Could Historical Mortality Data Predict Mortality Due to Unexpected Events?

Research efforts focused on developing a better understanding of the evolution mortality over time are considered to be significant interest—not just demographers. Mortality can expressed with different parameters through multiparametric prediction models. Based Beta Gompertz generalized Makeham (BGGM) distribution, this study aims evaluate and map four such for 22 countries European Union, period 1960–2045. The BGGM probabilistic distribution is multidimensional model, which predict using corresponding following parameters: infant (parameter θ), population aging ξ), individual due unexpected exogenous factors/events (parameters κ λ, respectively). This work focuses random risk factor (λ) that affect entire population, regardless age gender, increasing depicting developments trends, both temporally (past–present–future) spatially (22 countries). Moreover, could help policymakers in field health provide solutions terms mortality. Mathematical models like used achieve highlight probable cyclical repetitions sudden events (such as Covid-19) series geographical areas. GIS context spatial patterns estimated parameter well these variations during examined men women.