APEestim

REF	Parag and Donnelly (2020)
Docs	None
Github	Github
Last commit	Feb 12, 2021
Installation	None, this is code to augment EpiEstim

Description

Copied from the developer site

APEestim estimates the time-varying reproduction number on cases by date of infection (using a similar approach to that implemented in EpiEstim).

The quality of this estimate is highly dependent on the size of a smoothing window (k) that is employed. This code presents a method for optimally selecting k in a manner that balances reliable R(t) estimation with short-term forecasts of incidence. This method is based on the accumulated prediction error (APE) idea from information theory.

Methods

This package aims to improve upon the limitation of fixed sliding windows, specifically by optimizing the choice of the window size.

Assessment

Features
Ability to nowcast/forecast	No
Incorporates delay distributions	No
Estimates expected cases	No
Communicates uncertainty	Yes
Validation
Documentation of package methods	Yes
Documentation of package implementation	No

Sample Code

See this file in the Github repo.

Parag KV, Donnelly CA. 2020. Using information theory to optimise epidemic models for real-time prediction and estimation. PLoS Computational Biology 16:e1007990; doi:10.1371/journal.pcbi.1007990.