Spatial interpolation of acute malnutrition: a quest using probability proportional to the size of the population-based survey data

To effectively combat this public health issue, enhancing the ability to tell where acute malnutrition is of great concern over other areas is paramount. Possessing this information leverages the ability of programmers to prioritize limited resources - that are now getting even more shrunk amid the modern resource-constraint era.

This repository is an R implementation of a quest applying spatial interpolation modelling techniques. The quest aims to explore the usability of probability proportional to the size of the population-based survey sampling data to predict the prevalence of acute malnutrition from the observed sampling points to unobserved locations across a given survey area of interest.

Research (quest) question:

• Does spatial interpolation produce reliable (precise and accurate) estimates when applied to a small-scale PPS-based survey?

If yes:

• How comparable are the predicted estimates against the observed (from the original survey)

Study area

The analysis covered the Karamoja region in Uganda. This region has an area of 27,514 square kilometres and is subdivided into 9 districts (admin level 2), namely Abim, Amudat, Kaabong, Karenga, Kotido, Moroto, Nabilatuk, Nakapiripirit and Napak. The immediate lower administrative units after districts are counties (admin level 4). There are 64 counties in Karamoja.

The quest used derived from a Food Security and Livelihood Assessment (FSLA) conducted in February 2021. The FSLA was designed to represent each district independently. It employed the SMART methodology..

Repository Structure

• raw-data/: a data frame of the input data. This is encrypted.
• R/: some handy user-defined functions.
• scripts/: a set of R scripts. These are split into different files, based on the specific task that they execute.

The following workflow is recommended:

flowchart LR
A(Retrieve secret key for decryption)
B(Load project-specific functions.R)
C(Run read-in-data.R)
D(Run wrangle-aspatial-attributes.R)
E(Run data-quality-check.R)
F(Run wrangle-spatial-attributes.R)
G(Run krige-interpolate-wfhz.R OR krige-interpolate-wfhz-automap)

  A --> B --> C --> D --> E --> F --> G

Loading

The above flowchart can be implemented simply by running the scrip.R file found in the root directory.

Reproducibility information

The repository was created in R version 4.4.3. This project uses the {renv} framework to record R package dependencies and versions. Packages and versions used are recorded in renv.lock and code used to manage dependencies is in renv/ and other files in the root project directory. On starting an R session in the working directory, run renv::restore() to install R package dependencies.

Data encryption

This project uses {cyphr} to encrypt the raw data that lives in data-raw/ directory. In order to be able to access and decrypt the encrypted data, the user will need to have created their own personal SSH key and make a request to be added to the project. An easy-to-grasp guide on how to make a request will be found here

License

This repository is licensed under a GNU General Public License 3 (GPL-3).

Feedback

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Author

Tomás Zaba