George Tsaklidis - Academia.edu (original) (raw)

Papers by George Tsaklidis

arXiv (Cornell University), Jul 3, 2022

The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date... more The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV modelan extension/improvement of the classic SIR compartmental modelwhich also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number 0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimate 0. The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters. Furthermore, the relevant analysis supports the importance of vaccination, as even a small increase in the dialy vaccination rate could lead to a notable reduction in mortality and hospitalizations.

Physica D: Nonlinear Phenomena, Mar 1, 2020

Change point analysis is performed on the seismicity in Gulf of Corinth (Greece), an extensional ... more Change point analysis is performed on the seismicity in Gulf of Corinth (Greece), an extensional graben which constitutes one of the most seismically active areas in Greece. Seismicity appears intense and strongly clustered and therefore analysis on mean and variance is appropriate. Sample autocorrelation function of the data is non-zero even for bigger lags, indicating long-range correlations. This phenomenon can be justified by possible changes in the mean of the observations. Non-parametric multiple change point analysis is applied to both the sequence of the earthquakes from a set of observations and its detrended data considering the earthquake occurrence frequency. The results of the analysis on the initial data set are compared to those of its detrended residuals. This procedure employs both online and offline methods providing different perspectives. Promising patterns are defined offline and most of them are detectable online.

arXiv (Cornell University), Nov 14, 2019

This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlat... more This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements which belong to some interval with given censoring limits, is considered. Two improvements of the standard TKF process are proposed, in order to estimate the hidden state vectors. Firstly, the exact covariance matrix of the censored measurements is calculated by taking into account the censoring limits. Secondly, the probability of a latent (normally distributed) measurement to belong in or out of the uncensored region is calculated by taking into account the Kalman residual. The designed algorithm is tested using both synthetic and real data sets. The real data set includes human skeleton joints' coordinates captured by the Microsoft Kinect II sensor. In order to cope with certain real-life situations that cause problems in human skeleton tracking, such as (self)-occlusions, closely interacting persons etc., adaptive censoring limits are used in the proposed TKF process. Experiments show that the proposed method outperforms other filtering processes in minimizing the overall Root Mean Square Error (RMSE) for synthetic and real data sets. Keywords Censored data • Adaptive Tobit Kalman filter • Human skeleton tracking.

EGUGA, Apr 1, 2012

ABSTRACT The present paper aims at the application of hidden semi-Markov models (HSMMs) in an att... more ABSTRACT The present paper aims at the application of hidden semi-Markov models (HSMMs) in an attempt to reveal key features for the earthquake generation, associated with the actual stress field, which is not accessible to direct observation. The models generalize the hidden Markov models by considering the hidden process to form actually a semi-Markov chain. Considering that the states of the models correspond to levels of actual stress fields, the stress field level at the occurrence time of each strong event is revealed. The dataset concerns a well catalogued seismically active region incorporating a variety of tectonic styles. More specifically, the models are applied in Greece and its surrounding lands, concerning a complete data sample with strong (M≥ 6.5) earthquakes that occurred in the study area since 1845 up to present. The earthquakes that occurred are grouped according to their magnitudes and the cases of two and three magnitude ranges for a corresponding number of states are examined. The parameters of the HSMMs are estimated and their confidence intervals are calculated based on their asymptotic behavior. The rate of the earthquake occurrence is introduced through the proposed HSMMs and its maximum likelihood estimator is calculated. The asymptotic properties of the estimator are studied, including the uniformly strongly consistency and the asymptotical normality. The confidence interval for the proposed estimator is given. We assume the state space of both the observable and the hidden process to be finite, the hidden Markov chain to be homogeneous and stationary and the observations to be conditionally independent. The hidden states at the occurrence time of each strong event are revealed and the rate of occurrence of an anticipated earthquake is estimated on the basis of the proposed HSMMs. Moreover, the mean time for the first occurrence of a strong anticipated earthquake is estimated and its confidence interval is calculated.

Physica D: Nonlinear Phenomena, May 1, 2020

Journal of Seismology, Dec 16, 2015

The 2013 Aigion earthquake swarm that took place in the west part of Corinth Gulf is investigated... more The 2013 Aigion earthquake swarm that took place in the west part of Corinth Gulf is investigated for revealing faulting and seismicity properties of the activated area. The activity started on May 21 and was appreciably intense in the next 3 months. The recordings of the Hellenic Unified Seismological Network (HUSN), which is adequately dense around the affected area, were used to accurately locate 1501 events. The double difference (hypoDD) technique was employed for the manually picked P and S phases along with differential times derived from waveform crosscorrelation for improving location accuracy. The activated area with dimensions 6 × 2 km is located approximately 5 km SE of Aigion. Focal mechanisms of 77 events with M ≥ 2.0 were determined from P wave first motions and used for the geometry identification of the ruptured segments. Spatio-temporal distribution of earthquakes revealed an eastward and westward hypocentral migration from the starting point suggesting the division of the seismic swarm into four major clusters. The hypocentral migration was corroborated by the Coulomb stress change calculation, indicating that four fault segments involved in the rupture process successively failed by stress change encouragement. Examination of fluid flow brought out that it cannot be unambiguously considered as the driving mechanism for the successive failures.

