Pneumonia Surveillance in Peru: Reproducible Analysis and Visualization Repository (2015-2022)
Description
This repository provides reproducible Python notebooks for analyzing and visualizing pneumonia surveillance data from Peru spanning 2015 to 2022. The code supports the findings reported in the manuscript: "Pneumonia incidence, hospitalizations and mortality before and during the COVID-19 pandemic in Peru: a nationwide observational study" (PLOS ONE, 2026). The repository enables full replication of national time-series trends and departmental-level choropleth maps for pneumonia incidence, hospitalizations, and mortality across pre-pandemic (2015–2019), pandemic (2020–2021), and post-pandemic (2022) periods. Contents: Maps.ipynb: Generates four-panel choropleth maps for children under 5 years and adults aged 60+ years, comparing 2015–2019, 2020, 2021, and 2022 with shared color scales. TimeSeries.ipynb: Creates weekly national time-series plots with 4-week moving averages for both age groups, including markers for COVID-19 onset (March 2020) and post-pandemic period (January 2022). datos/: Contains raw surveillance data from MINSA (datos_abiertos_vigilancia_iras_2000_2023.csv) and Peruvian departmental shapefiles for geospatial mapping. Key Features: National and departmental-level pneumonia trend analysis Age-stratified analysis comparing children under 5 and adults 60+ Temporal comparisons across pre-pandemic, pandemic, and post-pandemic periods Publication-quality figures generated with matplotlib and seaborn Fully reproducible Python workflow using pandas, geopandas, matplotlib, and seaborn Data Source: Peruvian public health surveillance system, CDC Peru, MINSA. Population denominators from INEI. Methods: Study period 2015–2022. Outcomes: incidence, hospitalization, and mortality rates per 100,000 inhabitants. Statistical analysis: annual incidence rates with 95% CI (Poisson) and incidence rate ratios comparing pandemic to pre-pandemic periods. Dependencies: pandas, geopandas, matplotlib, seaborn, numpy, shapely. Citation: Ramírez-Soto MC, Arroyo-Hernandez H, Bogado Machuca JV, Stalder DH, Schaerer CE. (2026). Peru Pneumonia Surveillance: Reproducible Analysis and Visualization Repository (2015-2022) [Data set]. Mendeley Data. Contact: Dr. Max Carlos Ramírez-Soto (maxcrs22@gmail.com) Acknowledgments: Universidad Tecnologica del Peru (Grant P-2025-LIM-26) . D.S. and C.E.S. acknowledge the support of FEEI-CONACYT-ARASY ESTR01-23. Funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.
Files
Steps to reproduce
1. Setup Environment bash pip install pandas geopandas matplotlib seaborn numpy shapely 2. Prepare Data Place these files in data/: datos_abiertos_vigilancia_iras_2000_2023.csv – Surveillance data DEPARTAMENTOS.shp – Peruvian departmental shapefiles 3. Generate Time-Series Plots bash jupyter notebook TimeSeries.ipynb Outputs: Weekly incidence, hospitalization, and mortality rates with 4-week moving averages. 4. Generate Choropleth Maps bash jupyter notebook Maps.ipynb Outputs: Four-panel maps for pre-pandemic (2015–2019), 2020, 2021, and 2022. 5. Output Figures Figures saved. 6. Verify Results Compare outputs with reported: Incidence rate ratios (IRRs) for 2020–2022 Age-stratified trends Regional heterogeneity across 25 departments Data Source: MINSA (link) Population: INEI annual regional estimates (2015–2022) Troubleshooting: Ensure all shapefile files (.shp, .dbf, .shx, .prj, .cpg) are present in datos/