The article highlights the problem of salmonellosis among the population of the Kharkov region, Ukraine. Three time series were used for calculations: a series of incidence rates for men, a series of incidence rates for women and a series of incidence rates for the general population, each of the series was an ordered set of monthly values from December 2015 to December 2018. It was revealed that the most effective tool for analyzing these statistical data is the use of the autoregressive moving average model (ARIMA). The following steps were used: identification and replacement of outliers, the use of smoothing and decomposition of the series. The developed model allows you to explicitly indicate the order of the model using the arima () function or automatically generate a set of optimal values (p, d, q) using the auto.arima () function. The validated model allows to calculate the predicted values of the incidence of salmonellosis for 50 days. In certain cases, models of exponential smoothing are able to give forecasts that are not inferior in accuracy to forecasts obtained using more complex models.
Keywords: autocorrelation graphs; autoregressive moving average model (ARIMA); exponential smoothing model; prognosis; salmonellosis incidence; time series; validated model.
© National Institute of Public Health – National Institute of Hygiene.