Relevant bibliographies by topics / Mortality – Forecasting – Mathematical models

Academic literature on the topic 'Mortality – Forecasting – Mathematical models'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Mortality – Forecasting – Mathematical models"

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Lakman, I. A., A. A. Agapitov, L. F. Sadikova, O. V. Chernenko, S. V. Novikov, D. V. Popov, V. N. Pavlov, et al. "COVID‑19 mathematical forecasting in the Russian Federation." "Arterial’naya Gipertenziya" ("Arterial Hypertension") 26, no. 3 (June 25, 2020): 288–94. http://dx.doi.org/10.18705/1607-419x-2020-26-3-288-294.

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A new coronavirus infection (CVI) is a challenge to the medical system of the Russian Federation and requires precise flow forecasting to take the necessary measures on time. The article provides an overview of modern mathematical tools for predicting the course of CVI in the world. The created CVI forecasting project office allowed to determine the most effective analysis tools in the Russian Federation — the ARIMA, SIRD and Holt–Winters exponential smoothing models. Implementation of these models allows for prediction of short-term morbidity, mortality and survival of patients with an accuracy of 99 % both in the Russian Federation in general and in the regions. In addition, the distribution of CVI was characterized. Particularly, Moscow and Moscow region have the maximum spread of infection, and other regions are lagging behind in the dynamics of the incidence by 1–3 weeks. The obtained models allow us to predict the course of the disease in the regions successfully and take the necessary measures in a timely manner.
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Sursaieva, L. M., and V. M. Zhebel. "Predictors of development and forecasting models in the diagnosis of chronic heart failure against hypertension." Reports of Vinnytsia National Medical University 26, no. 1 (March 28, 2022): 101–7. http://dx.doi.org/10.31393/reports-vnmedical-2022-26(1)-19.

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Annotation. Chronic heart failure (CHF) is a recognized public health problem with significant morbidity and mortality. Complementing and improving existing ones, as well as finding new methods for diagnosing and predicting the development of CHF is a promising and important area of research. The probable association between plasma concentrations of cerebral (BNP) and vascular (CNP) types of natriuretic peptides, features of clinical status and single nucleotide polymorphism of the BNP gene encoding was studied. The aim of the study was to improve the prognosis of CHF in women of Podolsk region of Ukraine aged 40-65 with hypertension by determining the range of the most important predictors of risk of CHF and creating a prognostic mathematical model for early personalized diagnosis of CHF based on carriers of polymorphic variants of the BNP gene. The survey involved 180 women aged 40-65 living in the Podolsk region of Ukraine: 67 women in the control group without signs of cardiovascular disease, 62 women with uncomplicated EH and 51 women with EH complicated by CHF. All patients were examined using general clinical, instrumental and laboratory methods. Genotyping of the BNP gene was performed by polymerase chain reaction. Plasma concentrations of BNP and CNP were determined by plate solid phase enzyme-linked immunosorbent assay. Mathematical processing was performed on a personal computer using the standard statistical package Statistica 10.0. It is established that among the predictors of the risk of heart failure in women 40-65 years with uncomplicated EH the most important role is played by: the level of plasma concentrations of BNP and CNP, overweight, burdened heredity of EH, the onset of EH up to 40 years, blood pressure, LV EF <40% and the presence of left ventricular diastolic dysfunction type of relaxation disorders. The proposed prognostic mathematical model in the form of a scheme of equations in the future can be a convenient and fast method of early individualized diagnosis of CHF, available for use in online format.
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Sursaieva, L. M., and V. M. Zhebel. "Predictors of development and forecasting models in the diagnosis of chronic heart failure against hypertension." Reports of Vinnytsia National Medical University 26, no. 1 (March 28, 2022): 101–7. http://dx.doi.org/10.31393/reports-vnmedical-2022-26(1)-19.

