Biography

Olatunji Johnson is a Lecturer in Statistics in the department of Mathematics at Manchester University. He was formerly a post-doctoral researcher and PhD student at CHICAS Research Group, Lancaster Medical School, Lancaster University, UK. His PhD was supervised by Prof. Peter Diggle, Dr Emanuele Giorgi and Prof. Jo Knight. 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.

Interests

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

Education

  • 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

Skills

R

100%

Statistics

100%

Python

50%

Experience

 
 
 
 
 

Lecturer in Statistics

Manchester University

August 2021 – Present UK
Teaching statistics and conducting research in spatial statistics with applications in public health and environmental epidemiology
 
 
 
 
 

Senior Research Associate

Lancaster Medical School, Lancaster University

October 2019 – July 2021 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

Awards

Connected Health Cities PhD Funding

Lancaster Medical School Jan 2017 – Sep 2019
Awarded PhD studentship funding to work on real-time visualisation and prediction of COPD emergency admission

Excellent Essay project Award

African Institute for Mathematical Sciences Jul 2016

Outstanding Student Award

African Institute for Mathematical Sciences Jul 2016

Master’s Full Scholarship

African Institute for Mathematical Sciences Jul 2015
Awarded a full-funded scholarship to study for a master’s degree

Best Graduating Student

Federal University of Technology, Akure Dec 2014

Recent Publications

Quickly discover relevant content by filtering publications.

MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists

User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for …
Johnson, Olatunji, Fronterre, Claudio, Diggle, Peter, Amoah, Benjamin, Giorgi, Emanuele
PDF DOI

An Integrated District Mapping Strategy for Loiasis to Enable Safe Mass Treatment for Onchocerciasis in Gabon

The lack of a WHO-recommended strategy for onchocerciasis treatment with ivermectin in hypo-endemic areas co-endemic with loiasis is an …
Sylvie Ntsame Ella, Kisito Ogoussan, Katherine Gass, Lee Hundley, Peter J. Diggle, Olatunji Johnson, Marco Biamonte, Julienne Atsame
PDF DOI

Model-based geostatistical methods enable efficient design and analysis of prevalence surveys for soil-transmitted helminth infection and other neglected tropical diseases

Maps of the geographical variation in prevalence play an important role in large-scale programs for the control of neglected tropical …
Johnson, Olatunji, Fronterre, Claudio, Amoah, Benjamin, Montresor, Antonio, Giorgi, Emanuele, Midzi, Nicholas, Mutsaka-Makuvaza, Masceline Jenipher, Kargbo-Labor, Ibrahim, Hodges, Mary H, Zhang, Yaobi, Diggle, Peter
PDF DOI

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 …
Johnson, Olatunji, Gatheral, Tim, Knight, Jo, Giorgi, Emanuele
PDF Dataset DOI

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 …
Midzi, Nicholas, Montresor, Antonio, Mutsaka-Makuvaza, Masceline J, Fronterre, Claudio, Manangazira, Portia, Phiri, Isaac, Johnson, Olatunji, Mhlanga, Gibson, Diggle, Peter J
PDF Dataset DOI
See all publications

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 …
Jan 29, 2020 1:00 PM Source Themes HQ
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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 …
Jun 15, 2018 1:00 PM Source Themes HQ
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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.
May 1, 2020 2 min read

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.
Mar 14, 2019 11 min read

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.
Mar 2, 2019 4 min read

Contact

  • +44(0)1612755817
  • Room 1.129, Alan Turing Building, Department of Mathematics, University of Manchester, Manchester, Manchester, M13 9PL
  • Enter the Alan Turing Building and locate room 1.129
  • Monday 9:00 to 17:30
    Tuesday 9:00 to 17:30
    Wednesday 9:00 to 17:30
    Thursday 9:00 to 17:00
    Friday 09:00 to 7:00
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