Mathematical modeling has been crucial to address fundamental issues related to
COVID-19 disease-control policy decisions. This thesis deals with the robust
quantification of the disease burden associated with COVID-19 across different
socio-demographic settings. The presented work includes the statistical analysis of
novel epidemiological records to provide solid estimates describing the clinical
course of SARS-CoV-2 infections and the simulation of data-driven models to
forecast the potential impact of COVID-19 in rural and urban areas of Ethiopia.
Obtained estimates show that being older than 60 years of age is associated with
about 40% likelihood of developing symptoms after SARS-CoV-2 infection and 1%
risk of requiring intensive care. The analysis of potential SARS-CoV-2 transmission
in Ethiopia suggests that the low prevalence and mortality observed during 2020
can be explained by combined effect of younger demography and a reduced
transmission generated by school closures implemented in response to the
pandemic. Provided estimates highlight that in this country, after the launch of
vaccination in 2021, the highest fraction of severe cases is expected to arise from the
interaction between children (who are the main responsible for the spread of the
disease) with the elderly (representing the most vulnerable population segment).
Remarkably, prioritizing the vaccination of the elderly emerged as the best strategy
to reduce the number of critical patients, irrespectively to the limited number of
doses made available to low-income settings.