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Annotation. Chronic heart failure (CHF) is a recognized public health problem with significant morbidity and mortality. Complementing and improving existing ones, as well as finding new methods for diagnosing and predicting the development of CHF is a promising and important area of research. The probable association between plasma concentrations of cerebral (BNP) and vascular (CNP) types of natriuretic peptides, features of clinical status and single nucleotide polymorphism of the BNP gene encoding was studied. The aim of the study was to improve the prognosis of CHF in women of Podolsk region of Ukraine aged 40-65 with hypertension by determining the range of the most important predictors of risk of CHF and creating a prognostic mathematical model for early personalized diagnosis of CHF based on carriers of polymorphic variants of the BNP gene. The survey involved 180 women aged 40-65 living in the Podolsk region of Ukraine: 67 women in the control group without signs of cardiovascular disease, 62 women with uncomplicated EH and 51 women with EH complicated by CHF. All patients were examined using general clinical, instrumental and laboratory methods. Genotyping of the BNP gene was performed by polymerase chain reaction. Plasma concentrations of BNP and CNP were determined by plate solid phase enzyme-linked immunosorbent assay. Mathematical processing was performed on a personal computer using the standard statistical package Statistica 10.0. It is established that among the predictors of the risk of heart failure in women 40-65 years with uncomplicated EH the most important role is played by: the level of plasma concentrations of BNP and CNP, overweight, burdened heredity of EH, the onset of EH up to 40 years, blood pressure, LV EF <40% and the presence of left ventricular diastolic dysfunction type of relaxation disorders. The proposed prognostic mathematical model in the form of a scheme of equations in the future can be a convenient and fast method of early individualized diagnosis of CHF, available for use in online format.
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Dovgalyuk, I. F., D. A. Kudlay, and A. A. Starshinova. "Tuberculosis prevalence in children in the Northwestern Federal District of Russia before and after COVID-19 pandemic: prognosis and epidemiological models." Pacific Medical Journal, no. 4 (January 17, 2023): 43–48. http://dx.doi.org/10.34215/1609-1175-2022-4-43-48.

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Aim. To assess changes in the epidemic indicators of tuberculosis infection (TB) in children in the Northwestern Federal District of Russia before and after the COVID-19 pandemic based on mathematical modeling and forecasting.Materials and methods. The main epidemiological indicators of TB were analyzed using the official statistical data for 2009–2021. A mathematical forecasting of epidemiological indicators was performed based on chest X-ray screening for TB. A statistical analysis was carried out using the software environment R (v.3.5.1) and the commercial software Statistical Package for Social Sciences (SPSS Statistics for Windows, version 24.0, IBM Corp., 2016). Time series forecasting was performed using the programming language of statistical calculations R, version 4.1.2 and the bsts package, version 0.9.8. Results. The mean regression coefficient of a single predictor was found to differ in a model for TB morbidity in children is 0.0098. X-ray screening for TB was established to be a significant mortality predictor in children. At least 60% of the population should undergo TB screening in order for TB prevalence to be controlled in a country with a population above 140 million people.Conclusions. The conducted study revealed a positive correlation between the incidence of tuberculosis in children in Russia and TB screening in at least 60% of the population. Under the current TB screening system in Russia, the epidemic TB situation will continue to improve, despite COVID-19 restrictions. At the same time, in the Northwestern Federal District of Russia, preventive TB screening can be considered sufficient only in the Kaliningrad, Murmansk, and Pskov Oblasts.
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Zhao, Ming, and Wei Wu. "Multiple Population Mortality Jointly Forecasting in China Using PCF Model." Mathematical Problems in Engineering 2022 (September 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2132224.

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Based on the data of population mortality in China since 1994, this paper studies the changing trend of sex differences of mortality by proposing and implementing some novel approaches. First of all, this paper proposes a new method using the Poisson common factor (PCF) model to forecast the mortality jointly by both sexes. The study finds that the PCF model effectively captures the common trend of mortality between two sexes and uses additional factors to reflect differences of between two sexes, which can reduce the errors caused by low quality or large fluctuation of the mortality in China. Meanwhile, the forecasting values of mortality based on the PCF model can abide by the human biological law well, and the sex ratio of mortality converges to a fixed constant in the long run without increasing too much statistical error. Second, this paper improves the parameter estimation method of PCF model, and the innovative two-step method is used to estimate the model, which can make the maximum likelihood estimator converge more easily. Finally, as an application of the novel methods which are proposing in this study, we measure the longevity risk of pension by using the PCF model and find that the PCF model can make up the underestimation of longevity risk from traditional models and provide more scientific information to the sponsor of pension plan.
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Golubenko, V. V., A. A. Aleksandrov, and V. V. Sirotyuk. "ANALYSIS OF PREDICTION METHODS FOR THE FUNCTIONAL DURABILITY OF ROAD MARKINGS." Vestnik SibADI 15, no. 4 (September 12, 2018): 574–87. http://dx.doi.org/10.26518/2071-7296-2018-4-574-587.

