In International journal of forecasting
Researchers from various scientific disciplines have attempted to forecast the spread of the 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 diverse set of algorithms that we evaluated, original NIPA performs best on 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.
Achterberg Massimo A, Prasse Bastian, Ma Long, Trajanovski Stojan, Kitsak Maksim, Van Mieghem Piet
Bayesian methods, Epidemiology, Forecast accuracy, Machine learning methods, Network inference, SIR model, Time series methods