An analytical approach for identifying trend-seasonal components and detecting unexpected behaviour in psychological time-series.

The recent advances in technological capabilities have led to a massive production of time-series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time-series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time-series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively.

Towards the new primary care reform in Greece: a focus on patients' expectations, views and perceptions from rural healthcare centres.

Introduction: Patient perception of quality of care is an essential component in evaluating healthcare delivery. This article reports data from primary health care (PHC) centers before Greece's most recent PHC reform. The study was undertaken to offer some baseline information about patient experience, support the decision-making processes taking place, and provide valuable input for future policy-making comparisons in Greece.

Individual Differences on Wellbeing Indices during the COVID-19 Quarantine in Greece: A National Study.

The impact of COVID-19 and the associated lockdown measures on people's physical and mental wellbeing, as well as their daily lives and functioning, has been extensively studied. This study takes the approach of investigating the consequences of COVID-19 on a national scale, considering sociodemographic factors. The main objective is to make a contribution to ongoing research by specifically examining how age, gender, and marital status influence the overall impact of COVID-19 and wellbeing indicators during the second lockdown period that was implemented in response to the COVID-19 pandemic in the Greek population.

On stochastic dynamic modeling of incidence data.

In this paper, a Markov Regime Switching Model of Conditional Mean with covariates, is proposed and investigated for the analysis of incidence rate data. The components of the model are selected by both penalized likelihood techniques in conjunction with the Expectation Maximization algorithm, with the goal of achieving a high level of robustness regarding the modeling of dynamic behaviors of epidemiological data. In addition to statistical inference, Changepoint Detection Analysis is performed for the selection of the number of regimes, which reduces the complexity associated with Likelihood Ratio Tests.

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

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.

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

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.