Software
Dr Olatunji Johnson
I’m a Senior Lecturer in Statistics (from 1 August 2026) in the Department of Mathematics at The University of Manchester. I was formerly a post-doctoral researcher and PhD student at CHICAS, Lancaster Medical School, Lancaster University (UK). My PhD was supervised by Prof. Peter Diggle, Prof. Emanuele Giorgi and Prof. Jo Knight.
My research develops novel Model-based Geostatistical (MBG) methods to provide valuable answers to many global public health questions and support informed decision-making in low-resource settings. I have worked and currently working on several global public health problems including COPD, malaria and neglected tropical diseases (NTDs) such as river blindness, soil-transmitted helminths, schistosomiasis, Trachoma, Loiasis.
News
- Jul 2026 — Promoted to Senior Lecturer in Statistics at The University of Manchester, effective 1 August 2026.
- Jul 2026 — SDALGCP2 (the successor to SDALGCP) and MBGapp are now on CRAN, and a new package, spLeakage, is available on GitHub. See the Software page.
- Mar 2026 — New preprint: Decoupling Distance and Networks — Hybrid Graph Attention–Geostatistical Methods for Spatio-temporal Risk Mapping. See Publications.
- Feb 2026 — New paper on disentangling spatial interference and spatial confounding biases in causal inference.
- Jan 2026 — Delivered the Advanced Geostatistical Modelling workshop at FUTA, Nigeria. Materials here.
- 2025 — Released ESPENAPI (NTD data access) and variogramApp (now hosted online). See the Software page.
Contact
Email: olatunji.johnson@manchester.ac.uk
ORCID: https://orcid.org/0000-0002-4080-0999
Google Scholar: https://scholar.google.com/citations?user=gn9CZVYAAAAJ&hl=en
Research interests
My research interests lie in the interface of statistics, epidemiology and health informatics with a particular interest in spatial statistics, public health and environmental epidemiology. I am interested in developing novel model-based geostatistical methods for tropical disease mapping.
My methodological themes include spatial and spatio-temporal statistics; joint modelling of multiple outcomes; geostatistical methods for spatial misalignment and Machine Learning.
My application themes include real-time health surveillance; tropical disease mapping; environmental epidemiology.
PhD Opportunities
I welcome enquiries from motivated students interested in pursuing a PhD in spatial statistics and its applications to global public health. Possible research directions include:
Mixture models for spatially clustered disease data — developing Bayesian mixture approaches to detect and characterise spatial clustering in geo-referenced health surveys, with applications to NTD mapping.
Multivariate spatio-temporal modelling — extending geostatistical frameworks to jointly model multiple correlated outcomes (e.g. co-endemic diseases) across space and time, including scalable inference methods for large datasets.
Hybrid geostatistical and machine learning methods — investigating principled ways to combine the predictive power of machine learning with the uncertainty quantification and interpretability of model-based geostatistics for health risk mapping.
Geostatistical methods for spatially misaligned data — addressing the challenge of combining data collected at incompatible spatial scales (e.g. individual surveys, areal counts, and remote-sensing covariates) within a unified inferential framework.
Prospective students are encouraged to contact me with a CV and a brief statement of their research interests. Funding opportunities (including EPSRC and university scholarships) are available for strong candidates. Please email olatunji.johnson@manchester.ac.uk to discuss.
Current projects
- Climate and Health — Causal Inference methods for estimating the indirect effect of climate change on health.
- Hybrid Machine learning and Geostatistical models — Investigating how best to combine these approaches.
- COVID-19 - Developing spatio-temporal models and mathematical models for COVID-19.
Software
I develop open-source R packages and Shiny applications for spatial statistics and global health, including SDALGCP2, ESPENAPI, MBGapp, variogramApp, and spLeakage. See the Software page for the full list with documentation and live demos.
I develop open-source software that makes model-based geostatistics and spatial epidemiology methods accessible to researchers, students, and public-health practitioners. Everything below is free and open source.
R packages
SDALGCP2
The successor to SDALGCP, now on CRAN. An R package fitting a spatially discrete approximation to the log-Gaussian Cox process for spatially aggregated disease count data, estimated by Monte Carlo Maximum Likelihood.
install.packages("SDALGCP2")spLeakage
An R package to detect and quantify spatial information leakage in predictive modelling.
remotes::install_github("olatunjijohnson/spLeakage")ESPENAPI
An R package for downloading Neglected Tropical Disease (NTD) data directly from the WHO ESPEN portal API and the WHO Global Health Observatory, returning tidy data frames ready for analysis.
remotes::install_github("olatunjijohnson/ESPENAPI")MBGapp
An R package and Shiny application for teaching model-based geostatistics, walking learners through the full workflow: exploration → variogram → model fitting → prediction. Published in PLOS ONE (2021). Now on CRAN.
install.packages("MBGapp")variogramApp
An R package and Shiny application for interactively exploring variograms and Gaussian random fields — a hands-on teaching tool for understanding spatial correlation across twelve covariance models.
remotes::install_github("olatunjijohnson/variogramApp")Shiny applications
MBGapp
Interactive model-based geostatistics for population-health scientists, using a Loa loa case study. Runs entirely in the browser — no R installation needed.
variogramApp
Explore how covariance-model parameters shape the variogram and simulate Gaussian random fields in one or two dimensions. Ideal for classroom use — share the URL or a QR code.
LEBLiverpool
A Shiny application visualising Life Expectancy at Birth (LEB) across Liverpool, UK, illustrating small-area health inequalities.
All code is released under open-source licences. Contributions, bug reports, and feature requests are welcome via the GitHub repositories above.