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Description
Pre-Exposure Prophylaxis (PrEP) is any medical or public health procedure used before exposure to the disease causing agent, its purpose is to prevent, rather than treat or cure a disease. Most commonly, PrEP refers to an experimental HIV-prevention strategy that would use antiretrovirals to protect HIV-negative people from HIV infection.

Pre-Exposure Prophylaxis (PrEP) is any medical or public health procedure used before exposure to the disease causing agent, its purpose is to prevent, rather than treat or cure a disease. Most commonly, PrEP refers to an experimental HIV-prevention strategy that would use antiretrovirals to protect HIV-negative people from HIV infection. A deterministic mathematical model of HIV transmission is developed to evaluate the public-health impact of oral PrEP interventions, and to compare PrEP effectiveness with respect to different evaluation methods. The effects of demographic, behavioral, and epidemic parameters on the PrEP impact are studied in a multivariate sensitivity analysis. Most of the published models on HIV intervention impact assume that the number of individuals joining the sexually active population per year is constant or proportional to the total population. In the second part of this study, three models are presented and analyzed to study the PrEP intervention, with constant, linear, and logistic recruitment rates. How different demographic assumptions can affect the evaluation of PrEP is studied. When provided with data, often least square fitting or similar approaches can be used to determine a single set of approximated parameter values that make the model fit the data best. However, least square fitting only provides point estimates and does not provide information on how strongly the data supports these particular estimates. Therefore, in the third part of this study, Bayesian parameter estimation is applied on fitting ODE model to the related HIV data. Starting with a set of prior distributions for the parameters as initial guess, Bayes' formula can be applied to obtain a set of posterior distributions for the parameters which makes the model fit the observed data best. Evaluating the posterior distribution often requires the integration of high-dimensional functions, which is usually difficult to calculate numerically. Therefore, the Markov chain Monte Carlo (MCMC) method is used to approximate the posterior distribution.
ContributorsZhao, Yuqin (Author) / Kuang, Yang (Thesis advisor) / Taylor, Jesse (Committee member) / Armbruster, Dieter (Committee member) / Tang, Wenbo (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Using a simple $SI$ infection model, I uncover the

overall dynamics of the system and how they depend on the incidence

function. I consider both an epidemic and endemic perspective of the

model, but in both cases, three classes of incidence

functions are identified.

In the epidemic form,

power incidences, where the infective portion $I^p$

Using a simple $SI$ infection model, I uncover the

overall dynamics of the system and how they depend on the incidence

function. I consider both an epidemic and endemic perspective of the

model, but in both cases, three classes of incidence

functions are identified.

In the epidemic form,

power incidences, where the infective portion $I^p$ has $p\in(0,1)$,

cause unconditional host extinction,

homogeneous incidences have host extinction for certain parameter constellations and

host survival for others, and upper density-dependent incidences

never cause host extinction. The case of non-extinction in upper

density-dependent

incidences extends to the case where a latent period is included.

Using data from experiments with rhanavirus and salamanders,

maximum likelihood estimates are applied to the data.

With these estimates,

I generate the corrected Akaike information criteria, which

reward a low likelihood and punish the use of more parameters.

This generates the Akaike weight, which is used to fit

parameters to the data, and determine which incidence functions

fit the data the best.

From an endemic perspective, I observe

that power incidences cause initial condition dependent host extinction for

some parameter constellations and global stability for others,

homogeneous incidences have host extinction for certain parameter constellations and

host survival for others, and upper density-dependent incidences

never cause host extinction.

The dynamics when the incidence function is homogeneous are deeply explored.

I expand the endemic considerations in the homogeneous case

by adding a predator into the model.

Using persistence theory, I show the conditions for the persistence of each of the

predator, prey, and parasite species. Potential dynamics of the system include parasite mediated

persistence of the predator, survival of the ecosystem at high initial predator levels and

ecosystem collapse at low initial predator levels, persistence of all three species, and much more.
ContributorsFarrell, Alexander E. (Author) / Thieme, Horst R (Thesis advisor) / Smith, Hal (Committee member) / Kuang, Yang (Committee member) / Tang, Wenbo (Committee member) / Collins, James (Committee member) / Arizona State University (Publisher)
Created2017