This report models weekly disease case counts in a specific district using Negative Binomial Regression, which is well-suited for over-dispersed count data. Environmental predictors include: Rainfall (mm/week) Temperature (°C — mean weekly) NDVI (Normalised Difference Vegetation Index — proxy for vegetation density and humidity)

This report presents the sub-county level distribution of Neglected Tropical Diseases (NTDs) across Kenya’s 47 counties and 290 sub-counties. Kenya bears a significant NTD burden, with diseases such as Schistosomiasis, Lymphatic Filariasis (LF), Soil-Transmitted Helminths (STH), Visceral Leishmaniasis (Kala-azar), Trachoma, and Onchocerciasis disproportionately affecting populations in coastal, western, and arid/semi-arid regions.

The MV Hondius, a Dutch expedition cruise ship, experienced an outbreak of Andes hantavirus (ANDV) beginning April 2026. The index cases — a Dutch couple — are believed to have been exposed during a birdwatching trip across Chile, Argentina, and Uruguay prior to boarding on 20 March 2026. As of 7 May 2026, WHO reports 8 identified cases (5 confirmed, 3 suspected) and 3 deaths, with the ship en route to Tenerife, Spain. Key epidemiological question: What does the incubation period distribution tell us about exposure timing and potential for further spread?

Generalized Additive Models (GAMs) extend linear models by allowing non-linear smooth functions of predictors while maintaining interpretability

This report demonstrates logistic regression using a diabetes dataset. The outcome is diabetes diagnosis (Yes/No), predicted by age, BMI, glucose level, physical activity, and family history.

This report analyses a simulated disease outbreak affecting 30 individuals across 4 communities. Network methods identify high-risk individuals, transmission pathways, and key structural features that drive epidemic spread.

Geospatial Mapping, Prediction & Interpretation Using Shapefiles

A Comprehensive Burden of Disease Study

This report presents a comprehensive geospatial analysis of flood risk in Nairobi County, Kenya. Using proximity-weighted risk modelling, multi-factor susceptibility analysis, and seasonal prediction techniques, the study identifies and quantifies flood-prone areas across the city. The findings confirm that informal settlements along river corridors — particularly Mathare, Kibera, and Mukuru — face the highest and most persistent flood risk, driven by a combination of hydrological, topographic, infrastructural, and socio-economic factors.