  Coronavirus disease (COVID-19) is a severe ongoing novel pandemic that has
emerged in Wuhan, China, in December 2019. As of October 13, the outbreak has
spread rapidly across the world, affecting over 38 million people, and causing
over 1 million deaths. In this article, I analysed several time series
forecasting methods to predict the spread of COVID-19 second wave in Italy,
over the period after October 13, 2020. I used an autoregressive model (ARIMA),
an exponential smoothing state space model (ETS), a neural network
autoregression model (NNAR), and the following hybrid combinations of them:
ARIMA-ETS, ARIMA-NNAR, ETS-NNAR, and ARIMA-ETS-NNAR. About the data, I
forecasted the number of patients hospitalized with mild symptoms, and in
intensive care units (ICU). The data refer to the period February 21,
2020-October 13, 2020 and are extracted from the website of the Italian
Ministry of Health (www.salute.gov.it). The results show that i) the hybrid
models, except for ARIMA-ETS, are better at capturing the linear and non-linear
epidemic patterns, by outperforming the respective single models; and ii) the
number of COVID-19-related hospitalized with mild symptoms and in ICU will
rapidly increase in the next weeks, by reaching the peak in about 50-60 days,
i.e. in mid-December 2020, at least. To tackle the upcoming COVID-19 second
wave, on one hand, it is necessary to hire healthcare workers and implement
sufficient hospital facilities, protective equipment, and ordinary and
intensive care beds; and on the other hand, it may be useful to enhance social
distancing by improving public transport and adopting the double-shifts
schooling system, for example.

Applications;Machine Learning