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

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

International Journal of Forecasting, 2022, vol. 38, issue 2, 489-504

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.

Keywords: Epidemiology; Network inference; Forecast accuracy; Bayesian methods; SIR model; Time series methods; Machine learning methods (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:2:p:489-504

DOI: 10.1016/j.ijforecast.2020.10.001

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