epysurv: Epidemiological Surveillance in Python

epysurv is a pythonic wrapper around the R surveillance package. It’s main goal is to predict disease outbreaks, right now focusing on univariate count time series. epsurv operates on pandas DataFrames and strives to implement a scikit-learn like API.

epysurv supports two problem formalizations of outbreak detection: time point classification and time series classification.

This documentation mainly explains the usage of epysurv and the ideas behind the problem formalizations. For more details about the algorithms have a look at the vignette of the R surveillance package or the literature references in the model docstrings.

This package was originally developed at the Robert Koch Institute in the Signale Project .

Contents

• Quickstart
  • Installation
  • Demo
• Outbreak Detection
  • Time Point Classification
  • Time Series Classification
  • Models
• User Guide
  • Data Format
  • Fitting
  • Prediction
  • Using Time Series Classification Models
• epysurv
  • epysurv package

Indices and tables

• Index

• Module Index

• Search Page