scholarly journals Calendar Effect and In-Sample Forecasting Applied to Mesothelioma Mortality Data

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2260
Author(s):  
Alex Isakson ◽  
Simone Krummaker ◽  
María Dolores Martínez-Miranda ◽  
Ben Rickayzen
Keyword(s):  
Great Britain ◽  
Health And Safety ◽  
Insurance Companies ◽  
Mortality Data ◽  
Mortality Forecasting ◽  
Ladder Model ◽  
Chain Ladder ◽  
Continuous Chain

In this paper, we apply and further illustrate a recently developed extended continuous chain ladder model to forecast mesothelioma deaths. Making such a forecast has always been a challenge for insurance companies as exposure is difficult or impossible to measure, and the latency of the disease usually lasts several decades. While we compare three approaches to this problem, we show that the extended continuous chain ladder model is a promising benchmark candidate for asbestosis mortality forecasting due to its flexible and simple forecasting strategy. Furthermore, we demonstrate how the model can be used to provide an update for the forecast of the number of deaths due to mesothelioma in Great Britain using in recent Health and Safety Executive (HSE) data.

scholarly journals Identification of the age-period-cohort model and the extended chain-ladder model

Biometrika ◽  
2008 ◽  
Vol 95 (4) ◽  
pp. 979-986 ◽  
Author(s):  
D. Kuang ◽  
B. Nielsen ◽  
J. P. Nielsen
Keyword(s):  
Cohort Model ◽  
Ladder Model ◽  
Extended Chain ◽  
Chain Ladder

scholarly journals Forecasting with the age-period-cohort model and the extended chain-ladder model

Biometrika ◽  
2008 ◽  
Vol 95 (4) ◽  
pp. 987-991 ◽  
Author(s):  
D. Kuang ◽  
B. Nielsen ◽  
J. P. Nielsen
Keyword(s):  
Cohort Model ◽  
Ladder Model ◽  
Extended Chain ◽  
Chain Ladder

scholarly journals Forecast of the COVID-19 trend in India: a simple modelling approach

2021 ◽  
Author(s):  
Haitao Song ◽  
Guihong Fan ◽  
Shi Zhao ◽  
Huichen Li ◽  
Qihua Huang ◽  
...  
Keyword(s):  
Model Simulation ◽  
Current Trend ◽  
Health And Safety ◽  
Wave Pattern ◽  
Mortality Data ◽  
Local Elections ◽  
Sporting Events ◽  
Epidemic Size ◽  
New Variant ◽  
Short Period

Abstract By February 2021, the overall impact of the COVID-19 pandemic in India had been relatively mild in terms of total reported cases and deaths. Surprisingly, the second wave in early April becomes devastating and attracts worldwide attention. On April 30, 2021, India became the first country reporting over 400,000 daily new cases. Multiple factors drove the rapid growth of the epidemic in India and caused a large number of deaths within a very short period. These factors include a new variant with increased transmissibility, a lack of preparations exists national wide, and health and safety precautions poorly implemented or enforced during festivals, sporting events, and state/local elections. Moreover, India's cases and deaths are vastly underreported due to poor infrastructure, and low testing rates. In this paper, we use the COVID-19 mortality data in India and a mathematical model to calculate the effective reproduction number and to model the wave pattern in India. We propose a new approach to forecast the epidemic size and peak timing in India with the aim to inform mitigation in India. Our model simulation matched the reported deaths accurately and is reasonably close to results of serological study. We forecast that the IAR could reach 43% by June 13, 2021 under the current trend, which means 532,629 reported deaths with a 95% CI (552,445, 513,194) ie., double the current total deaths. Our approach is readily applicable in other countries and with other type of data (e.g. excess deaths).

scholarly journals Mortality Forecasting: How Far Back Should We Look in Time?

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 22 ◽  
Author(s):  
Han Li ◽  
Colin O’Hare
Keyword(s):  
Predictive Power ◽  
Historical Data ◽  
Mortality Rates ◽  
Point Of View ◽  
Mortality Data ◽  
Male Mortality ◽  
Historical Information ◽  
Mortality Forecasting ◽  
Mortality Models ◽  
Forecasting Performance

Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns in age, time and cohort dimensions either in a deterministic fashion or a stochastic fashion. The second uses non-parametric smoothing techniques to model mortality and thus has no explicit constraints placed on the model. We argue that from a forecasting point of view, the main difference between the two types of models is whether they treat recent and historical information equally in the projection process. In this paper, we compare the forecasting performance of the two types of models using Great Britain male mortality data from 1950–2016. We also conduct a robustness test to see how sensitive the forecasts are to the changes in the length of historical data used to calibrate the models. The main conclusion from the study is that more recent information should be given more weight in the forecasting process as it has greater predictive power over historical information.

