodology described in Kalligeris et al. 13 for analyzing influenza-like illness (ILI) rate data for Greece is generalized through implementing changepoint methodology in conjunction with meteorological covariates and periodic-type ARMA modeling for developing more accurate and efficient estimations. In this way, this work succeeds in having satisfactory results by periodic-type ARMA modeling with covariates for both the typical as well as the outbreak time-series data.…”

Section: Introductionmentioning

confidence: 99%

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Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

Kalligeris

Karagrigoriou

Parpoula

2019

Stat Methods Med Res

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When it comes to incidence data, most of the work on this field focuses on the modeling of nonextreme periods. Several attempts have been made and a variety of techniques are available to achieve so. In this work, in order to model not only the nonextreme periods but also capture the behavior of the whole time-series, we make use of a dataset on influenza-like illness rate for Greece, for the period 2014–2016. The identification of extreme periods is made possible via changepoint detection analysis and model selection techniques are developed in order to identify the optimal periodic-type auto-regressive moving average model with covariates that best describes the pattern of the time-series. In addition, in the context of incidence data modeling, an advanced algorithm was developed in order to improve the accuracy of the selected model. The derived results are satisfactory since the changepoint method seems to identify correctly the extreme periods, and the selected model: (1) estimates accurately the influenza-like illness syndrome morbidity burden in the case of Greece, and (2) captures satisfactorily enough the behavior of the whole time-series.

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“…In such studies, multivariate techniques for time series analysis and health indicators play a key role. For instance, efficiency and productivity of healthcare systems have been claimed to have a major impact on healthcare costs and have been a topic of great research activity (see, e.g., in [1,2]).…”

Section: Introductionmentioning

confidence: 99%

Causality Distance Measures for Multivariate Time Series with Applications

et al. 2021

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In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.

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On mixed PARMA modeling of epidemiological time series data

Cited by 7 publications

References 26 publications

A distribution-free control charting technique based on change-point analysis for detection of epidemics

A distribution-free control charting technique based on change-point analysis for detection of epidemics

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

Causality Distance Measures for Multivariate Time Series with Applications

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