Event Detail

Event Type: 
Mathematical Biology Seminar
Thursday, November 15, 2012 - 05:00 to 06:00
STAG 107

Speaker Info

Clemson University

The study of dengue dynamics at the population scale have significantly contributed to the
understanding of dengue transmission. Most studies have used point estimates of parameter
values derived from clinical and laboratory experiments: in particular, data on population-level
parameters such as transmission or susceptibility are extremely limited due to inability to fea-
sibly conduct experiments of infection in people and instead must be estimated from indirect
population-level data. We suggest a Bayesian approach which uses Monte Carlo Markov Chain
(MCMC) simulation to find estimates for the unknown parameters of a generic dengue mathe-
matical model we formulated based on previous dengue models. Prior knowledge is combined
with data on hospital visits to perform the statistical inference on the unknown parameters.
Our model allows for the inclusion of different hypotheses about dengue epidemiology and we
explore the consistency of clinical data with the epidemiological hypothesis by determining
goodness of fit of the model to the data for each combination of hypothesis. We use Akaike
Information Criterion on the results from the Bayesian MCMC on our dengue model and select
a model that most parsimoniously agrees with the data. A dengue vaccine is expected to be
available within 3-4 years and we explore the vaccine allocation policy to curb the dengue infec-
tion. Specifically, we consider the possibility of a transient period when instantaneous number
of infectious individuals can be higher than what the infectious number of individuals would
have been without the use of vaccination.