Comparing the accuracy of several network-based COVID-19 prediction algorithms

Massimo A. Achterberg; Bastian Prasse; Long Ma; Stojan Trajanovski; Maksim Kitsak; Piet Van Mieghem

doi:10.1016/j.ijforecast.2020.10.001

Comparing the accuracy of several network-based COVID-19 prediction algorithms

Massimo A. Achterberg^*, Bastian Prasse, Long Ma, Stojan Trajanovski, Maksim Kitsak, Piet Van Mieghem

^*Corresponding author for this work

Network Architectures and Services

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Abstract

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.

Original language	English
Pages (from-to)	489-504
Number of pages	16
Journal	International Journal of Forecasting
Volume	38
Issue number	2
DOIs	https://doi.org/10.1016/j.ijforecast.2020.10.001
Publication status	Published - 2022

Keywords

Bayesian methods
Epidemiology
Forecast accuracy
Machine learning methods
Network inference
SIR model
Time series methods

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10.1016/j.ijforecast.2020.10.001

1-s2.0-S0169207020301552-mainFinal published version, 2.13 MBLicence: CC BY

Cite this

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AU - Trajanovski, Stojan

AU - Kitsak, Maksim

AU - Van Mieghem, Piet

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KW - Forecast accuracy

KW - Machine learning methods

KW - Network inference

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Comparing the accuracy of several network-based COVID-19 prediction algorithms

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