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Introduction. The actuality of the research is emphasized in the Strategy of proclaiming the desire for zero mortality on the country’s roads. Increasing the functional durability (the service life period) of horizontal road markings is an effective and cheap way to organize the movement of vehicles and pedestrians, which could reduce the number of accidents by 15-30%.Methods. The detailed factor analysis, influenced on the functional durability of the horizontal road markings and on the durability forecasting methods, is made by the authors.Results. The authors have established the main factors determining the functional durability of the horizontal road marking. They are divided into five groups: weather-climatic; mechanical; properties of the marking material; technological; properties of the road pavement. Moreover, the article presents a critical analysis of the existing methods of forecasting the functional durability of horizontal marking. In foreign publications on the issue under consideration there is no work on the prediction of the functional durability of horizontal marking by creating complex mathematical models. Most foreign and Russian models are based on empirical dependences. However, these methods and models do not take into account a number of important factors, such as the degree of roughness and abrasion of stone materials, their embedding in asphalt concrete coating, etc.Disscussion and conclusion. The authors came to the conclusion that attempts to create a single reliable mathematical model that takes into account more than 40 factors couldn’t be realistic. Therefore, there is a task of developing a number of models that allow to have greater extent that take into account the properties of the road surface, the location and variety of marking lines and predict the functional durability of horizontal road markings for different types of road pavement more reliably.
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Bakhrushyn, Volodymyr. "System approach to data analysis of pandemic development and forecasting." System technologies 4, no. 135 (April 5, 2021): 107–18. http://dx.doi.org/10.34185/1562-9945-4-135-2021-12.

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The Covid-19 pandemic is one of the greatest challenges to humanity of last decades. Effective prevention of its development is the problem of decision-making with many criteria, high uncertainty of the results of a choice, insufficiently understood feedbacks. There has been collected statistics on the main indicators of the pandemic over the past year. However, according to most researchers, the initial data on the number of infected and fatal cases are significantly underestimated. This makes inaccurate other important indicators, in particular, those that characterize the nature of the dynamics, the rate of infection and its mortality. At the same time, a large number of research results provide additional sources of information to improve the quality of pandemic analysis and forecasts. Mathematical models infections spreading have been significantly developed, which make it possible to refine individual indicators.According to model estimates, the maximum number of daily cases can be 50 times higher than the official data, and the dates of maxima can be up to 4-5 weeks earlier. For Ukraine, the highest values of "excess mortality" (as a percentage of the average level in 2015 - 2016) were observed in September (15.8%), October (20.3%), November (33.7%) and December (29.2%). In January 2021, according to incomplete preliminary data it is expected the significant reducing of the indicator to the value less than 2%. Per 100 thousand inhabitants, the "excess mortality" in 2020 was about 38.8 thousands (in the first 5 months there were negative values of the indicator), and for the period from June 2020 to January 2021 - about 53.5 thousands, or 14.1% of the base level for this period. An analysis of Google's search queries gives grounds to assume that in early January 2020, the pandemic spread in many countries on different continents, and precautionary measures were taken too late.The obtained data show that the involvement of additional sources of information makes it possible to compensate for the imperfections of official operational data and to un-derstand better the patterns of occurrence and development of Covid-19 pandemic. At the same time, a system approach to decision-making on prevention the development of a pandemic should take into account the information on morbidity and mortality statistics as well as other information, in particular on virus mutations, re-infection, vaccination, testing, socio-economic consequences etc. This should be the subject of further research.
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Bezverbny, Vadim A., and Sergey V. Pronichkin. "MODELING OF THE DEMOGRAPHIC AND LABOR POTENTIAL OF THE RYAZAN REGION IN THE CONTEXT OF ECONOMIC DEVELOPMENT PROBLEMS." Scientific Review. Series 1. Economics and Law, no. 4 (2020): 29–43. http://dx.doi.org/10.26653/2076-4650-2020-4-03.

