Senior Research Associate in Geospatial Statistical Methods

Lancaster University


Dr Olatunji Johnson is a senior research associate at CHICAS Research Group, Lancaster Medical School, Lancaster University, UK. He was formerly a PhD student at Lancaster University and supervised by Prof. Peter Diggle, Dr Emanuele Giorgi and Prof. Jo Knight.

His research interests lie in the interface of statistics, epidemiology and health informatics with a particular interest in spatial statistics and environmental epidemiology. His research focuses on the development of novel geospatial statistical methodology for analysing epidemiological data, currently working on Neglected Tropical Diseases (NTDs) in low resource countries. He is the author of SDALGCP R package.


  • Real-time Visualization and Prediction
  • Disease Mapping
  • Spatial and Spatio-Temporal Modelling
  • Neglected Tropical Diseases (NTDs)
  • Disease surveillance


  • PhD in Statistics and Epidemiology, 2017 - 2020

    Lancaster University, UK

  • MSc in Mathematical Sciences , 2015 - 2016

    African Institute for Mathematical Sciences, Tanzania

  • BTech in Statistics, 2009 - 2014

    Federal University of Technology, Akure, Nigeria










Senior Research Associate

Lancaster Medical School, Lancaster University

October 2019 – Present UK
Working on the development of geospatial statistical methods for Neglected Tropical Diseases (NTDs)

Graduate Teaching Assistant

Mathematics and Statistics Department, Lancaster University

October 2017 – July 2019 UK

Tutored the following courses:

  • Generalised Linear Mixed Model
  • Computational Mathematics
  • Statistical Inference

Graduate Teaching Assistant

Biomedical Life Sciences Department, Lancaster University

October 2017 – July 2019 UK
Tutored: Experimental Design and Data Analysis

Mathematics Tutor

Government Unity Secondary School

May 2015 – August 2015 Nigeria
Tutored advanced mathematics courses

Data Analyst

Power Holding Company of Nigeria

April 2013 – October 2013 Nigeria

Responsibilities include:

  • Produce daily report on sales
  • Predict Monthly target for the company
  • Evaluate the performance of the company towards the target


Connected Health Cities PhD Funding

Awarded PhD studentship funding to work on real-time visualisation and prediction of COPD emergency admission

Excellent Essay project Award

Outstanding Student Award

Master’s Full Scholarship

Awarded a full-funded scholarship to study for a master’s degree

Best Graduating Student

Recent Publications

Quickly discover relevant content by filtering publications.

Rethinking neglected tropical disease prevalence survey design and analysis: a geospatial paradigm

Current methods for the design and analysis of neglected tropical disease prevalence surveys largely rely on classical survey sampling …

A modelling framework for developing early warning systems of COPD emergency admissions

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the …

Elimination of STH morbidity in Zimbabwe: Results of 6 years of deworming intervention for school-age children

This paper reports the prevalence and intensity of soil-transmitted helminth (STH) infections measured in Zimbabwe before and after a …

Dealing with spatial misalignment to model the relationship between deprivation and life expectancy: a model-based geostatistical approach

Life expectancy at birth (LEB), one of the main indicators of human longevity, has often been used to characterise the health status of …

A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data

In this paper, we develop a computationally efficient discrete approximation to log-Gaussian Cox process (LGCP) models for the analysis …

Recent & Upcoming Talks

Statistical Modelling Approaches to Disease Mapping

In this talk, I will discuss statistical models used in disease mapping. Spatial statistics is classified into three categories …

A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data

n this paper, we develop a computationally efficient discrete approximation to log‐Gaussian Cox process (LGCP) models for the analysis …

Recent Posts

Multiple Y Axes Plot with Plotly

Introduction This post briefly describe how to produce a multiple axes plot in R using plotly. Happy reading!!! Generate the Data countM <-rpois(n = 26, lambda = 10) countF <-rpois(n = 26, lambda = 10) rateM <- countM/1000 rateF <- countF/1000 age <- LETTERS[seq( from = 1, to = 26 )] data <- data.

Spatial Probit Model Using Gaussian Random Field

Introduction Spatial probit models is very popular in spatial econometrics and the book of J. LeSage and Pace (2009) gives a very good overview. This is basically an extension of probit model when one is interested to adjust for both fixed and spatial random effect.

Fitting Geostatistical Model Using TensorFlow API from R

Introduction This tutorial simply estimate the parameter of a geostatistical model using the TensorFlow API from R. There are many tutorial and links online on how to use TensorFlow in R, see https://www.


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