Bogoti90/MERS-CoV-Meta-analysis

MERS-CoV systematic review and Meta-analysis in Africa # MERS-CoV Meta-analysis MERS-CoV systematic review and Meta-analysis in Africa ## Overview This repository contains R code for analyzing MERS-CoV prevalence data in Africa, focusing on both dromedary camels and human cases. The analysis includes spatial distribution, temporal trends, and meta-analysis of the prevalence data. ## Prerequisites Required R packages: - tidyverse - openxlsx - spData - maps - maptools - rworldmap - sf - ggsn - janitor - esc - meta - metafor ## Data Requirements The analysis requires the following input files: - FAOSAT_camel_data.xlsx - MERS-Review Extraction tool_2024.xlsx ## Directory Structure ``` . ├── data/ │ ├── FAOSAT_camel_data.xlsx │ └── MERS-Review Extraction tool_2024.xlsx ├── figures/ ├── R/ │ └── MERS_review.Rmd └── README.md ``` ## Analysis Components 1. Spatial distribution of MERS-CoV studies in Africa 2. Temporal analysis of research publications 3. Seroprevalence analysis for both dromedaries and humans 4. Meta-analysis and forest plots 5. Meta-regression analysis ## Usage 1. Clone this repository 2. Place your data files in the `data/` directory 3. Open the R project and run the analysis.Rmd file 4. Output figures will be saved in the `figures/` directory ## Output Files The analysis generates several visualization outputs: - Number of studies Africa map - Temporal trends of MERS prevalance - Forest plots for meta-analysis - Publication bias funnel plots ## License This project is licensed under the CC0-1.0 license. ## Contact - **Brian M Ogoti** - Global Health | Virologist. - Contact: brian.ogoti@cema.africa - Web: The Center for Epidemiological Modelling and Analysis CEMA - Twitter/X: @diyobraz2 ---