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Assessment of Climate Driven Variations in Malaria Transmission in Amhara Region Using the VECTRI Model

Researcher: Hailu Fentaw
Institution: Addis Ababa University
Date: June 2025

1. Research Context & Problem Statement

  • The Challenge: Malaria remains a pressing public health concern in Ethiopia, particularly in the Amhara region, where transmission is increasingly shaped by climate variability.
  • The Shift: Climate variables such as temperature and rainfall directly affect the lifecycle of the mosquito vector and the parasite, potentially shifting transmission to previously low-risk highland areas.
  • The Gap: Previous studies often relied on descriptive trends or short-term surveillance; there is a lack of localized, process-based modeling (like VECTRI) to simulate biological transmission processes in the Amhara region.

2. Methodology & Data Sources

This study assessed the spatio-temporal relationship between climate and malaria from 2013 to 2019 using a dynamical model.

  • Model Used: VECTRI (Vector-borne disease community model of ICTP, Trieste), a process-based model integrating climate, population density, and vector biology.
  • Study Area: Amhara Region, Ethiopia.
  • Climate Input Data:
    • ERA5: Reanalysis data for temperature and rainfall (consistently reported higher values).
    • CHIRPS: Satellite-blended rainfall data.
  • Validation Data: Malaria case surveillance data sourced from the Ethiopian Public Health Institute (EPHI).

3. Key Findings

A. Seasonal Dynamics and Lag Effects

  • Rainfall Patterns: The region follows a bimodal pattern, with the main rainfall season (Kiremt) occurring from June to September.
  • The Lag Effect: Biological lag effects resulted in Entomological Inoculation Rate (EIR) peaks occurring 1–2 months after peak rainfall.
  • Peak Transmission: Malaria cases and EIR typically peaked in September, October, and November.

B. The Impact of Climate Anomalies

  • Temperature Influence: High temperatures during the Belg season (March–May) significantly influenced EIR patterns.
  • 2015 El Niño Event: The study highlighted that 2015, a year associated with El Niño, showed high malaria cases and EIR despite negative rainfall anomalies, suggesting that warmer temperatures drove the epidemic.
  • Drought Years: Conversely, 2017–2018 showed lower transmission due to drought conditions linked to La Niña.

C. Model Performance & Data Resolution

  • CHIRPS vs. ERA5: The VECTRI model effectively captured seasonal dynamics, particularly when driven by high-resolution CHIRPS data, which produced sharper peaks than ERA5.
  • Sensitivity: The model is sensitive to input data; CHIRPS-driven simulations consistently produced higher EIR values than ERA5.

D. Spatial Hotspots

  • High Risk Areas: Western highlands, such as the Awi Zone and South Gondar, were identified as hotspots due to sustained rainfall.
  • Low Risk Areas: Eastern lowlands (e.g., North Wollo) showed lower transmission intensity due to sporadic rainfall.

4. Implications for Policy & Practice

  • Early Warning Systems: The integration of climate-informed models like VECTRI into early warning systems is essential for anticipating outbreaks.
  • Targeted Interventions: Interventions (e.g., bed nets, spraying) should be timed to coincide with the identified post-rainfall transmission peaks (Sept–Nov).
  • Climate Adaptation: As climate change may extend transmission into previously low-risk highland areas, proactive surveillance is critical.

5. Recommendations

  1. Integrate Models: Incorporate real-time climate monitoring and predictive models like VECTRI into national malaria control programs.
  2. Enhance Data: Expand local meteorological data collection to validate satellite datasets.
  3. Cross-Sector Collaboration: Foster coordination between the EPHI and the National Meteorological Agency to operationalize climate-health linkages.

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