Neat Elite Research and Statistical Consultancy

Bongani Ncube

Statistical Experience

My statistical experience ranges from:

• modelling biodiversity of ecological species
• abundance and diversity of taxa of importance, to whole ecosystem functional trait analysis.
• data science and public health

The methods used have mostly been applied within the coding language {r}, with models being applied of varying complexity, in both frequentist and bayesian frameworks as well as Machine Learning algorithms, from one or two fixed effects to more complex mixed effects, hierarchical regression, General Additive, Random Forest, XGBoost, Neural Network classification models. Have Generally worked on:

• time to event models

• Generalised Additive models

• Generalised linear models

• Longitudinal data analysis

• Unsupervised machine learning

• Supervised machine learning

• Advanced Explanatory analysis

• Advanced Data Visualisation

• Time Series Forcasting

• zero-inflated/one-inflated/zero-altered/one-altered alternatives.

Consultancy Fieldwork Experience

I have been in fore front in Analyzing Eswatini Prevention of Mother-to-child Transmission Of HIV (PMTCT) Impact Measurement Survey ,2022

my main duties were to:

• Determine the overall national mother to child transmission rates of 6-8 weeks ,9,12,18 and 24 months posrpartum among HIV exposed infants by Region in Eswatini

• Determine the National population-based HIV-free survival from 6 weeks to 25 months

• identify risk factors associated with HIV transmission at 6-8 weeks ,9-,12-,18- and 24- months among HIV positive women and exposed children in selected health facilities in Eswatini

• conduct training manual writing and Quality Assurance

• data analysis and report writing

• development of M&E systems

• Lead review of the data management, analysis of health facility survey data

• contribute to the drafting of the overall Eswatini Health Service profiles assessment report.

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