Scripts and data to generate figures of the paper "Proper pandemic preparedness requires an integrated cross-regional effort, the case of the ECLIPSE consortium in America: a narrative review".

Introduction

This repository contains two Python scripts for geospatial data visualization using GeoPandas and Matplotlib. The scripts visualize global data on dengue notification rates and H5N1 HPAI (Highly Pathogenic Avian Influenza) affected countries.

Prerequisites

• Python 3.x
• Required Python libraries:
  • pandas
  • geopandas
  • matplotlib
• Input data files:
  • Dengue notification rates: dengue_oct23_sep24.csv
  • H5N1 affected countries: H5N1_1oct2023-30sep2024_FAO_only_countries.csv
• World shapefile: 110m_cultural/ne_110m_admin_0_countries.shp

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Script 1: Dengue Notification Rates Visualization

Description

This script visualizes dengue notification rates across countries, creating a choropleth map with color gradients representing notification rates.

Features

• Displays global notification rates as a color-coded map.
• Highlights countries with missing data in light grey.
• Saves the map as a high-resolution PDF.

Instructions

1. Place the input files dengue_oct23_sep24.csv and the world shapefile in the specified directories.
2. Run the script.
3. The output map will be saved as dengue_rates_2024.pdf.

Key Code Highlights

• Data is merged using the country names in the shapefile (ADMIN) and CSV (Country).
• Missing values are visualized in light grey.
• The output plot is titled: "Dengue Notification Rates Around the World (2024)".

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Script 2: H5N1 Affected Countries Visualization

Description

This script visualizes countries affected by H5N1 HPAI (2023–2024) using a binary map.

Features

• Affected countries are marked in red.
• Non-affected or countries with no data are shown in light grey.
• Adds a custom legend differentiating affected and non-affected countries.
• Saves the map as a high-resolution PDF.

Instructions

1. Place the input files H5N1_1oct2023-30sep2024_FAO_only_countries.csv and the world shapefile in the specified directories.
2. Update the country_name_mapping dictionary to ensure country names match between the CSV and shapefile.
3. Run the script.
4. The output map will be saved as HPAI_map.pdf.

Key Code Highlights

• Uses a dictionary (country_name_mapping) to align mismatched country names between the input data and shapefile.
• Adds a custom legend for affected and non-affected countries.

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Common Issues

1. Country Name Mismatches: Ensure the Country column in the CSV matches the ADMIN field in the shapefile. Update the country_name_mapping as needed.
2. Shapefile Path: Ensure the path to the shapefile is correct.

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Output

• Dengue Notification Rates Map: dengue_rates_2024.pdf
• H5N1 Affected Countries Map: HPAI_map.pdf

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Repository Structure

• dengue_oct23_sep24.csv: Input CSV for dengue data
• H5N1_1oct2023-30sep2024_FAO_only_countries.csv: Input CSV for H5N1 data
• 110m_cultural/: Directory containing the world shapefile
  • ne_110m_admin_0_countries.shp: World shapefile
• dengue_visualization.py: Script for dengue visualization
• h5n1_visualization.py: Script for H5N1 visualization
• dengue_rates_2024.pdf: Output dengue map
• HPAI_map.pdf: Output H5N1 map

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Notes

For any questions or issues, please open an issue in this repository.