PubMed İndeksli Yayınlar Koleksiyonu

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Citation - Scopus: 2
Analysis of inoculation strategies during COVID-19 pandemic with an agent-based simulation approach
(Elsevier Ltd, 2025) Oray Kulaç; Ayhan Özgür Toy; Kamil Erkan Kabak; Toy, Ayhan Özgür; Kabak, Kamil Erkan; Kulaç, Oray
Background: The severity of recent Coronavirus (COVID-19) pandemics has revealed the importance of development of inoculation strategies in case of limited vaccine availability. Authorities have implemented inoculation strategies based on perceived risk factors such as age and existence of other chronic health conditions for survivability from the disease. However various other factors can be considered for identifying the preferred inoculation strategies depending on the vaccine availability and disease spread levels. This study explores the effectiveness of inoculating different groups of population in case of various vaccine availabilities and disease spread levels by means of some performance metrics namely: Attack Rate (AR) Death Rate (DR) and Hospitalization Rate (HR). Method: In this study we have implemented a highly detailed Agent-Based Simulation (ABS) model that extends classical SEIR Model by including five more additional states: Asymptomatic (A) Quarantine (Q) Hospitalized (H) Dead (D) and Immune (M) which can be used as a decision support tool to prioritize the groups of the population inoculated. The approach employs the modelling of daily mobility of individuals their interactions and transmission of virus among individuals. The population is heterogeneously clustered according to age family size work status transportation and leisure preferences with 17 different groups in order to find the most appropriate one to inoculate. Three different Disease Spread Levels (DSL) (low mid high) are experimented with four different Vaccine Available Percentages (VAP) (25% 50% 75% and 85%) with a total of 84 scenarios. Results: As the benchmark under the No Vaccine case Attack Rate Hospitalization Rate and Death Rate goes as high as 99.53% 16.96% and 1.38% respectively. Corresponding highest performance metrics (rates) are 72.33% 15.95% and 1.35% for VAP = 25%, 50.25% 9.55% and 0.94% for VAP = 50%, 24.53% 2.62% and 0.25% for VAP = 75%, and 11.51% 0.002% and 0.08% for VAP = 85%. The results of our study shows that the common practice of inoculation based on the age of individual does not yield the best outcome in terms of performance metrics across all DSL and VAP values. The groups containing workers and students that represent highly interactive individuals i.e. Group (9 10) Group (9 11 10‾) and Group (9 10 11 12‾) emerge as a commonly recommended choice for inoculation in the majority of cases. As expected we observe that the higher is the VAP levels the more is the number of alternative inoculation groups. Conclusions: Findings of this study present that: (i) inoculation considerably decreases the number of infected individuals the number of deaths and the number of hospitalized individuals due to the disease (ii) the best inoculation group/groups with respect to performance metrics varies depending on the vaccine availability percentages and disease spread levels (iii) simultaneous implementation of both inoculation and precautions like lock-down social distances and quarantines yields a stronger impact on disease spread and its consequences. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 30
Citation - Scopus: 35
Determining a continuous marker for sleep depth
(Pergamon-Elsevier Science Ltd, 2007) Musa Hakan Asyali; Richard Barnett Berry; Michael C.K. Khoo; Ayşe Asyali Altinok; Khoo, Michael C.K.; Asyali, Musa H.; Berry, Richard B.; Altinok, Ayse
Detection and quantification of sleep arousals is an important issue as the frequent arousals are known to reduce the quality of sleep and cause daytime sleepiness. In typical sleep staging electroencephalograph (EEG) is the core signal and based on the visual inspection of the frequency content of EEG non-rapid eye movement sleep is staged into four somewhat rough categories. In this study we aimed at developing a continuous marker based on a more rigorous spectral analysis of EEG to measure or quantify the depth of sleep. In order to develop such a marker we obtained the time-frequency map of two EEG channels around sleep arousals and identified the frequency bands that show the most change during arousals. We then evaluated classification performance of the potential signals for representing the depth of sleep using receiver operating characteristic analysis. Our comparisons based on the area under the curve values revealed that the sum of absolute powers in alpha and beta bands is a good continuous marker to represent the depth of sleep. Higher values of this marker indicate low-quality sleep and vice versa. We believe that use of this marker will lead to a better quantification of sleep quality. © 2007. © 2008 Elsevier B.V. All rights reserved., MEDLINE® is the source for the MeSH terms of this document.
Citation - WoS: 9
Citation - Scopus: 13
Gene expression profile class prediction using linear Bayesian classifiers
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Musa H. Asyali; Asyali, Musa H.
Due to recent advances in DNA microarray technology using gene expression profiles diagnostic category of tissue samples can be predicted with high accuracy. In this study we discuss shortcomings of some existing gene expression profile classification methods and propose a new approach based on linear Bayesian classifiers. In our approach we first construct gene-level linear classifiers to identify genes that provide high class-prediction accuracies i.e. low error rates. After this screening phase starting with the gene that offers the lowest error rate we construct a multi-dimensional linear classifier by incorporating next best-per-forming genes until the prediction error becomes minimum or 0 if possible. When we compared classification performance of our approach against prediction analysis of microarrays (PAM) and support vector machines (SVM) based approaches we found that our method outperforms PAM and produces comparable results with SVM. In addition we observed that the gene selection scheme of PAM could be misleading. Albeit SVM achieves relatively higher prediction performance it has two major disadvantages: Complexity and lack of insight about important genes. Our intuitive approach offers competing performance and also an efficient means for finding important genes. (c) 2007 Elsevier Ltd. All rights reserved.

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