Geospatial Epidemiology of Rift Valley Fever (2006–2007) in Kenya and Tanzania Using QGIS

This project investigated the spatial distribution and outbreak severity of Rift Valley Fever (RVF) across Kenya and Tanzania during the 2006–2007 epidemic. Leveraging Geographic Information Systems (GIS) and QGIS 3.44.0, the study visualized the geographic burden of disease using administrative boundary shapefiles, case data, and city coordinates to build a choropleth map of district-level RVF severity. The result was a robust, policy-informing spatial model for regional outbreak surveillance and intervention planning.

Objectives:
Analyze the distribution of human and livestock RVF cases across administrative zones

Highlight regional hotspots and cross-border transmission zones

Demonstrate how open-source GIS tools can support epidemic response and public health planning in resource-constrained settings

Data Science & GIS Workflow:
Data Preparation & Cleaning (Excel):

Converted coordinates from DMS to Decimal Degrees

Filtered data for Kenya and Tanzania only

Output saved as RVF_Cleaned.csv

Geospatial Modeling (QGIS):

Imported outbreak data and shapefiles (af_admin_1.shp, 10cities.shp)

Used Point-in-Polygon spatial join to calculate case counts per district

Symbolized severity using graduated color scales

Labeled capitals, added map legend, scale, and north arrow

Map Composition:

Produced choropleth map highlighting hotspot districts (Garissa, Wajir, Arusha, Kongwa, Kilosa)

Visualized both primary and secondary RVF activity zones

Included district-level severity scores and city locations for spatial context

Key Findings:
Severe RVF outbreaks clustered in northeastern Kenya and northern Tanzania, aligning with arid flood-prone zones

Environmental triggers (El Niño rainfall, flooding) catalyzed mosquito population booms

Pastoral mobility and informal livestock trade likely contributed to cross-border spread

RVF cases also appeared in non-endemic areas like Nairobi and Lamu, suggesting behavioral and ecological exposure risks

GIS clearly revealed spatially-organized hotspots, supporting the need for localized vector control and public health messaging

Strengths & Impacts of the Project:
Demonstrated GIS as a decision-making tool for epidemic preparedness

Visualized data in a way that is intuitive for both public health experts and policymakers

Provided a replicable, open-source spatial epidemiology workflow using QGIS for low-resource settings

Skills Demonstrated:
Data wrangling and geographic data cleaning in Excel

Multi-layered spatial data modeling in QGIS

Spatial joins, thematic mapping, and map interpretation

Integration of epidemiological concepts with data science and GIS

Application of public health informatics in real-world outbreak scenarios

Recommendations:
Invest in real-time GIS-linked surveillance for vector-borne diseases

Train health workers in spatial thinking and geodata literacy

Use severity maps to prioritize vaccine deployment, resource allocation, and targeted community education

Incorporate climate risk modeling into early warning systems