Climate-Driven Malaria Modeling with VECTRI¶

Spatial Patterns

Time-averaged mosquito density showing transmission hotspots

Temporal Dynamics

Seasonal cycles in vector populations over time

Disease Metrics

Entomological Inoculation Rate (EIR) distribution

Seasonal Analysis

Average seasonal cycle of vector density

Seasonal Maps

Transmission patterns across different seasons

Variable Relationships

Correlations between key VECTRI variables

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Overview¶

This training is designed to help participants understand how climate-driven malaria modeling using VECTRI can be used to enhance malaria early warning systems and support evidence-based public health interventions in Ethiopia. In particular, this training is prepared in collaboration between the Swedish Meteorological and Hydrological Institute (SMHI) and Addis Ababa University (AAU) to support participants from the Ethiopian Meteorological Institute (EMI).

It will cover both foundational concepts and hands-on practical applications using real-world data from the Amhara region. This training is financed by the Swedish International Development Cooperation Agency (Sida) as part of the Water and Climate Change Services for Africa, Ethiopia (WACCA-E), phase 2 project.

Workshop Details¶

Target Audience¶

• EMI health, hydrology and meteorology team
• Masters and PhD students from AAU
• Experts from Ethiopian Public Health Institute (EPHI)

Learning Outcomes¶

By the end of this training, participants will be able to:

1. Source, quality check, and preprocess ERA5/CHIRPS climate data into daily, VECTRI-ready NetCDF format (rainfall, 2-m temperature)
2. Compile and run VECTRI; interpret outputs (EIR - Entomological Inoculation Rate, HBR - Human Biting Rate, cases) and evaluate lags (EIR→cases)
3. Understand the biological basis of malaria transmission and how climate variables drive vector and parasite dynamics
4. Create environmental input files (population, soil type) for VECTRI modeling
5. Conduct spatial and temporal analysis of model outputs to identify malaria transmission hotspots and seasonal patterns

Daily Structure (Quick Glance)¶

Requirements¶

Software & Tools¶

• Python 3.8+ (Jupyter Notebooks)
• VECTRI model (compiled from source)
• Linux/Unix environment (native or WSL2)
• NetCDF utilities

Key Datasets¶

• Climate:

  • Rainfall

    • Historical: CHIRPS, ARC2, TAMSAT
    • Near-real-time: NCEP-GFS, ECMWF HRES
    • Sub-seasonal: ECMWF S2S, ECMWF S2S Ensemble
    • Seasonal: ECMWF Seasonal (C3S)
    • Long-term projections: CHC-CMIP6, CMIP6, ISIMIP3b, Regional products (e.g. CORDEX)

  • Temperature:

    • Historical: CHIRTS, ERA5-Land, ERA5
    • Near-real-time: NCEP-GFS, ECMWF HRES
    • Sub-seasonal: ECMWF S2S, ECMWF S2S Ensemble
    • Seasonal: ECMWF Seasonal (C3S), NCEP CFSv2, NMME
    • Long-term projections: CHC-CMIP6, CMIP6, ISIMIP3b, Regional products (e.g. CORDEX)

• Population data:

  • AfriPop (WorldPop)
  • WorldPop Projections

• Soil Type/Soil Fraction:

  • Harmonized World Soil Database (HWSD)

• Geographic:

  • Administrative boundaries (shapefiles) GADM/FAO GAUL

• Malaria: EPHI confirmed case data

Facilitators¶

Interactive Learning with Binder¶

Experience hands-on learning with our interactive Jupyter notebooks! No installation required - just click and start coding.

Launch Interactive Environment

Includes all lessons, sample climate data, and pre-configured Python environment.

💡 What is Binder?

A free service that turns our GitHub repository into a live, interactive Jupyter environment. Perfect for following along with lessons or experimenting with code!

💬 Real-Time Collaboration¶

Join our dedicated real-time collaborative space for Q&A, notes, and discussions during training sessions:

For more collaboration options, visit our full collaboration guide.

Participants List¶

📱 Scan to Access Workshop Materials

vectri-emi-smhi.netlify.app

Access on mobile device

Contact¶

For inquiries about this workshop, please contact:

Yonas Mersha

Hydro-Climate Modelling and ML/AI Expert
International Livestock Research Institute (ILRI)

📧 yonas.mersha14@gmail.com

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