Long-Term Forecasting of Influenza-Like Illnesses in Russia

Abstract

This paper compares the feasible methods for the long-term forecasting of the incidence rates of influenza-like illnesses (ILI) and acute respiratory infections (ARI), which is important for strategic management. A literature survey shows that the most appropriate techniques for long-term ILI & ARI morbidity projections are the following well-known statistical methods: simple averaging of observations, point-to-point linear estimates, Serfling-type regression models, autoregressive models such as autoregressive integrated moving average (ARIMA) models, and generalized exponential smoothing using the Holt-Winters approach. Using these methods and official data on the total number of ILI & ARI cases per week in 2000-2012 in Moscow, St. Petersburg, Novosibirsk, Yekaterinburg, Nizhny Novgorod and Yakutsk, we developed oneyear projections and evaluated their accuracy. Different methods yielded the best results, depending on the time series. Generally, it is preferable to use the Serfling model. The Serfling model forecasts almost matched the pointto-point linear estimates. In certain cases, ARIMA outperformed the Serfling model. Simple averaging can ensure a fairly good prediction when the ILI & ARI time series do not exhibit a trend. The results of exponential smoothing were poorer than those of other techniques.

A Computational Approach to Investigate Patterns of Acute Respiratory Illness Dynamics in the Regions with Distinct Seasonal Climate Transitions

“…Since the pioneering but now outdated work of Baroyan and Rvachev [1], [14] the data analysis and modeling of ARI dynamics in Russia have attracted only limited attention. The few works that can be named in this field are [8] and [13]. Nevertheless, the Russian ARI data, for our opinion, could be used effectively to verify the existing hypotheses concerning the ARI and ILI dynamics mechanism.…”