| Summary: | The ongoing pandemic has resulted in the development of models dealing with the rate of virus spread and the modelling of mortality rates <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>μ</mi><mrow><mi>x</mi><mo>,</mo><mi>t</mi></mrow></msub></semantics></math></inline-formula>. A new method of modelling the mortality rates <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>μ</mi><mrow><mi>x</mi><mo>,</mo><mi>t</mi></mrow></msub></semantics></math></inline-formula> with different time intervals of higher and lower dispersion has been proposed. The modelling was based on the Milevski–Promislov class of stochastic mortality models with Markov switches, in which excitations are modelled by second-order polynomials of results from a linear non-Gaussian filter. In contrast to literature models where switches are deterministic, the Markov switches are proposed in this approach, which seems to be a new idea. The obtained results confirm that in the time intervals with a higher dispersion of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>μ</mi><mrow><mi>x</mi><mo>,</mo><mi>t</mi></mrow></msub></semantics></math></inline-formula>, the proposed method approximates the empirical data more accurately than the commonly used the Lee–Carter model.
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