Abstract |
Epidemic outbreaks have been a major concern in public health throughout history. However from
the 14th century till now there have been a lot of discoveries about them. Especially when mathematical
methods were introduced to statistically support the data.
In late 2019 SARS-CoV-2 virus, or Covid-19 started spreading around the world. Soon after the
number of symptomatically infected and severely ill individuals overwhelmed the medical system
in many countries. It also lead to more than 4 million deaths by July 2020. This pandemic also
had severe consequences in the global economy due to disruption in manufacturing and services,
income reductions and rize of unemployment.
Public health officials use epidemiological models for disease surveilance and the investigation of
outbreaks, along with observational studies, in order to identify risk factors and implement disease
control measures. Although data are almost always available from occuring epidemics, they are
often incomplete due to underreporting. In particluar, for the Covid-19 epidemic there is mounting
evidence that some of the rapid spread of this virus has been driven by asymptomatic infections.
Due to this lack of reliable data mathematical modeling and computer simulations have been used
to perform theoretical experiments to estimate the parameters of the transmission mechanism and
the spread of the disease. Moreover, such experiments may be useful in comparing the effects of
preventive measures, such as social distancing or quarantine.
A well known epidemical model is the SIR model, as it gives results that are similar with the real
data.
The aim of this thesis is the analytical and computational study of an extended SIR model which
includes the class of asymptomatic individuals and compare its predictions with real Covid-19 data
from Greece and elsewhere.
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