Pesticide incidents reported to the Health and Safety Executive 1989/90- 1991/92

1995 ◽  
Vol 14 (8) ◽  
pp. 630-633 ◽  
Author(s):  
JP Thompson ◽  
PB Casey ◽  
JA Vale
Keyword(s):  
Great Britain ◽  
Methyl Bromide ◽  
Private Property ◽  
Information Service ◽  
Health And Safety ◽  
Insufficient Data ◽  
Pesticide Poisoning ◽  
The Public ◽  
Occupational Setting ◽  
The Uk

1 Data concerning pesticide incidents investigated by the Field Operations Division (FOD) of the Health and Safety Executive (HSE) in Great Britain have been collated for the three year period April 1989 to March 1992. Over this period the HSE investigated 613 incidents concerning pes ticides : 338 related to general or environmental com plaints not involving human poisoning and 275 were sus pected poisoning incidents. 2 The two hundred and seventy-five suspected poisoning incidents were assessed by the Pesticide Incidents Appraisal Panel (PIAP) as 'confirmed', 'likely', 'unlikely', 'not confirmed', or that there were 'insufficient data' to make an assessment. Assessed data are unavailable for eight incidents reported in 1989. 3 Four hundred and eighteen members of the public were involved in 202 assessed incidents and 79 workers were exposed in 65 assessed incidents. Overall, 129 (48%) inci dents were assessed as 'confirmed' or 'likely', 121 (45%) as 'unlikely' or 'not confirmed' and in 17 (6%) there were 'insufficient data' to form a judgement. Incidents occur ring in an occupational setting were assessed as 'con firmed' or 'likely' more frequently (62%) than those involving members of the public (44%). 5 Thirty-six per cent of those involved in a 'confirmed' poisoning incident were working with a pesticide or were in close proximity to the operator; 41% were on private property adjacent to a field being sprayed and a further 23% involved those walking, cycling or jogging past a sprayed field. 6 These data underestimate the number of occupational pesticide poisoning incidents in Great Britain as incidents involving sheep dips are not considered by PIAP but are dealt with by a parallel scheme run by the Veterinary Medicines Directorate. In addition, few of the non-occupa tional incidents, for example those referred to one of the UK National Poisons Information Service Centres, are included in these data. 7 Sixty deaths from pesticide poisoning were recorded in England and Wales between 1989-1991, though the major ity of cases followed the deliberate ingestion of a pesticide rather than occupational exposure. Only one of these was reported to the HSE and that concerned the deliberate ingestion of mevinphos. A second death reported to the HSE involved a farmer who died some 11 months after he became unwell following fumigation of his glasshouse soil with methyl bromide by a contractor. It is unlikely that this death was related to exposure to methyl bromide. 8 Over the 3 year period of the study there was an increase in the number of prosecutions laid before the courts (Under the Control of Pesticides Regulations 1986 made under the Food and Environment Protection Act 1985), though the number of Enforcement Notices served decreased. In addition, the average total fine decreased for the incidents prosecuted successfully.

Chain ladder and maximum likelihood

1991 ◽  
Vol 118 (3) ◽  
pp. 489-499 ◽  
Author(s):  
R. J. Verrall
Keyword(s):  
Maximum Likelihood ◽  
Real Data ◽  
Ladder Model ◽  
Second Moments ◽  
Chain Ladder

ABSTRACTThis paper derives second moments of estimates of the parameters in the chain ladder model. Thus, the so-called link ratios, and proportions of ultimate claims for each development year are considered. This enables confidence statements about the chain ladder parameters to be made with statistical rigour. The methods are illustrated using 6 sets of real data taken from the DTI returns.

scholarly journals Identifying Structural Breaks in Stochastic Mortality Models

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Colin O’Hare ◽  
Youwei Li
Keyword(s):  
Life Expectancy ◽  
Structural Breaks ◽  
Structural Break ◽  
Mortality Rates ◽  
Insurance Companies ◽  
Mortality Data ◽  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Mortality Forecasts

In recent years, the issue of life expectancy has become of utmost importance to pension providers, insurance companies, and government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data to anticipate future life expectancy and hence quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age, and cohort and forecast these trends into the future by using standard statistical methods. These approaches rely on the assumption that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper, we carry out a comprehensive investigation of the presence or of structural breaks in a selection of leading mortality models. We find that structural breaks are present in the majority of cases. In particular, we find that allowing for structural break, where present, improves the forecast result significantly.

scholarly journals Modelling and Prediction of the Gross Mortality Rate in Ecuador

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Mónica Mite ◽  
Sandra Garcia-Bustos ◽  
Marcela Pincay ◽  
Ana Debón ◽  
Francisco Santoja
Keyword(s):  
Mortality Rate ◽  
Cohort Effect ◽  
Mortality Data ◽  
Arima Models ◽  
Men And Women ◽  
Mortality Forecasting ◽  
Basic Model ◽  
Adjusted Model ◽  
Age Range ◽  
Carter Model

This paper presents the results obtained from the modelling of the mortality data in Ecuador from 1990 to 2010, using the StMoMo library in the open source programming language R. This library was developed based on the Generalized Age-Period-Cohort Models (GAPC), among which is the Lee-Carter model, which has been widely applied in the actuarial area. The gross mortality rate of men and women in an age range of 1 to 85 years was modelled for the data of Ecuador, in the period 1990-2010. Of a total of eight models, two models have been selected because they present a good fit of the data for both genders. The first is the basic model of Lee-Carter and the second, the Plat model, which incorporates the cohort effect. A comparison was made with the two models to determine which one has a better forecast in a horizon of 20 years for specific ages. Both models show and predict the decrease in mortality in Ecuador of both genders, a decrease that is more pronounced, in general, for women at certain ages. In determining the uncertainty of the models, the bootstrap technique was used to define the confidence intervals of the adjusted model. The GAPC and ARIMA models were also compared; the former improve the mortality forecasting.

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