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The article is devoted to the assessment and forecasting of demographic indicators, gross regional product, employment, labor force and unemployment by industry in the Ryazan region until 2025-2050. The article analyzes the trends in the demographic development of the Ryazan region, including the dynamics of fertility, mortality and migration. The consequences of population aging and the peculiarities of changes in the age and sex structure of the region's population are also considered. To solve the problem of modeling and forecasting, economic and mathematical models have been developed that include the parameters of socio-economic development. The social component is based on a systematic approach to forecasting employment, depending on the anthropogenic load index, which takes into account life expectancy and standard of living, literacy of the population, crime rate, ecological state and other indicators of socio-economic development of the region. The economic component uses econometric analysis by types of economic activities in the Ryazan region, as well as time series analysis to predict employment in both the medium and short term. In terms of the labor market, the labor force is forecasted taking into account the socio-economic effect of hidden unemployment. In conclusion, forecasts are made about the dynamics of unemployment in the Ryazan region and the influence of demographic factors on the formation of the labor force.
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Krivorotko,, O. I., and N. Y. Zyatkov,. "DATA-DRIVEN REGULARIZATION OF INVERSE PROBLEM FOR SEIR-HCD MODEL OF COVID-19 PROPAGATION IN NOVOSIBIRSK REGION." Eurasian Journal of Mathematical and Computer Applications 10, no. 1 (March 2022): 51–68. http://dx.doi.org/10.32523/2306-6172-2022-10-1-51-68.

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Abstract The inverse problem for SEIR-HCD model of COVID-19 propagation in Novosi- birsk region described by system of seven nonlinear ordinary differential equations (ODE) is numerical investigated. The inverse problem consists in identification of coefficients of ODE system (infection rate, portions of infected, hospitalized, mortality cases) and some ini- tial conditions (initial number of asymptomatic and symptomatic infectious) by additional measurements about daily diagnosed, critical and mortality cases of COVID-19. Due to ill-posedness of inverse problem the regularization is applied based on usage of additional information about antibodies IgG to COVID-19 and detailed mortality statistics. The inverse problem is reduced to a minimization problem of misfit function. We apply data-driven ap- proach based on combination of global (OPTUNA software) and gradient-type methods for solving the minimization problem. The numerical results show that adding new information and detailed statistics increased the forecasting scenario in 2 times.
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Corpas-Burgos, Francisca, and Miguel A. Martinez-Beneito. "An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting." Mathematics 9, no. 4 (February 14, 2021): 384. http://dx.doi.org/10.3390/math9040384.

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One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.
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Dissertations / Theses on the topic "Mortality – Forecasting – Mathematical models"

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Sulemana, Hisham. "Comparison of mortality rate forecasting using the Second Order Lee–Carter method with different mortality models." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-43563.

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Mortality information is very important for national planning and health of a country. Mortality rate forecasting is a basic contribution for the projection of financial improvement of pension plans, well-being and social strategy planning. In the first part of the thesis, we fit the selected mortality rate models, namely the Power-exponential function based model, the ModifiedPerks model and the Heligman and Pollard (HP4) model to the data obtained from the HumanMortality Database [22] for the male population ages 1–70 of the USA, Japan and Australia. We observe that the Heligman and Pollard (HP4) model performs well and better fit the data as compared to the Power-exponential function based model and the Modified Perks model. The second part is to systematically compare the quality of the mortality rate forecasting using the second order Lee–Carter method with the selected mortality rate models. The results indicate that Power-exponential function based model and the Heligman and Pollard (HP4) model gives a more reliable forecast depending on individual countries.
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Ramos, Anthony Kojo. "Forecasting Mortality Rates using the Weighted Hyndman-Ullah Method." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54711.

