Epidemiological forecasting facilitates scientifically sound solutions to upcoming theoretical and practical issues, in the development of public health management, in particular of infectious diseases. Aim: To critically analyze the most recent scientific advances in the biosocial nature and methodology of epidemiological forecasting to present a real-life example of pertussis, a disease with shifting epidemiology. Materials and methods: For the prediction of pertussis morbidity the

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... ive integrated moving average (ARIMA) the model was established by utilizing the method of time series analysis to construct a model of overall morbidity using Time series modeller in SPSS v.25. To model pertussis morbidity we obtained official data from the Ministry of Health and the National Center for Infectious and Parasitic Diseases, since the beginning of disease registration from 1903 until 2018. We also analyzed the shifting epidemiology of pertussis. Results: The proper identification procedures we applied indicated ARIMA (3,0,0) model to best fit our original time series of the annual whooping cough morbidity for the 1921-2018 period. The model predicts better morbidity in a one-step forecast. The incidence rate is expected to be stable at about 1.35 per 100,000 in the next three years, which is close to the 2016 level and lower than those in 2017-2018. Conclusion: The ARIMA (3,0,0) model in our study is an adequate tool for presenting the pertussis morbidity trend and is suitable to forecast near-future disease dynamics, with acceptable error tolerance.