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

Author

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

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.

Suggested Citation

  • Achterberg, Massimo A. & Prasse, Bastian & Ma, Long & Trajanovski, Stojan & Kitsak, Maksim & Van Mieghem, Piet, 2022. "Comparing the accuracy of several network-based COVID-19 prediction algorithms," International Journal of Forecasting, Elsevier, vol. 38(2), pages 489-504.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:2:p:489-504
    DOI: 10.1016/j.ijforecast.2020.10.001
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
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