Mortality Forecast: Local or Global?

2015 ◽  
Author(s):  
Han Li ◽  
Colin O'Hare
Keyword(s):  
Mortality Forecast

Optimal Values for Calculation of Premium in Life Insurance

2013 ◽  
Vol 2 (3) ◽  
pp. 68-85
Author(s):  
Cvetko Andreeski
Keyword(s):  
Developing Countries ◽  
Life Insurance ◽  
Data Sets ◽  
Mortality Trend ◽  
Republic Of Macedonia ◽  
The Future ◽  
Optimal Values ◽  
Mortality Forecast ◽  
The Republic

Life insurance is very challenging sector in developing countries. Life insurance makes contribute at the investments in every country, so the more developed life insurance, more investments one should expect. One of the main aspects in calculation of risk in life insurance is using updated tables of mortality and forecast of the future values of mortality. There are many functions and models for mortality forecast calculation. Lee-Carter and Azbel Model for mortality trend calculation are used in this paper. In order to evaluate the results, data sets with the mortality in the Republic of Macedonia are used.

scholarly journals ARMA Models for Mortality Forecast

2016 ◽  
Vol 55 (1) ◽  
pp. 31-44
Author(s):  
Natalja Šiškina ◽  
Jonas Šiaulys
Keyword(s):  
Mortality Rate ◽  
Longevity Risk ◽  
Arma Models ◽  
Men And Women ◽  
Rate Distribution ◽  
Baltic Countries ◽  
Mortality Forecast

In the last several decades, many countries have been paying a lot of attention to mortality forecastingbecause of high longevity risk. The purpose of this paper is to analyze mortality characteristics of Baltic countries andmake predictions using ARMA models. Research shoved that mortality rate distribution is almost the same in Lithuania, Latvia and Estonia and all of them represent longevity trends. It means that men and women, children and adults have thesame mortality structure in all Baltic countries and live longer than before.

scholarly journals Post-hospital Mortality Forecast in Young and Middle-Aged Patients with Myocardial Infarction

Doctor Ru ◽  
2020 ◽  
Vol 19 (5) ◽  
pp. 24-29
Author(s):  
E.A. Shishkina ◽  
◽  
O.V. Khlynova ◽  
A.B. Cheremnykh ◽  
◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Hospital Mortality ◽  
Middle Aged ◽  
Aged Patients ◽  
Mortality Forecast

scholarly journals From predictions to prescriptions: A data-driven response to COVID-19

2020 ◽  
Author(s):  
Dimitris Bertsimas ◽  
Leonard Boussioux ◽  
Ryan Cory-Wright ◽  
Arthur Delarue ◽  
Vasileios Digalakis ◽  
...  
Keyword(s):  
Clinical Studies ◽  
Care Management ◽  
Healthcare Providers ◽  
Data Driven ◽  
Policy Makers ◽  
Social Distancing ◽  
Comprehensive Data ◽  
Data Driven Approach ◽  
Mortality Forecast ◽  
Economic Price

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic’s spread and inform social distancing policies. Fourth, we propose an optimization model to reallocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and equitable vaccine distribution planning at a major pharmaceutical company, and have been integrated into the US Center for Disease Control’s pandemic forecast.Significance StatementIn the midst of the COVID-19 pandemic, healthcare providers and policy makers are wrestling with unprecedented challenges. How to treat COVID-19 patients with equipment shortages? How to allocate resources to combat the disease? How to plan for the next stages of the pandemic? We present a data-driven approach to tackle these challenges. We gather comprehensive data from various sources, including clinical studies, electronic medical records, and census reports. We develop algorithms to understand the disease, predict its mortality, forecast its spread, inform social distancing policies, and re-distribute critical equipment. These algorithms provide decision support tools that have been deployed on our publicly available website, and are actively used by hospitals, companies, and policy makers around the globe.

scholarly journals Mortality forecast from gastroduodenal ulcer disease for different gender and age population groups in Ukraine.

