**Description:**
This involves modeling under-5 malaria prevalence (U5MP) in Nigeria across the space to determine malaria hotspots for proper allocation of resources and policy making. Due to the sparse nature of available data in Nigeria, disease mapping borrows information across space. In this work, we will be using data from the Nigeria Demographic and Health Survey (NDHS).
Things to learn include data cleaning, exploratory data analysis and introduction to basic disease mapping. The analytical software to use are R-INLA (Integrated Nested Laplace Approximation) and SUMMER package which are open access for download.

**Prerequisite:**
1\. For this workshop\, you are expected to use a functional laptop with R installed\. You may download R and RStudio from their website\.
**NB: R and Rstudio installation link:**
R Download here : https://cran.r-project.org/bin/windows/base/
RSTUDIO Download here : https://posit.co/download/rstudio-desktop/#download

2\. Participants are encouraged to register with The DHS Program for data access and download Nigeria admin2 shapefiles ahead of the session\.
**Follow this procedure:**
Download the shapefile for the country, preferably Nigeria in this case, from https://gadm.org/download_country.html, selecting the country from the drop-down menu.

3\. You should have R\-INLA installed on your system\. This can be done by running the following code on your R console:
install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).
In case R-INLA fails to install, you may run the following code and then rerun the R-INLA code again:
options(timeout=6000)

4\. Interested participants need to register with https://dhsprogram\.com/ for permission to use their data\. The data we would be making use of is the Nigeria Malaria Indicator Survey \(MIS\) 2021\.
**Target:**
Public health practitioners, researchers and data scientists (modelers)
**Outcome for participants:**
Exposure to geospatial modeling of public health indicators and exploratory data analysis (EDA) technique

Kenneth Enwerem

Bilikisu Olatunji

Ifeoma Egbogah

Queeneth Onwuka 

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Muhammed Tahir Muhammed is a PhD student at the Federal University of Technology Akure under the Biostatistics and Geospatial Unit, department of Statistics.
His research interests span across Bayesian Hierarchical Spatiotemporal modeling with focus on Adaptive Gaussian Markov random field (AGMRF) in the estimation of Under-five Malaria prevalence under shock in Nigeria.
Currently a lecturer at Federal Polytechnic Idah Kogi State. His working experience cut across public health (Vaccine Preventable Diseases), data science and lecturing.

Muhammed Tahir Muhammed