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The performance of three methods of mortality modelling and forecasting are compared. These include the basic Lee–Carter and two functional demographic models; the basic Hyndman–Ullah and the weighted Hyndman–Ullah. Using age-specific data from the Human Mortality Database of two developed countries, France and the UK (England&Wales), these methods are compared; through within-sample forecasting for the years 1999-2018. The weighted Hyndman–Ullah method is adjudged superior among the three methods through a comparison of mean forecast errors and qualitative inspection per the dataset of the selected countries. The weighted HU method is then used to conduct a 32–year ahead forecast to the year 2050.
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Boulougari, Andromachi. "Application of a power-exponential function based model to mortality rates forecasting." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39921.

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De modellering van een wet of mortaliteit heeft een consequente interesse van een grote meerderheid van onderzoekers en vele modellen door de jaren is voorgesteld. The first aim of this thesis is to systematically evaluate a selection of models --- Modified Perks, Heligman-Pollard and Power-exponential --- to determine their relative strengths and weaknesses with regard to forecasting the mortality rate using the Lee-Carter model. Den andre målsætningen er at tilpasse dødelighedsdata ved de selektive modeller fra USA, Sverige og Grækenland ved hjælp af numeriske teknikker til kurvefitting med den ikke-lineære mindst kvadratmetode. The results indicate that the Heligman-Pollard model performs better especially when the phenomenon of the `` accident hump '' occurs during adulthood.
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Putnam, Douglas Alan. "Forecasting for local water management." PDXScholar, 1985. https://pdxscholar.library.pdx.edu/open_access_etds/3540.

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Forecast models are investigated and developed for use in local water management to aid in determining short term water requirements and availability. The forecast models include precipitation occurrence and depth using a Markov chain model, temperature and solar radiation with a multivariate autoregressive model, and streamflow with autoregressive-moving average models. The precipitation, temperature, and solar radiation forecasts are used with a soil moisture model to determine water demands. A state space approach to the Muskingum-Cunge streamflow routing technique is developed. The forecast water demands and streamflow forecasts are used as inputs to this routing model. Forecast model errors and propagation of these errors from one model into the next are investigated.
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Zbib, Imad J. (Imad Jamil). "Sales Forecasting Accuracy Over Time: An Empirical Investigation." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332526/.

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This study investigated forecasting accuracy over time. Several quantitative and qualitative forecasting models were tested and a number of combinational methods was investigated. Six time series methods, one causal model, and one subjective technique were compared in this study. Six combinational forecasts were generated and compared to individual forecasts. A combining technique was developed. Thirty data sets, obtained from a market leader in the cosmetics industry, were used to forecast sales. All series represent monthly sales from January 1985 to December 1989. Gross sales forecasts from January 1988 to June 1989 were generated by the company using econometric models. All data sets exhibited seasonality and trend.
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Nyulu, Thandekile. "Weather neutral models for short-term electricity demand forecasting." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1018751.

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Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.
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Lu, Zhen Cang. "Price forecasting models in online flower shop implementation." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691395.

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Venter, Daniel Jacobus Lodewyk. "The consolidation of forecests with regression models." Thesis, Nelson Mandela Metropolitan University, 2014. http://hdl.handle.net/10948/d1020964.

