A framework for evaluating epidemic forecasts

BMC Infectious Diseases
Farzaneh Sadat TabatabaMadhav V Marathe

Abstract

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. In this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by eva...Continue Reading

References

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Citations

Oct 13, 2018·PLoS Computational Biology·Prithwish ChakrabortyNaren Ramakrishnan
May 21, 2019·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Oliver Morgan
Nov 24, 2018·BMC Bioinformatics·Swapna ThorveMadhav V Marathe
Jun 4, 2020·Euro Surveillance : Bulletin Européen Sur Les Maladies Transmissibles = European Communicable Disease Bulletin·Paul P SchneiderJohn Paget
Dec 15, 2020·International Journal of Forecasting·Fotios PetropoulosNeophytos Stylianou
Nov 5, 2020·Journal of the Indian Institute of Science·Aniruddha AdigaAnil Vullikanti
May 6, 2021·PloS One·Gabriel RecchiaDavid Spiegelhalter
Jul 26, 2019··Naren RamakrishnanXinfeng Xu

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Software Mentioned

Evaluator
BlackArmstrong
features
Epi
MdAPE
sMAPE

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