Unit:
Κατεύθυνση ΒιοστατιστικήLibrary of the School of Health Sciences
Author:
Koutsospyrou Christina
Supervisors info:
Νικόλαος Δεμίρης, Επικ. Καθηγητής, Τμήμα Στατιστικής, ΟΠΑ
Παναγιώτα Τουλούμη, Καθηγήτρια, Ιατρική Σχολή, ΕΚΠΑ
Βασιλική-Αναστασία Σύψα, Αναπλ. Καθηγήτρια, Ιατρική Σχολή, ΕΚΠΑ
Original Title:
Epidemic Models and their Application in the Analysis of Influenza Outbreaks
Translated title:
Epidemic Models and their Application in the Analysis of Influenza Outbreaks
Summary:
Influenza vaccine can be used to maximize health benefits through targeted vaccination policies. In general, vaccinations target specific target groups that are more susceptible to influenza virus. This process is combined with the need for financial efficiency and this can be achieved by epidemic simulation forward. Epidemic models have two main purposes. The first concerns the representation of the propagation mechanism and the assessment of the relevant parameters. The second goal is to use such estimates to predict the spread and control of diseases. In this work we try to reconstruct the Greek flu epidemic of a specific period. Using a modern documentation-synthesis approach, we use biological, clinical, epidemiological and behavioral data to develop a stratified age and risk transmission model that reproduces influenza behavior in specific strains for the 2017-2018 season in Greece, having consider getting vaccinated during this period. We estimate the number of infections as resulted from the historical check program compared to non-vaccination and the reduction, implementing different policies during the period. For this purpose, we use the fluEvidenseSynthesis package and replace several data sources with Greek flu data from the period 2017-2018 to calculate the number of people infected with the flu in that year. Such estimates can be used to guide control efforts to reduce the weight of influenza in view of the coming season.
Main subject category:
Science
Keywords:
Seasonal influenza, MCMC, Bayesian analysis, Epidemic models
