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Article

Could Historical Mortality Data Predict Mortality Due to Unexpected Events?

1
Department of Geography, Harokopio University of Athens, El. Venizelou St., 70, Kallithea, 17671 Athens, Greece
2
Institute for Space Applications and Remote Sensing, National Observatory of Athens, BEYOND Centre of EO Research & Satellite Remote Sensing, 15236 Athens, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Stamatis Kalogirou
ISPRS Int. J. Geo-Inf. 2021, 10(5), 283; https://doi.org/10.3390/ijgi10050283
Received: 27 February 2021 / Revised: 25 April 2021 / Accepted: 26 April 2021 / Published: 29 April 2021
Research efforts focused on developing a better understanding of the evolution of mortality over time are considered to be of significant interest—not just to the demographers. Mortality can be expressed with different parameters through multiparametric prediction models. Based on the Beta Gompertz generalized Makeham (BGGM) distribution, this study aims to evaluate and map four of such parameters for 22 countries of the European Union, over the period 1960–2045. The BGGM probabilistic distribution is a multidimensional model, which can predict using the corresponding probabilistic distribution with the following parameters: infant mortality (parameter θ), population aging (parameter ξ), and individual and population mortality due to unexpected exogenous factors/events (parameters κ and λ, respectively). This work focuses on the random risk factor (λ) that can affect the entire population, regardless of age and gender, with increasing mortality depicting developments and trends, both temporally (past–present–future) and spatially (22 countries). Moreover, this study could help policymakers in the field of health provide solutions in terms of mortality. Mathematical models like BGGM can be used to achieve and highlight probable cyclical repetitions of sudden events (such as Covid-19) in different time series for different geographical areas. GIS context is used to map the spatial patterns of this estimated parameter as well as these variations during the examined period for both men and women. View Full-Text
Keywords: mortality; demography; BGGM probabilistic distribution; mapping; GIS; Covid-19 mortality; demography; BGGM probabilistic distribution; mapping; GIS; Covid-19
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MDPI and ACS Style

Andreopoulos, P.; Kalogeropoulos, K.; Tragaki, A.; Stathopoulos, N. Could Historical Mortality Data Predict Mortality Due to Unexpected Events? ISPRS Int. J. Geo-Inf. 2021, 10, 283. https://doi.org/10.3390/ijgi10050283

AMA Style

Andreopoulos P, Kalogeropoulos K, Tragaki A, Stathopoulos N. Could Historical Mortality Data Predict Mortality Due to Unexpected Events? ISPRS International Journal of Geo-Information. 2021; 10(5):283. https://doi.org/10.3390/ijgi10050283

Chicago/Turabian Style

Andreopoulos, Panagiotis, Kleomenis Kalogeropoulos, Alexandra Tragaki, and Nikolaos Stathopoulos. 2021. "Could Historical Mortality Data Predict Mortality Due to Unexpected Events?" ISPRS International Journal of Geo-Information 10, no. 5: 283. https://doi.org/10.3390/ijgi10050283

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