# Could Historical Mortality Data Predict Mortality Due to Unexpected Events?

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Construction of Mortality Models

#### 2.2. Selection of the Appropriate Mortality Model

#### 2.3. Selection of Mortality Data

## 3. Results

#### 3.1. Non Age-Related Random Risk Factor for the Whole Population (Parameter λ)

#### 3.2. Sensitivity Analysis of the Prediction Model

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**The evolution of collective mortality (λ) for the 22 EU countries in the past, present, and future, for both men (males, blue) and women (females, red), for the years from 1960 to 2045.

**Figure 6.**Approach to the projection of mortality (red) in the real (blue) data indicative for the countries of Greece, the Czech Republic, Estonia, France, Iceland and Sweden, for the years 2010–2017.

Range | Min | Max | Mean | Std. Deviation | ||
---|---|---|---|---|---|---|

1960 | 0.2700 | 1.0000 | 1.2700 | 1.1445 | 0.0116 | 0.0542 |

1970 | 0.2900 | 1.0000 | 1.2900 | 1.1418 | 0.0140 | 0.0655 |

1980 | 0.4900 | 1.0000 | 1.4900 | 1.1418 | 0.0273 | 0.1281 |

1990 | 0.4700 | 1.0100 | 1.4800 | 1.1414 | 0.0243 | 0.1141 |

2000 | 0.0000 | 1.1400 | 1.1400 | 1.1400 | 0.0000 | 0.0000 |

2010 | 0.0000 | 1.1400 | 1.1400 | 1.1400 | 0.0000 | 0.0000 |

2015 | 0.0000 | 1.1400 | 1.1400 | 1.1400 | 0.0000 | 0.0000 |

2020 | 0.0800 | 1.1400 | 1.2200 | 1.1450 | 0.0036 | 0.0171 |

2025 | 0.0000 | 1.1400 | 1.1400 | 1.1400 | 0.0000 | 0.0000 |

2030 | 0.0100 | 1.1400 | 1.1500 | 1.1405 | 0.0005 | 0.0021 |

2035 | 0.0000 | 1.1400 | 1.1400 | 1.1400 | 0.0000 | 0.0000 |

2040 | 0.0300 | 1.1100 | 1.1400 | 1.1350 | 0.0017 | 0.0080 |

2045 | 0.3600 | 1.0200 | 1.3800 | 1.1391 | 0.0159 | 0.0746 |

Range | Min | Max | Mean | Std. Deviation | ||
---|---|---|---|---|---|---|

1960 | 0.0743 | 1.0000 | 1.0743 | 1.0391 | 0.0047 | 0.0221 |

1970 | 0.0876 | 1.0000 | 1.0876 | 1.0348 | 0.0054 | 0.0254 |

1980 | 0.1114 | 1.0000 | 1.1114 | 1.0525 | 0.0098 | 0.0460 |

1990 | 0.2364 | 1.0140 | 1.2504 | 1.0937 | 0.0147 | 0.0690 |

2000 | 0.1309 | 1.0000 | 1.1309 | 1.0446 | 0.0077 | 0.0361 |

2010 | 0.1897 | 1.0000 | 1.1897 | 1.1041 | 0.0103 | 0.0482 |

2015 | 0.1984 | 1.0095 | 1.2079 | 1.1001 | 0.0102 | 0.0479 |

2020 | 0.1459 | 1.0478 | 1.1937 | 1.1104 | 0.0075 | 0.0353 |

2025 | 0.2032 | 1.0101 | 1.2132 | 1.1105 | 0.0110 | 0.0518 |

2030 | 0.2122 | 1.0163 | 1.2285 | 1.1247 | 0.0109 | 0.0512 |

2035 | 0.1941 | 1.0516 | 1.2457 | 1.1265 | 0.0101 | 0.0474 |

2040 | 0.2438 | 1.0182 | 1.2620 | 1.1242 | 0.0121 | 0.0565 |

2045 | 0.1493 | 1.0104 | 1.1597 | 1.1185 | 0.0066 | 0.0309 |

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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