Code from: Multiple environmental conditions precede Ebola spillovers in central Africa

Published Feb 09, 2026 on Dryad. https://doi.org/10.5061/dryad.15dv41p9b

Data files

Feb 09, 2026 version files 40.79 KB

deforestation_panel_regression.R

9.22 KB
DTW_env_ts_20kmbuffer.R

27.33 KB
README.md

4.25 KB

Abstract

To assess potential environmental determinants of Zaire ebolavirus (EBOV) spillovers, we analyzed time series of vegetation health, rainfall, temperature, forest loss, and human population size surrounding Central African spillover locations from 1990 to 2022. We evaluated whether environmental conditions before spillover were atypical for each location by quantifying the similarities in environmental time series between spillover years and non-spillover years. This dataset includes the environmental and anthropogenic time series analyzed around EBOV spillover locations from 1990 to 2022. These data are all publicly available and were retrieved from data repositories in Google Earth Engine, The Climate Data Store, or Worldpop.

Dataset DOI: 10.5061/dryad.15dv41p9b

Description of the data and file structure

Code to accompany "Multiple Environmental Conditions Precede Ebola Spillovers in Central Africa" by Baranowski and Bharti, 2025, submitted to Biology Letters Special Feature on Disease Ecology.

All data were retrieved from publicly available sources and served as input files for the code. Due to licensing restrictions (because Dryad datasets are published under the CC0 license waiver), the data files required to run the code are not included. However, users can contact the corresponding author to request the necessary input files at: ktb5143@psu.edu (Kelsee Baranowski)

Files and variables

File: deforestation_panel_regression.R

Description: R code to perform panel regression on the hectares of forest area and forest loss by spillover location.

This code calculates forest loss around spillover locations, identifies years with significant spillover, and fits bias-corrected fixed-effects logistic models to predict spillover events. It then evaluates model performance using ROC curves and AUC, and reshapes predictions for plotting and analysis.

File: DTW_env_ts_20kmbuffer.R

Description: R code to perform Dynamic Time Warping on the environmental variables of interest by spillover location.

The code uses Dynamic Time Warping to compare environmental time series (rainfall, temperature, EVI) across spillover and non-spillover years, tests for significant differences with permutation tests, and visualizes alignment costs within locations.

Code/software

We used R and RStudio for the Dynamic Time Warping and Panel Regression Analyses.

R packages used for cleaning: plyr, dplyr, tidyr, stingr

R packages used for analysis: astsa, bife, IncDTW, pROC

R packages used for visualizations: ggplot2, ggpubr, dendextend

Access information

Data for this manuscript come from open-source data: MODIS MYD13Q1 16-day EVI from Google Earth Engine: https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13Q,1, summarized as field "spmean_EVI" in csv files in "environmental_time_series_DTW.zip"

ERA5-Land post-processed daily statistics from 1950 to present 2m-temperature from the Climate Data Store: https://cds.climate.copernicus.eu/datasets/derived-era5-land-daily-statistics?tab=overview summarized as field "spmean_temp" in csv files in "environmental_time_series_DTW.zip"

CHIRPS Daily Rainfall from R API code: https://data.chc.ucsb.edu/products/CHIRPS-2.,0/ summarized as field "spmean_rain" in csv files in "environmental_time_series_DTW.zip"

Landscan Annual Human Population Counts from Google Earth Engine: https://developers.google.com/earth-engine/datasets/catalog/projects_sat-io_open-datasets_ORNL_LANDSCAN_GLOBAL summarized in each buffer size in "after200spills_allbuffs_landscanfinaldf_20250810.csv"

WorldPop Annual Human Population Counts from Worldpop: https://hub.worldpop.org/geodata/listing?id=74 calibrated ratio of population in each buffer size in "after200spills_allbuffs_landscan_wpopr_finaldf_20250820.csv"

Copernicus Climate Change Initiative Annual Land Cover from the Climate Data Store: https://cds.climate.copernicus.eu/datasets/satellite-land-cover?tab=overview area of each lands summarized in each buffer in "allspills_allbuffs_CCIland_20250828.csv"

Resolve Ecoregions and Biomes from ArcGIS Living Atlas: https://hub.arcgis.com/datasets/esri::resolve-ecoregions-and-biomes/about used for visualization and grouping in Figure 2C and Figure S5.