Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble

Abstract

The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

“…From the beginning of 2020, an increasing body of literature has employed various approaches to forecast the spread of the COVID-2019 outbreak [ 9 , 22 , 26 , 58 , 73 , 78 , 79 , 83 , 85 ]. The most frequently used were ARIMA models [ 3 , 8 , 14 , 62 ], ETS models [ 13 , 44 ], artificial neural network (ANN) models [ 55 , 75 ], TBATS models [ 68 , 71 ], models derived from the susceptible–infected–removed (SIR) basic approach [ 22 , 26 , 58 , 78 , 85 ], and hybrid models [ 15 , 29 , 68 , 69 ]. The implementation and comparison of these approaches—with the exception of mechanistic–statistical models (such as SIR)—represents the core of this paper.…”

Comparison of ARIMA, ETS, NNAR and Hybrid Models to Forecast the Second Wave of COVID-19 Hospitalizations in Italy

“…2020;Tuite et al, 2020;Wu et al, 2020;Xu et al, 2020;Zhao et al, 2020;Zhou et al, 2020). The most used are ARIMA (Alzahrani et al 2020;Benvenuto et al, 2020, Ceylan, 2020Perone, 2020a & b), ETS (Bhandary et al, 2020;Cao et al, 2020;Joseph et al, 2020), artificial neural network models (Melin et al, 2020;Wieczorek et al, 2020), models derived from the susceptible-infected-removed (SIR) basic approach (Fanelli and Piazza, 2020;Giordano et al, 2020;Nesteruk, 2020;Wu et al, 2020;Zhou et al, 2020), and hybrid models (Chakraborty and Ghosh, 2020;Hasan et al, 2020;Singh et al, 2020;Swaraj et al, 2020).…”