Comparing the accuracy of several network-based COVID-19 prediction algorithms
M.A. Achterberg (TU Delft - Network Architectures and Services)
Bastian Prasse (TU Delft - Network Architectures and Services)
Long Ma (TU Delft - Network Architectures and Services)
S. Trajanovski (Microsoft)
M.A. Kitsak (TU Delft - Network Architectures and Services)
PFA Van Mieghem (TU Delft - Network Architectures and Services)
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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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