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PhD Defense - Ying Zhou

Start: 8/20/2013 at 8:30AM
End: 8/20/2013 at 12:00PM
Location: 100 Stinson Remick
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Ying Zhou , a Computer Science and Engineering PhD candidate, will present and defend her doctoral dissertation on August 20, 2013 at 8:30 am in 100 Stinson Remick.

Her advisor, Dr. Gregory Madey will be in attendance with committee members: Dr. Nora Beransky, Dr. Brian Blake and Dr.Frank Collins. Faculty and students are welcome to attend the presentation portion of her defense.  Light refreshments will be served.



The first one is presented as a complete case study (PGMS) that uses Geo-tagged PDAs to electronically collect malaria-related data in the field. It includes two major parts: the database designed for subsequent cross-sectional data analysis and the programs customized for the six study sites. The design principles adopted in PGMS can be applicable to a wide variety of data collection implementations. The experience we learnt from this case study can be also used by smart phone-based, tablet-based or netbook based surveys.

Due to the Anopheles gambiae’s pivotal role in malaria transmission, modeling its population dynamics can assist in finding factors in the mosquito life cycle that can be targeted to decrease malaria transmission to a lower level. This dissertation presents an agent-based weather-aware entomological model of the complete Anopheles gambiae mosquito life cycle. The model tracks the development and the mortality rates of mosquitoes in eight distinct life stages: Egg, Larva, Pupa, Immature Adult, Mate Seeking, Blood meal Seeking, Blood meal Digesting, and Gravid. It also models the transitions among these stages.



We also explore an application of this model, theoretically
comparing the effectiveness of various hypothetical vector control
interventions that target mosquitoes in different life stages and mosquitoes
having different mosquito biological behavior applied separately and jointly on
the reduction of mosquito abundance and malaria transmission capacity of a mosquito

The emergence and prevalence of insecticide resistance in
mosquitoes greatly undermines the effectiveness and sustainability of vector
control measures such as long-lasting insecticidal nets (LLINs) and indoor
residual spraying (IRS) that use insecticides. Read et al. proposed
hypothetical evolution-proof late-life-acting (LLA) insecticidal strategies to
deal with mosquito insecticide resistance, and found that LLAs could provide
more effective malaria control while more weakly resistance evolution in a
mosquito population than the current insecticides approved for LLINs and IRS.

This dissertation proposes another application of the above model
that evaluates the late-life-acting insecticides and instant-acting
insecticides in terms of malaria control and evolution-proof of insecticide
resistance. We come to the similar conclusions presented by Read. And we also
relaxed Read’s strict-timing working pattern of LLAs by allowing a LLA
insecticide to target the mosquitoes in much more wide age range. Our
simulation results show although the relaxed-timing LLAs’ performance of
controlling mosquitoes and malaria transmission is less effectively than the
strict-timing LLAs, they still work more efficiently than the traditional
instant insecticides.