Postdoc: Modeling Population Dynamics and Genetics of Mosquito-vectored diseases
PROJECT DESCRIPTION: Dengue is a mosquito-vectored disease that affects over
100 million people each year. With funding from the NIH and Gates
Foundation, we have developed a set of mathematical models ranging from
simple to complex, aimed at assisting the design and deployment of novel
approaches for suppressing transmission of dengue by its major mosquito
vector, Aedes aegypti. We are especially interested in evaluating the
potential utility of, as well as risks associated with, using genetically
engineered, selfish genetic elements to drive genes into mosquito
populations that render them incapable of transmitting dengue fever. Our
work also extends to models relevant to suppressing malaria.
Our most detailed model simulates the population dynamics and
population genetics of Ae. aegypti in a city on the Amazon river, Iquitos,
for which there are rich data sets on both mosquito dynamics and dengue
epidemiology. An accompanying epidemiological model is currently under
development. Both are coded in C++.
We are just completing a large-scale experiment in Iquitos to both test the
model and collect data that can be used to improve the parameterization of
all Ae. aegypti models. In the experiment, the mosquito population in one
area of the city is suppressed and then allowed to return to normal
densities. Although our population dynamics model and many others have been
tested to determine if they predict equilibrium dynamics, a much more
stringent test is to determine if they can predict response to a
perturbation. Following a transition period interfacing with our current
postdoc, the new postdoc will assume responsibility for leading the analysis
of these data and will further develop the model to examine the dynamic
behavior and efficacy of a set of novel transgenic and non-transgenic
approaches for manipulating the mosquito population. These innovative
technologies and strategies are being developed by our colleagues, so
empirical data on small field tests will be available. We are also building
simple spatial and non-spatial, deterministic models as heuristic tools for
better understanding basic principles.
In addition to working on model development and analysis, and on
comparisons between model and data, the person in this position will
collaborate in an interdisciplinary research group composed of mosquito
ecologists, disease epidemiologists, molecular biologists,
biomathematicians, ethicists, and scientists from disease-endemic countries,
in efforts to develop novel transgenic strategies for disease reduction. The
person in this position will have the opportunity to spend time in Iquitos
to better understand the system being modeled. Desirable skills include the
ability to program in C++ or knowledge of a related programming language,
statistical experience, particularly in parameter estimation and evaluation
of performance of mechanistic models.
For more details on the project see the following publications:
Magori, K., M. Legros, M. Puente, D. A. Focks, T. W. Scott, A. Lloyd, F,
Gould. 2009. Skeeter Buster: a stochastic, spatially-explicit modeling tool
for studying Aedes aegypti population replacement and population suppression
strategies. PLoS Negl Trop Dis 3(9): e508. doi:10.1371/journal.pntd.0000508
Gould, F., Huang, Y., Legros, M., Lloyd, A. L. 2008. A killer-rescue system
for self-limiting gene drive of anti-pathogen constructs. Proc. Royal. Soc.
Lond. B. 275:2823-2829.
Xu, C., Legros, M., Gould, F, Lloyd, A. L. 2010.Understanding Uncertainties
in Model-Based Predictions of Aedes aegypti Population Dynamics. PLoS Negl.
Trop. Dis. 4(9): e830. doi:10.1371/journal.pntd.0000830
Huang, Y., Lloyd, A.L., Legros, M., Gould, F. 2010. Gene-drive into insect
populations with age and spatial structure: a theoretical assessment. Evol.
Appl. ISSN 1752-4571.
Okamoto K. W., M. A. Robert, A. L. Lloyd, F. Gould. 2013. A reduce and
replace strategy for suppressing vector-borne diseases: Insights from a
stochastic, spatial model. PLoS ONE 8(12): e81860.
doi:10.1371/journal.pone.0081860
To apply: email a cover letter and CV to [email protected] and Alun
[email protected]
