|dc.contributor.advisor||Streftaris, Doctor George||
|dc.contributor.advisor||Zachary, Doctor Stan||
|dc.contributor.author||Gamado, Kokouvi Mawuli||
|dc.description.abstract||The objective of this thesis is to study the e ect of under-reporting in epidemics. In
particular, there are two broad questions we investigate:
In the situation of under-reporting in epidemics, what would happen if the data
were treated as if no under-reporting were occurring? Such assumption leads
to an under-estimation of the contact rate, implying an under-estimation of the
By allowing for the fact that under-reporting is occurring, how and how well
can we estimate the reporting rate and other parameters of the model?
We explore the above questions by considering the stochastic Markovian SIR epidemic
in which various reporting processes are incorporated. We consider cases of constant
reporting probability and move on to more realistic assumptions such as the reporting
probability depending on time, the number of reported cases and the dependence on
the source of infection for each infected individual.
We develop various methodologies, based on temporal data, to account for underreporting
in the Bayesian framework using MCMC to sample from the posterior distributions
of the model parameters.
An introduction to the spatial aspect is also considered with the SIR model with
reporting process on Z.||en_US
|dc.publisher||Mathematical and Computer Sciences||en_US
|dc.rights||All items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.||
|dc.title||Inference for epidemics and effect of reporting processes||en_US