We employ mathematical modelling skills to understand the emergence, transmission, spread and control of infectious diseases. The goal of our research is to gain insights from data and provide evidence that guides policies on prevention and control of these infectious diseases to improve public health.
Our mathematical modeling projects focus on understanding disease transmission dynamics and evaluating intervention strategies to guide evidence-based public health policies.
Utilizing mathematical models to evaluate and compare the effectiveness of various malaria control interventions across different subnational regions in Kenya, providing evidence-based recommendations for targeted malaria control strategies.
Mathematical analysis of measles vaccination strategies, examining how different dosage regimens and coverage levels affect measles elimination goals in Kenya, supporting national immunization program optimization.
Developing mathematical models to support chikungunya vaccine trial design and implementation, providing insights into optimal trial parameters and expected vaccine efficacy outcomes for public health decision-making.
Mathematical modeling to identify and optimize treatment strategies for schistosomiasis elimination in Kenya, analyzing different intervention scenarios to accelerate progress towards elimination targets.
Comprehensive mathematical analysis of intervention strategies for soil transmitted helminths control and elimination, providing evidence-based guidance for national deworming programs and elimination efforts.
Mathematical modeling study focused on identifying optimal strategies to accelerate rabies elimination in Kenya, analyzing vaccination coverage, outbreak response, and surveillance system effectiveness.
Mathematical assessment of current progress and future projections for Taenia solium control in Kenya, providing insights into intervention effectiveness and recommendations for enhanced control strategies.