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The primary objective of this study was to develop a dashboard for the consolidation of multiple forecasts utilising a range of multiple linear regression models. The term dashboard is used to describe with a single word the characteristics of the forecasts consolidation application that was developed to provide the required functionalities via a graphical user interface structured as a series of interlinked screens. Microsoft Excel© was used as the platform to develop the dashboard named ConFoRM (acronym for Consolidate Forecasts with Regression Models). The major steps of the consolidation process incorporated in ConFoRM are: 1. Input historical data. Select appropriate analysis and holdout samples. 3. Specify regression models to be considered as candidates for the final model to be used for the consolidation of forecasts. 4. Perform regression analysis and holdout analysis for each of the models specified in step 3. 5. Perform post-holdout testing to assess the performance of the model with best holdout validation results on out-of-sample data. 6. Consolidate forecasts. Two data transformations are available: the removal of growth and time-periods effect from the time series; a translation of the time series by subtracting ̅i, the mean of all the forecasts for data record i, from the variable being predicted and its related forecasts for each data record I. The pre-defined regression models available for ordinary least square linear regression models (LRM) are: a. A set of k simple LRM’s, one for each of the k forecasts; b. A multiple LRM that includes all the forecasts: c. A multiple LRM that includes all the forecasts and as many of the first-order interactions between the input forecasts as allowed by the sample size and the maximum number of predictors provided by the dashboard with the interactions included in the model to be those with the highest individual correlation with the variable being predicted; d. A multiple LRM that includes as many of the forecasts and first-order interactions between the input forecasts as allowed by the sample size and the maximum number of predictors provided by the dashboard: with the forecasts and interactions included in the model to be those with the highest individual correlation with the variable being predicted; e. A simple LRM with the predictor variable being the mean of the forecasts: f. A set of simple LRM’s with the predictor variable in each case being the weighted mean of the forecasts with different formulas for the weights Also available is an ad hoc user specified model in terms of the forecasts and the predictor variables generated by the dashboard for the pre-defined models. Provision is made in the regression analysis for both of forward entry and backward removal regression. Weighted least squares (WLS) regression can be performed optionally based on the age of forecasts with smaller weight for older forecasts.
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Chan, Johnson Lap-Kay. "Numerical procedure for potential flow problems with a free surface." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/28637.

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A numerical procedure based upon a boundary integral method for gravity wave making problems is studied in the time domain. The free-surface boundary conditions are combined and expressed in a Lagrangian notation to follow the free-surface particle's motion in time. The corresponding material derivative term is approximated by a finite difference expression, and the velocity terms are extrapolated in time for the completion of the formulations. The fluid-body intersection position at the free surface is predicted by an interpolation function that requires information from both the free surface and the submerged surface conditions. Solutions corresponding to a linear free-surface condition and to a non-linear free-surface condition are obtained at small time increment values. Numerical modelling of surface wave problems is studied in two dimensions and in three dimensions. Comparisons are made to linear analytical solutions as well as to published experimental results. Good agreement between the numerical solutions and measured values is found. For the modelling of a three dimensional wave diffraction problem, results at high wave amplitude are restricted because of the use of quadrilateral elements. The near cylinder region of the free surface is not considered to be well represented because of the coarse element size. Wave forces calculated on the vertical cylinder are found to be affected by the modelled tank length. When the simulated wave length is comparable to the wave tank's dimension, numerical results are found to be less than the experimental measurements. However, when the wave length is shorter than the tank's length, solutions are obtained with very good precision.
Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate
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Yan, Tsz-leung, and 甄子良. "Spatio-temporal modeling and forecasting of air quality data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/197498.

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Respirable Suspended Particulate (RSP) time series data sampled in an air quality monitoring network are found strongly correlated and they are varying in highly similar patterns. This study provides a methodology for spatio-temporal modeling and forecasting of multiple RSP time series, in which the dynamic spatial correlations amongst the series can be effectively utilized. 　　The efficacy of the Spatio-Temporal Dynamic Harmonic Regression (STDHR) model is demonstrated. Based on the decomposition of the observed time series into the trend and periodic components, the model is capable of making forecast of RSP data series that exhibit variation patterns during air pollution episodes and typhoons with dynamic weather conditions. It is also capable to produce spatial predictions of RSP time series up to three unobserved sites. 　　The Noise-variance-ratio (NVR) form of the multivariate recursive algorithm ((M2) algorithm) that derived by the author can greatly facilitate its practical application in both multivariate and univariate time series analysis. The (M2) algorithm allows the spatial correlations to be specified at parametric levels. The state-space (SS) model formulation can flexibly accommodate the existing inter or intra (auto) correlations amongst the parameters of the data series. 　　Applications of the variance intervention (VI) are exploited and illustrated with a real life case study which involves forecasting of RSP data series during an air pollution episode. This illustrates that time series with abrupt changes can be predicted by automatic implementation of the VI approach. 　　The present study also extended the anisotropic Matern model to estimate the dynamic spatial correlation structure of the air quality data by using mean wind speed and prevailing wind direction in defining the spatial anisotropy. The Anisotropic Matern model by Mean Wind Speed and Prevailing Wind Direction (AMMP) model that devised by the author can avoid huge computational burden in estimating variogram at every variation of the underlying spatial structure. 　　Finally, the findings of this dissertation have laid the foundation for further research on multiple time series analysis and estimation of dynamic spatial structure.
published_or_final_version
Geography
Doctoral
Doctor of Philosophy
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Books on the topic "Mortality – Forecasting – Mathematical models"

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Shailer, G. E. P. Experimental failure prediction models for small companies. [Newcastle, Australia]: Dept. of Economics and Industrial Economics, 1986.

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Väinö, Kannisto, and Vaupel James W, eds. The force of mortality at ages 80 to 120. Odense, Denmark: Odense University Press, 1998.

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Battles, John. Projecting climate change impacts on forest growth and yield for California's Sierran mixed conifer forests: Final paper. Sacramento, Calif.]: California Energy Commission, 2009.

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Molnar, Alan T. Economic forecasting. New York: Nova Science Publishers, 2010.

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Jones, Andy. Mathematical models for forecasting passport demand. London: Home Office Research and Planning Unit, 1994.

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Molnar, Alan T. Economic forecasting. Hauppauge, NY: Nova Science Publishers, 2009.

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D, Lawrence Kenneth, and Guerard John, eds. Forecasting sales. Greenwich, Conn: JAI Press, 1994.

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Horowitz, Alan J. Statewide travel forecasting models. Washington, D.C: Transportation Research Board, 2006.

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Federico, Girosi, ed. Demographic forecasting. Princeton, NJ: Princeton Univeristy Press, 2008.

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Rogers, Andrei. Regional population projection models. Beverly Hills: Sage, 1985.

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Book chapters on the topic "Mortality – Forecasting – Mathematical models"

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Atance, David, Ana Debón, and Eliseo Navarro. "Resampling Methods to Assess the Forecasting Ability of Mortality Models." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 45–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_8.

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Sánchez, Manuel Sánchez. "Modern Forecasting of NOEM Models." In CIM Series in Mathematical Sciences, 617–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16118-1_34.

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Gogola, Ján. "Stochastic Mortality Models. Application to CR Mortality Data." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 113–16. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05014-0_26.

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Tabeau, Ewa. "A Review of Demographic Forecasting Models for Mortality." In European Studies of Population, 1–32. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/0-306-47562-6_1.

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Atancd, David, Alejandro Balbas, and Eliseo Navarro. "Method for Forecasting Mortality Based on Key Rates." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 39–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_7.

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Manton, Kenneth G., Burton H. Singer, and Eric Stallard. "Cancer Forecasting: Cohort Models of Disease Progression and Mortality." In Forecasting the Health of Elderly Populations, 109–36. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9332-0_5.

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Beard, R. E. "Appendix: Note on Some Mathematical Mortality Models." In Ciba Foundation Symposium - The Lifespan of Animals (Colloquia on Ageing, Vol. 5), 302–11. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470715253.app1.

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Heathcote, Christopher, and Tim Higgins. "Forecasting Mortality from Regression Models: The Case of the Netherlands." In European Studies of Population, 83–103. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/0-306-47562-6_4.

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Basellini, Ugofilippo, and Carlo Giovanni Camarda. "A Three-Component Approach to Model and Forecast Age-at-Death Distributions." In Developments in Demographic Forecasting, 105–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_6.

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Abstract Mortality forecasting has recently received growing interest, as accurate projections of future lifespans are needed to ensure the solvency of insurance and pension providers. Several innovative stochastic methodologies have been proposed in most recent decades, the majority of them being based on age-specific mortality rates or on summary measures of the life table. The age-at-death distribution is an informative life-table function that provides readily available information on the mortality pattern of a population, yet it has been mostly overlooked for mortality projections. In this chapter, we propose to analyse and forecast mortality developments over age and time by introducing a novel methodology based on age-at-death distributions. Our approach starts from a nonparametric decomposition of the mortality pattern into three independent components corresponding to Childhood, Early-Adulthood and Senescence, respectively. We then model the evolution of each component-specific death density with a relational model that associates a time-invariant standard to a series of observed distributions by means of a transformation of the age axis. Our approach allows us to capture mortality developments over age and time, and forecasts can be derived from parameters’ extrapolation using standard time series models. We illustrate our methods by estimating and forecasting the mortality pattern of females and males in two high-longevity countries using data of the Human Mortality Database. We compare the forecast accuracy of our model and its projections until 2050 with three other forecasting methodologies.
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Bergeron-Boucher, Marie-Pier, Søren Kjæ rgaard, Marius D. Pascariu, José Manuel Aburto, Jesús-Adrián Alvarez, Ugofilippo Basellini, Silvia Rizzi, and James W. Vaupel. "Alternative Forecasts of Danish Life Expectancy." In Developments in Demographic Forecasting, 131–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_7.