2016 ◽  
Vol 21 (1) ◽  
pp. 134-139
Author(s):  
I. D. Duzhiy ◽  
S. O. Muntyan ◽  
V. Yu. Dubnitskiy ◽  
S. V. Kharchenko ◽  
V. A. Smianov
Keyword(s):  
Gastroduodenal Ulcer ◽  
Population Groups ◽  
Ulcer Disease ◽  
Gender And Age ◽  
Mortality Forecast

Using Interest Rate Models to Improve Mortality Forecast

2017 ◽  
Author(s):  
Giovanna Apicella ◽  
Michel M. Dacorogna ◽  
Emilia Di Lorenzo ◽  
Marilena Sibillo
Keyword(s):  
Interest Rate ◽  
Interest Rate Models ◽  
Mortality Forecast

scholarly journals International COVID-19 mortality forecast visualization: covidcompare.io

JAMIA Open ◽  
2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Samir Akre ◽  
Patrick Y Liu ◽  
Joseph R Friedman ◽  
Alex A T Bui
Keyword(s):  
Public Health ◽  
Web Tool ◽  
Mortality Forecasting ◽  
Forecasting Models ◽  
Public Health Professionals ◽  
System Usability ◽  
Public Health Officials ◽  
Mortality Forecast ◽  
Mortality Forecasts ◽  
Insight Into

Abstract COVID-19 mortality forecasting models provide critical information about the trajectory of the pandemic, which is used by policymakers and public health officials to guide decision-making. However, thousands of published COVID-19 mortality forecasts now exist, many with their own unique methods, assumptions, format, and visualization. As a result, it is difficult to compare models and understand under which circumstances a model performs best. Here, we describe the construction and usability of covidcompare.io, a web tool built to compare numerous forecasts and offer insight into how each has performed over the course of the pandemic. From its launch in December 2020 to June 2021, we have seen 4600 unique visitors from 85 countries. A study conducted with public health professionals showed high usability overall as formally assessed using a Post-Study System Usability Questionnaire. We find that covidcompare.io is an impactful tool for the comparison of international COVID-19 mortality forecasting models.

scholarly journals A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST

2018 ◽  
Vol 48 (02) ◽  
pp. 481-508 ◽  
Author(s):  
Donatien Hainaut
Keyword(s):  
Neural Network ◽  
Term Structure ◽  
Predictive Power ◽  
Principal Component ◽  
Mortality Rates ◽  
Second Step ◽  
Inverse Transformation ◽  
Latent Factors ◽  
Neural Network Approach ◽  
Mortality Forecast

AbstractThis article proposes a neural-network approach to predict and simulate human mortality rates. This semi-parametric model is capable to detect and duplicate non-linearities observed in the evolution of log-forces of mortality. The method proceeds in two steps. During the first stage, a neural-network-based generalization of the principal component analysis summarizes the information carried by the surface of log-mortality rates in a small number of latent factors. In the second step, these latent factors are forecast with an econometric model. The term structure of log-forces of mortality is next reconstructed by an inverse transformation. The neural analyzer is adjusted to French, UK and US mortality rates, over the period 1946–2000 and validated with data from 2001 to 2014. Numerical experiments reveal that the neural approach has an excellent predictive power, compared to the Lee–Carter model with and without cohort effects.

scholarly journals MORTALITY FORECAST IN RUSSIA BASED ON MONITORING MAJOR SOCIAL DETERMINANTS

2020 ◽  
Vol 66 (6) ◽  
pp. 6-6
Author(s):  
A.E. Ivanova ◽  
Keyword(s):  
Life Expectancy ◽  
Social Determinants ◽  
Living Conditions ◽  
Self Assessment ◽  
Extensive Discussion ◽  
Significant Drop ◽  
Noticeable Increase ◽  
Current Trends ◽  
Exogenous Factors ◽  
Mortality Forecast

The prospect for increasing life expectancy in Russia is far from being disputed by experts, however the growth rate, its sustainability and achievability of the target indicators trigger extensive discussion in the scientific community. The purpose of the study is to develop a Russian mortality forecast until 2050 based on the hypotheses about effective monitoring over its major social determinants. Material and methods. Based on the material analysis of the Comprehensive monitoring over living conditions of the population, the author has assessed relevance of the National project measures in relation to the determinants of public health through self-assessment of living conditions and lifestyle. Two forecast scenarios have been developed. Results. In line with the "based on the current trends" scenario, life expectancy in Russia by 2050 may add up to 85.4 years in males and 87.7 years in females, mainly due to a significant drop in mortality in working ages and a shift of the beginning of the noticeable increase in mortality towards people aged over 70. According to the “taking into account additional policy measures" scenario, the male life expectancy can equal to 91.3 years and 93.9 years in females due to gaining almost a total control over mainly exogenous factors of mortality and reducing mortality to the minimum up to 75-80 years.

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