A framework for evaluating epidemic forecasts.

ABSTRACT:

Background

Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm.

Results

In this paper we present an evaluation f

SUBMITTER: Tabataba FS 

PROVIDER: S-EPMC5433189 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature