Socioeconomic, demographic and environmental factors may inform malaria intervention prioritization in urban Nigeria 
Table of Contents
Nigeria is one of three countries projected to have the largest absolute increase in the size of its urban population and this could intensify malaria transmission in cities. Accelerated urban population growth is out-pacing the availability of affordable housing and basic services and resulting in living conditions that foster vector breeding and heterogeneous malaria transmission. Understanding community determinants of malaria transmission in urban areas informs the targeting of interventions to population at greatest risk. This repository contains data and analysis scripts for the analysis and results presented in the manuscript entitled "Socioeconomic, demographic and environmental factors inform intervention prioritization in urban Nigeria".
All data is extracted and analyzed using R
Install R then RStudio an integrated development environment for R and Python, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
To replicate the findings, we extract data from various sources, namely, Demographic Health surveys for the years 2010, 2015, 2018, and various rasters. Note that this proccess takes considerable amount of time. However you can skip the step and procced to Descriptive analysis step and run a pre extracted and cleaned CSV.
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00_data_extraction. Scripts in this folder support extraction of study data and computation of data summaries for survey clusters. Scripts in 00_era5_temperature_precipitation_download_raster_generation folder are used to download and transform era5 netcdf temperature and rainfall data into a usable format. The 01_data_extractor script extracts malaria test positivity data and data for covariates and stores it several CSV files. All custom functions used are also provided within this folder.
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01_file_cleaner.R This script cleans and merges the various extracted scripts into two CSVs.
- 02_descriptive statistics.R This script loads the cleaned data and conducts descriptive analysis for both the main manuscript and the suplement publication. All custom functions used are provided here
- 03_glm_modelling.R This script loads pre-cleaned study data and conducts GLMM modeling analysis. Custom functions used are provided here
For inquiries, contact Ifeoma Ozodiegwu, Research Assistant Professor, Northwestern University (NU). Email -[email protected] or Chilo Chiziba, Research Assistant @ NU Malaria Modeling Team