📊 Modelling
Mathematical modeling for infectious diseases is a powerful tool used to understand, predict, and control disease dynamics. Burundi faces several gaps in this field that limit its use in effective public health planning.
🔍 1. Limited Data Availability
- Challenge: Models need timely and granular data (cases, mobility, climate, demographics).
- Gaps: Data systems are fragmented and under-resourced.
- Impact: Limits calibration, validation, and accuracy of predictions.
🧠 2. Capacity and Expertise
- Challenge: Modeling requires specialized skills in epidemiology, mathematics, and computation.
- Gaps: Few trained modelers; weak integration into decision-making.
- Impact: Models may be unused or misapplied in policies.
🧪 3. Infrastructure for Advanced Modeling
- Challenge: Advanced models require high computational resources.
- Gaps: Poor access to modeling platforms and tools.
- Impact: Limits real-time simulations for outbreaks.
🌍 4. Localized Modeling
- Challenge: Models must reflect Burundi’s specific dynamics and environment.
- Gaps: Most models are based on external templates with little adaptation.
- Impact: Reduces relevance of modeled interventions.
🤝 5. Integration with Public Health Policy
- Challenge: Models should inform decisions on response strategies.
- Gaps: Weak collaboration between modelers and health authorities.
- Impact: Missed opportunities for data-driven planning.