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

Journal article (2022)

Authors

M.A. Achterberg Network Architectures and Services - EEMCS
B. Prasse Network Architectures and Services - EEMCS
L. Ma Network Architectures and Services - EEMCS
S. Trajanovski Microsoft
M.A. Kitsak Network Architectures and Services - EEMCS
P.F.A. Van Mieghem Network Architectures and Services - EEMCS
Research Group
Network Architectures and Services (EEMCS) (TU Delft)
Copyright: © 2022 M.A. Achterberg, B. Prasse, L. Ma, S. Trajanovski, M.A. Kitsak, P.F.A. Van Mieghem
DOI
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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
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.