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Abstract In the last three decades, considerable progress in mortality forecasting has been achieved, with new and more sophisticated models being introduced. Most of these forecasting models are based on the extrapolation of past trends, often assuming linear (or log-linear) development of mortality indicators, such as death rates or life expectancy. However, this assumption can be problematic in countries where mortality development has not been linear, such as in Denmark. Life expectancy in Denmark experienced stagnation from the 1980s until the mid-1990s. To avoid including the effect of the stagnation, Denmark’s official forecasts are based on data from 1990 only. This chapter is divided into three parts. First, we highlight and discuss some of the key methodological issues for mortality forecasting in Denmark. How many years of data are needed to forecast? Should linear extrapolation be used? Second, we compare the forecast performance of 11 models for Danish females and males and for period and cohort data. Finally, we assess the implications of the various forecasts for Danish society, and, in particular, their implications for future lifespan variability and age at retirement.
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Conference papers on the topic "Mortality – Forecasting – Mathematical models"

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Ibrahim, Rose Irnawaty, Razak Mohd, Nuraini Ngataman, and Wan Nur Azifah Wan Mohd Abrisam. "Forecasting the mortality rates of Malaysian population using Heligman-Pollard model." In PROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Mathematical Sciences Exploration for the Universal Preservation. Author(s), 2017. http://dx.doi.org/10.1063/1.4995851.

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Kafi, Rahmat Al, Sri Mardiyati, and Maulana Malik. "Forecasting mortality rates of elderly in Indonesia using the first generalized Cairns-Blake-Dowd model." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136386.

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Aji, N. P., S. Mardiyati, and M. Malik. "Forecasting Indonesian mortality rates using by Lee-Carter model and Regression Linear model." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132468.

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Manulang, J. M., D. Lestari, and S. Mardiyati. "Forecasting Indonesian mortality rates using the Lee-Carter model and ANFIS method." In PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2017 (ISCPMS2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5064208.

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Zili, A. H. A., S. Mardiyati, and D. Lestari. "Forecasting Indonesian mortality rates using the Lee-Carter model and ARIMA method." In PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2017 (ISCPMS2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5064209.

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Ngataman, Nuraini, Rose Irnawaty Ibrahim, and Mazlynda Md Yusuf. "Forecasting the mortality rates of Malaysian population using Lee-Carter method." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954522.

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Penenko, Viktor V. "Mathematical models for monitoring and forecasting atmospheric aerosol spreading." In Sixth International Symposium on Atmospheric and Ocean Optics, edited by Gennadii G. Matvienko and Vladimir P. Lukin. SPIE, 1999. http://dx.doi.org/10.1117/12.370544.

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Henni, Omar, and Mustapha Belarbi. "Effect of Mathematical Models on Forecasting Analysis of Photovoltaic Power." In 2021 9th International Conference on Smart Grid (icSmartGrid). IEEE, 2021. http://dx.doi.org/10.1109/icsmartgrid52357.2021.9551221.

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El Mezouary, Lhoussaine, Bouabid El Mansouri, and Samir Kabbaj. "Mathematical and Deep Learning Models Forecasting for Hydrological Time Series." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010732000003101.

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Yandybaeva, Natalya, Alexander Rezchikov, Evgeny Gorschkov, Alexey Bogomolov, and Vadim Kuschnikov. "Mathematical Models and Algorithms for Forecasting National Security in Training Situational Centers." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247640.

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