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Πλοήγηση Διδακτορικές διατριβές ανά Συγγραφέα "Kondakis, Marios"

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  • Μικρογραφία εικόνας
    Τεκμήριο
    Some statistical models in ecology and epidemiology
    (12/13/2021) Kondakis, Marios; Κονδάκης, Μάριος; Athens University of Economics and Business, Department of Statistics; Ντζούφρας, Ιωάννης; Κυριακίδης, Επαμεινώνδας; Παππά, Μαρία; Κυπραίος, Θεόδωρος; Γιαννακόπουλος, Αθανάσιος; Καλογερόπουλος, Κωνσταντίνος; Δεμίρης, Νικόλαος
    This dissertation focuses on statistically modelling specific biological processes from a Bayesian standpoint and it can divided into four components. The first component is concerned with ecological models that account for uncertainty and describe the fitness of insects, as explained by deterministic and stochastic demographic models, in order to understand the population performance of invasive species. The second component involves the investigation of non-linear statistical models based on popular ecological functions that describe the developmental process of arthropods as it is affected by temperature. Statistical modelling may provide insights into the population evolution of arthropod pests, which is important for ecology. Moreover, we investigate various computation techniques in order to not only derive robust estimates of the parameters of interest, but also to compare different models and computation methods. The third component entails modelling predator-prey systems to account for changes in prey population consumption over time as well as inter-individual interactions within the same species. Hence, we study statistical models that generate data using the Binomial distribution while prey density change in real time is described via ordinary differential equations (ode) ecological models. To address the possibility of noise, we propose that the probability of being consumed be linked to a stochastic process that is centered and reduced to (in the absence of diffusion) the instantaneous ratio of consumed prey density (which is the default link). The fourth section differs from the previous sections in that it focuses on modeling and detection of the spread of Vector-borne diseases (VBDs), as well as the development of a semi-automatic early warning system for the prevention of these diseases in the context of epidemiology. A generic observation running throughout this work is that detailed and robust modelling may assist greatly in more accurate and cautious conclusions drawn when interpreting the data.