Project Title: Proposal for hands-on training on ecological data analysis using generalized linear mixed models (GLMMs) in R software to enhance malaria control strategies
Project Description: As the volume of data generated by various research initiatives continues to grow, the ability to analyze and interpret these datasets is crucial for making informed decisions in public health. Generalized Linear Mixed Models (GLMM) are increasingly recognized as essential tools in this domain, particularly for understanding ecological patterns and their implications for malaria control. GLMMs provide insights into how mosquito populations respond to interventions, and the factors influencing their abundance. To address these needs,... As the volume of data generated by various research initiatives continues to grow, the ability to analyze and interpret these datasets is crucial for making informed decisions in public health. Generalized Linear Mixed Models (GLMM) are increasingly recognized as essential tools in this domain, particularly for understanding ecological patterns and their implications for malaria control. GLMMs provide insights into how mosquito populations respond to interventions, and the factors influencing their abundance. To address these needs, we propose a three-day hands-on technical workshop focused on the application and deployment of GLMMs models using R. This workshop, scheduled for March 12- 14, 2025, at the Ifakara Health Institute, we will provide practical training to fifteen participants, including biostatisticians, epidemiologists, and researchers in the public health sector. The goal is to equip participants with the skills necessary to develop, implement, and monitor effective malaria control strategies using advanced statistical methods
Principal Investigator : Letus Muyanga
Department Name :
Time frame: (2024-12-03) - (2025-12-02)
AMMnet (Normal)
External Collaborating Partners
None added yet.