Research Square (Research Square), Oct 10, 2022

Journal of Applied Probability, Jun 1, 1994

In order to describe the evolution of the attainable structures of a homogeneous Markov system (H... more In order to describe the evolution of the attainable structures of a homogeneous Markov system (HMS) with fixed size, we evaluate the volume of the sets of the attainable structures in Euclidean space as they are changing in time and we find the value of the volume asymptotically. We also estimate the evolution of the distance of two (attainable) structures of the system as it changes following the transformations of the structures; extensions are obtained concerning results from the Perron–Frobenius theory referring to Markov systems.

Journal of Applied Statistics, Oct 9, 2018

The Markovian Arrival Process (MAP) is applied as a candidate model to describe the time-varying ... more The Markovian Arrival Process (MAP) is applied as a candidate model to describe the time-varying earthquake activity in Corinth Gulf, Greece. To the best of our knowledge, this is the first attempt to study the earthquake temporal evolution with the specific class of MAPs. A complete catalogue is used for the earthquake temporal distribution investigation, along with data sets of different magnitude cutoffs. The study area is divided into its western and eastern subareas, and possible variations in the earthquake occurrence times were sought. Hidden states of MAPs correspond to different levels of seismicity, and hence various numbers of states are examined. Akaike and Bayes information criteria are implemented for identifying the best model, and comparison to the most known and broadly accepted theoretical interevent time distributions is provided. In all cases, the fitted MAPs with phase type distributed intearrival times outperform the models with other distributions. Important indicators of the underlying Markov process are computed, and the earthquake frequency is approximated by the counting process. The analysis demonstrates high index of burstiness for the earthquake generation in the eastern part, i.e. long quiescent periods alternate with short ones of intense seismic activity.

International Journal for Numerical Methods in Biomedical Engineering, Sep 3, 2022

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that ... more Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on Electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we firstly outline the digitalization process of paper-based ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to ECG waveform, producing undesirable noise. Next, we propose the utilization of a low-complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in contrast with other arrhythmogenic abnormalities. Finally, by performing spectral analysis we investigate significant differentiations in the field of frequencies between normal ECGs and ECGs corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies where we encounter statistically significant differentiations, the normal ECGs are characterized by greater normalized amplitudes compared to the abnormal ones. The overall research carried out in this article highlights the importance of integrating mathematical methods into the examination and effective diagnosis of various diseases, aiming to a substantial contribution to their successful treatment.

Physica D: Nonlinear Phenomena, Oct 1, 2022

arXiv (Cornell University), Feb 20, 2020

This paper concerns Kalman filtering when the measurements of the process are censored. The censo... more This paper concerns Kalman filtering when the measurements of the process are censored. The censored measurements are addressed by the Tobit model of Type I and are one-dimensional with two censoring limits, while the (hidden) state vectors are multidimensional. For this model, Bayesian estimates for the state vectors are provided through a recursive algorithm of Kalman filtering type. Experiments are presented to illustrate the effectiveness and applicability of the algorithm. The experiments show that the proposed method outperforms other filtering methodologies in minimizing the computational cost as well as the overall Root Mean Square Error (RMSE) for synthetic and real data sets.

Chaos Solitons & Fractals, 2023

Physica A: Statistical Mechanics and its Applications, 2013

ABSTRACT h i g h l i g h t s • We apply discrete-time HMMs in order to reveal the stress field un... more ABSTRACT h i g h l i g h t s • We apply discrete-time HMMs in order to reveal the stress field underlying the earthquake generation. • By following two different approaches, we compare the models under study and select the optimal for each approach. • Based on the optimal model in both approaches we interpret the estimated parameters and we obtain forecasting results. a b s t r a c t The application of the hidden Markov models (HMMs) is attempted for revealing key features for the earthquake generation which are not accessible to direct observation. Considering that the states of the HMM correspond to levels of the stress field, our objective is to identify these states. The observations are considered after grouping earthquake magnitudes and the cases of different number of states are examined. The problems of HMMs theory are solved and the ensuing HMMs are compared on the basis of Akaike and Bayesian information criteria. A new insight on the evaluation of future seismic hazard is given by calculating the mean number of steps for the first visit to a particular state, along with the respective variance. We further calculate an estimator of the mean number of steps for the first visit to a particular state and we construct its confidence interval. Additionally, a second approach to the problem is followed by assuming a different determination of observations. The HMMs applied to both approaches, contribute significantly to seismic hazard assessment via revealing the number of the stress levels as well as the way in which these levels are associated with certain earthquake occurrence.

Journal of Applied Statistics, 2016

ABSTRACT The real stress field in an area associated with earthquake generation cannot be directl... more ABSTRACT The real stress field in an area associated with earthquake generation cannot be directly observed. For that purpose we apply hidden semi-Markov models (HSMMs) for strong earthquake occurrence in the areas of North and South Aegean Sea considering that the stress field constitutes the hidden process. The advantage of HSMMs compared to hidden Markov models (HMMs) is that they allow any arbitrary distribution for the sojourn times. Poisson, Logarithmic and Negative Binomial distributions as well as different model dimensions are tested. The parameter estimation is achieved via the EM algorithm. For the decoding procedure, a new Viterbi algorithm with a simple form is applied detecting precursory phases (hidden stress variations) and warning for anticipated earthquake occurrences. The optimal HSMM provides an alarm period for 70 out of 88 events. HMMs are also studied presenting poor results compared to these obtained via HSMMs. Bootstrap standard errors and confidence intervals for the parameters are evaluated and the forecasting ability of the Poisson models is examined.

Méthodes et modèles statistiques pour la sismogenèse, 2023