Courtney Cuinn Murdock

The paper investigates how larval density, temperature, and relative humidity interact to shape mosquito population dynamics and traits in a semi-field experiment.

This study shows that fluctuating temperatures affect West Nile virus transmission dynamics differently than constant temperatures, with implications for epidemiological modeling.

This study examines how temperature and time affect gene expression in Anopheles stephensi infected with Plasmodium falciparum, revealing coordinated transcriptional responses in midgut and systemic tissues.

This study shows that temephos, a pesticide, changes how Aedes aegypti mosquitoes respond to temperature, reducing survival and altering population growth rates.

This study shows that humidity modifies the thermal performance of key fitness traits in Anopheles stephensi, altering population fitness projections compared to temperature-only models.

The paper evaluates multiple species distribution models for predicting Aedes albopictus distributions at its invasion edge using field-validation and finds variation in model performance across validation methods and invasion stages.

The paper shows that relative humidity systematically shifts the temperature dependence of population growth in Anopheles stephensi, a malaria vector.

The paper compares thermal suitability models for malaria transmission using constant and fluctuating temperatures, finding that constant temperature models better predict thermal limits than rate summation.

This study investigates when spatial structure should be included in epidemiological models and derives a critical threshold for diffusion coefficient affecting disease spread predictions.

This study examines how temperature affects the transcriptome of Anopheles stephensi mosquitoes, identifying gene expression patterns and their implications for mosquito physiology and malaria transmission.

This study examines how temperature affects mosquito gene expression and ZIKV infection outcomes by comparing transcriptomes at different temperatures.

The paper develops a data-driven model to predict potential Zika virus vectors by analyzing vector-virus trait combinations, identifying 35 species, including seven in the continental US.