Research

Overview

My research focuses on developing and analyzing mathematical models of mosquito population dynamics in response to environmental conditions such as temperature, rainfall, and photoperiod. These models help uncover how seasonal and climatic factors shape mosquito life cycles and population trends. I conduct this research under the supervision of Dr. Amy Hurford.

Modeling Approaches

I use both deterministic and stochastic modeling frameworks to simulate mosquito life stages — from eggs to adults — incorporating biological processes such as diapause. My models integrate climate data to capture realistic seasonal patterns, allowing for robust predictions under varying environmental conditions.

Applications

The models I develop are applied to assess:

• The impact of seasonal climate variability and climate change on mosquito abundance.
  
• The timing and intensity of mosquito activity, including peak abundance periods.
  
• The potential for disease transmission under different environmental scenarios.

Analytical Tools

To evaluate the robustness of model predictions, I apply sensitivity analysis techniques such as Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC), alongside uncertainty analysis to quantify the effects of input variability. I also use scenario testing to explore alternative climate and intervention conditions, and employ visualization tools (e.g., ggplot2 in R) to communicate findings. These approaches help identify the parameters most influential on population outcomes and guide targeted interventions.

Code and Data

The code and data used in my research are openly available on GitHub:
- Mosquito Dynamics Model
- Climate Data Analysis
- Sensitivity Analysis