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

Research output: Contribution to journalArticleScientificpeer-review

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 languageEnglish
JournalInternational Journal of Forecasting
DOIs
Publication statusAccepted/In press - 2020

Keywords

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

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