Abstract
The paper examines the possibility of using an alternative approach to predicting statistical indicators of a new COVID-19 virus type epidemic. A systematic review of models for predicting epidemics of new infections in foreign and Russian literature is presented. The accuracy of the SIR model for the spring 2020 wave of COVID-19 epidemic forecast in Russia is analyzed. As an alternative to modeling the epidemic spread using the SIR model, a new CIR discrete stochastic model is proposed based on the balance of the epidemic indicators at the current and past time points. The new model describes the dynamics of the total number of cases (C), the total number of recoveries and deaths (R), and the number of active cases (I). The system parameters are the percentage increase in the C(t) value and the characteristic of the dynamic balance of the epidemiological process, first introduced in this paper. The principle of the dynamic balance of epidemiological process assumes that any process has the property of similarity between the value of the total number of cases in the past and the value of the total number of recoveries and deaths at present. To calculate the values of the dynamic balance characteristic, an integer linear programming problem is used. In general, the dynamic characteristic of the epidemiological process is not constant. An epidemiological process the dynamic characteristic of which is not constant is called non-stationary. To construct mid-term forecasts of indicators of the epidemiological process at intervals of stationarity of the epidemiological process, a special algorithm has been developed. The question of using this algorithm on the intervals of stationarity and non-stationarity is being examined. Examples of the CIR model application for making forecasts of the considered indicators for the epidemic in Russia in May-June 2020 are given.
Highlights
Coronavirus: Arima based time-series analysis to forecast near future. arXiv:2004.07859
Prediction of the COVID-19 epidemic trends based on SEIR and AI models
The paper examines the possibility of using an alternative approach to predicting statistical indicators of a new COVID-19 virus type epidemic
Summary
В пятом разделе обсуждаются результаты применения модели CIR и метода прецедентов (CBR – case based reasoning) для построения краткосрочных прогнозов динамики эпидемии COVID-19 в России в мае-июне 2020 года в реальном времени. Что хотя модели временных рядов и являются популярным инструментом прогнозирования, применение данного подхода для оценки распространения новых инфекций имеет свои ограничения. В работах [34, 35] описана новая модель CBRR (case-based rate reasoning) на базе данного подхода для прогнозирования будущих значений основных параметров эпидемии коронавируса в России, позволяющая строить краткосрочные прогнозы на основе аналогов динамики процентного прироста в других странах. В случае попыток прогнозирования вновь возникающих эпидемий, основной проблемой является отсутствие исторических данных, на основании которых можно было бы оценить значения входных параметров моделей. Выбор значений коэффициентов и при использовании этой модели для прогнозирования динамики эпидемии конкретного типа вируса осуществляется на основании имеющейся статистики прошлых периодов.
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