Abstract
Time series analysis became one of the most investigated fields of knowledge during spreading of the COVID-19 around the world. The problem of modeling and forecasting infection cases of COVID-19, deaths, recoveries and other parameters is still urgent. Purpose of the study. Our article is devoted to investigation of classical statistical and neural network models that can be used for forecasting COVID-19 cases. Materials and methods. We discuss neural network model NNAR, compare it with linear and nonlinear models (BATS, TBATS, Holt's linear trend, ARIMA, classical epidemiological SIR model). In our article we discuss the Epemedic.Network algorithm using the R programming language. This algorithm takes the time series as input data and chooses the best model from SIR, statistical models and neural network model. The model selection criterion is the MAPE error. We consider the implementation of our algorithm for analysis of time series for COVID -19 spreading in Chelyabinsk region, and predicting the possible peak of the third wave using three possible scenarios. We mention that the considered algorithm can work for any time se-ries, not only for epidemiological ones. Results. The developed algorithm helped to identify the pat-tern of COVID -19 infection for Chelyabinsk region using the models realized as parts of the consi-dered algorithm. It should be noted that the considered models make it possible to form short-term forecasts with sufficient accuracy. We show that the increase in the number of neurons led to in-creasing accuracy, as there are other cases where the error is reduced in case of reducing the number of neurons, and this depends on COVID -19 infection spreading pattern. Conclusion. Hence, to get a very accurate forecast, we recommend re-running the algorithm weekly. For medium-range fore-casting, only the NNAR model can be used from among those considered but it also allows to get good forecasts only with horizon 1–2 weeks.
Highlights
COVID-19 is one of the most serious problems facing the entire world today
A lot of studies have been published on forecasting the number of cases of COVID-19, both worldwide and in individual states and regions
Given the similarity of the characteristics of the models in the United States and Italy, it was suggested in [3] that the corresponding forecasting tools can be applied to other countries fighting the COVID-19 pandemic, as well as to any pandemics that may arise in the future
Summary
We consider methods for predicting the spread of COVID-19 (cases of infection, death, and recovery) in the Chelyabinsk region, using time series analysis models and NNAR neural networks. A lot of studies have been published on forecasting the number of cases of COVID-19, both worldwide and in individual states and regions These studies used mainly the ARIMA model, Holt's linear trend model, and the SIR state transition model. Linear Holt model Adaptive exponential smoothing models are a fairly popular tool for predicting the spread of coronavirus infection These models served as a general tool for making time-series projections corresponding to the development of the epidemic in different countries [2, 9, 10]. The paper considers the possibility of automatic selection of parameters of the ARIMA model for time series corresponding to the same process occurring in different conditions. The network is usually trained multiple times using different random starting points, and the results are averaged
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