EWS_epidemic

Code and data for the analysis of Proverbio, Daniele, et al. "Performance of early warning signals for disease re-emergence: A case study on COVID-19 data”. In: PLOS Computational Biology 18.3, e1009958. DOI: 10.1371/journal.pcbi. 1009958 (2022).

Navigate files

In EWS_epidemic:

• /src: code files (Jupyter noteboook and Matlab scripts)
  • R_t_EWS: estimates R(t) for all countries
  • Analysis_Reff: analyses the rate of approach to the transition
  • EWS_disease_emergence_figures: includes prevalence data and their analysis. Generates figures for Main Text
  • EWS_disease_emergence_figures_SupMat: completes analysis and generates figures for Supplementary Material
• /data: data for the evaluation of R(t), plus output of ARIMA detrending (files output*.csv)
• /csv: output of R_t_EWS, input for Analysis_Reff
• /R_T_plots: plots produced by R_t_EWS
• /Analysis_Reff_plots: plots produced by Analysis_Reff
• /plots_Main: figures for Main Text, produced by EWS_disease_emergence_figures
• /plots_SupMat: figures for Main Text, produced by EWS_disease_emergence_figures_SupMat

Requirements

The analysis was performed in MATLAB and Python. They require the Statistics Toolbox and the scipy library, respectively.

Credits

The code was developed by Daniele Proverbio and Françoise Kemp (ARIMA detrending).

In case of reuse, please cite the original manuscript: Proverbio, Daniele, et al. "Performance of early warning signals for disease emergence: a case study on COVID-19 data." medRxiv (2021).