Abstract
The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic’s second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box–Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020–October 13, 2020 were extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities’ decision-making, especially in the short-term.
Highlights
The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that officially emerged in Wuhan, China, in December 2019
For patients hospitalized with mild symptoms (Table 2), the optimal single models were the seasonal autoregressive moving average (ARIMA) (1,2,3) (0,0,1)7, exponential smoothing (ETS) (A,additive damped (Ad),N),16 neural network autoregression (NNAR) (7,1,4)7, and TBATS (0.428, {2,2}, 1, {< 7,2 >})
For patients hospitalized in the intensive care units (ICU) (Table 2), the optimal single models were the seasonal ARIMA (1,2,2) (0,0,1)7, ETS (A,A,N),18 NNAR (6,1,4)7, and (T)BATS (0.427,{0,0},1,−)
Summary
The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that officially emerged in Wuhan, China, in December 2019. As of January 21, 2021, it had affected 219 countries and territories with almost 100 million cases and over 2 million deaths [77]. At the time of writing, the countries most significantly affected include both advanced and developing countries, such as Brazil, France, India, Italy, Russia, Spain, the UK, and the US. From October to December 2020, several European countries, including Italy, saw a worrisome surge of COVID-19 infections. Was the first European country to be severely impacted by COVID-19, and it remained one of the main epicenters of the pandemic for approximately 2 months, i.e., from mid-February 2020 to mid-April 2020. As of January 21, 2021, Italy has suffered 84,202 deaths and 2,428,221 cases
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