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

Achterberg, M.A.; Prasse, B.; Ma, L.; Trajanovski, S.; Kitsak, M.A.; Van Mieghem, P.F.A.

doi:10.1016/j.ijforecast.2020.10.001

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

Journal article (2022)

Authors

M.A. Achterberg Network Architectures and Services -

B. Prasse Network Architectures and Services -

L. Ma Network Architectures and Services -

S. Trajanovski Microsoft

M.A. Kitsak Network Architectures and Services -

P.F.A. Van Mieghem Network Architectures and Services -

Research Group

Network Architectures and Services () (TU Delft)

DOI: https://doi.org/10.1016/j.ijforecast.2020.10.001

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

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More Info

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Published Date

2022

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Network Architectures and Services

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.

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