Journal articles: '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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Yaacob, Nurul Aityqah, Jamil J. Jaber, Dharini Pathmanathan, Sadam Alwadi, and Ibrahim Mohamed. "Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates." Mathematics 9, no. 18 (September 17, 2021): 2295. http://dx.doi.org/10.3390/math9182295.

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This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the ARIMA model is enhanced with the integration of various maximal overlap discrete wavelet transform filters such as the least asymmetric, best-localized, and Coiflet filters. These models are then applied to the mortality data of Australia, England, France, Japan, and USA. The accuracy of the projecting log of death rates of the MODWT-ARIMA model with the aforementioned wavelet filters are assessed using mean absolute error, mean absolute percentage error, and mean absolute scaled error. The MODWT-ARIMA (5,1,0) model with the BL14 filter gives the best fit to the log of death rates data for males, females, and total population, for all five countries studied. Implementing the MODWT leads towards improvement in the performance of the standard framework of the LC model in forecasting mortality rates.

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Ahmad, Zubair, Zahra Almaspoor, Faridoon Khan, and Mahmoud El-Morshedy. "On Predictive Modeling Using a New Flexible Weibull Distribution and Machine Learning Approach: Analyzing the COVID-19 Data." Mathematics 10, no. 11 (May 24, 2022): 1792. http://dx.doi.org/10.3390/math10111792.

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Predicting and modeling time-to-events data is a crucial and interesting research area. For modeling and predicting such types of data, numerous statistical models have been suggested and implemented. This study introduces a new statistical model, namely, a new modified flexible Weibull extension (NMFWE) distribution for modeling the mortality rate of COVID-19 patients. The introduced model is obtained by modifying the flexible Weibull extension model. The maximum likelihood estimators of the NMFWE model are obtained. The evaluation of the estimators of the NMFWE model is assessed in a simulation study. The flexibility and applicability of the NMFWE model are established by taking two datasets representing the mortality rates of COVID-19-infected persons in Mexico and Canada. For predictive modeling, we consider two pure statistical models and two machine learning (ML) algorithms. The pure statistical models include the autoregressive moving average (ARMA) and non-parametric autoregressive moving average (NP-ARMA), and the ML algorithms include neural network autoregression (NNAR) and support vector regression (SVR). To evaluate their forecasting performance, three standard measures of accuracy, namely, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are calculated. The findings demonstrate that ML algorithms are very effective at predicting the mortality rate data.

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Rasella, Davide, Gabriel Alves de Sampaio Morais, Rodrigo Volmir Anderle, Andréa Ferreira da Silva, Iracema Lua, Ronaldo Coelho, Felipe Alves Rubio, et al. "Evaluating the impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: Study protocol of a retrospective and forecasting approach based on the data integration with a cohort of 100 million Brazilians." PLOS ONE 17, no. 3 (March 22, 2022): e0265253. http://dx.doi.org/10.1371/journal.pone.0265253.

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Background Despite the great progress made over the last decades, stronger structural interventions are needed to end the HIV/AIDS pandemic in Low and Middle-Income Countries (LMIC). Brazil is one of the largest and data-richest LMIC, with rapidly changing socioeconomic characteristics and an important HIV/AIDS burden. Over the last two decades Brazil has also implemented the world’s largest Conditional Cash Transfer programs, the Bolsa Familia Program (BFP), and one of the most consolidated Primary Health Care (PHC) interventions, the Family Health Strategy (FHS). Objective We will evaluate the effects of socioeconomic determinants, BFP exposure and FHS coverage on HIV/AIDS incidence, treatment adherence, hospitalizations, case fatality, and mortality using unprecedently large aggregate and individual-level longitudinal data. Moreover, we will integrate the retrospective datasets and estimated parameters with comprehensive forecasting models to project HIV/AIDS incidence, prevalence and mortality scenarios up to 2030 according to future socioeconomic conditions and alternative policy implementations. Methods and analysis We will combine individual-level data from all national HIV/AIDS registries with large-scale databases, including the “100 Million Brazilian Cohort”, over a 19-year period (2000–2018). Several approaches will be used for the retrospective quasi-experimental impact evaluations, such as Regression Discontinuity Design (RDD), Random Administrative Delays (RAD) and Propensity Score Matching (PSM), combined with multivariable Poisson regressions for cohort analyses. Moreover, we will explore in depth lagged and long-term effects of changes in living conditions and in exposures to BFP and FHS. We will also investigate the effects of the interventions in a wide range of subpopulations. Finally, we will integrate such retrospective analyses with microsimulation, compartmental and agent-based models to forecast future HIV/AIDS scenarios. Conclusion The unprecedented datasets, analyzed through state-of-the-art quasi-experimental methods and innovative mathematical models will provide essential evidences to the understanding and control of HIV/AIDS epidemic in LMICs such as Brazil.

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Zhou, Yinghui, Zubair Ahmad, Zahra Almaspoor, Faridoon Khan, Elsayed tag-Eldin, Zahoor Iqbal, and Mahmoud El-Morshedy. "On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data." Mathematical Biosciences and Engineering 20, no. 1 (2022): 337–64. http://dx.doi.org/10.3934/mbe.2023016.

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<abstract><p>Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.</p></abstract>

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Batool, Humera, and Lixin Tian. "Correlation Determination between COVID-19 and Weather Parameters Using Time Series Forecasting: A Case Study in Pakistan." Mathematical Problems in Engineering 2021 (June 15, 2021): 1–9. http://dx.doi.org/10.1155/2021/9953283.

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Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan’s COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection.

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Tariq, Amna, Tsira Chakhaia, Sushma Dahal, Alexander Ewing, Xinyi Hua, Sylvia K. Ofori, Olaseni Prince, et al. "An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020–2021." PLOS Neglected Tropical Diseases 16, no. 3 (March 4, 2022): e0010228. http://dx.doi.org/10.1371/journal.pntd.0010228.

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Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with Rt<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.

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Shilbayeh, Samar A., Abdullah Abonamah, and Ahmad A. Masri. "Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction." Applied Sciences 10, no. 16 (August 17, 2020): 5696. http://dx.doi.org/10.3390/app10165696.

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Prediction models of coronavirus disease utilizing machine learning algorithms range from forecasting future suspect cases, predicting mortality rates, to building a pattern for country-specific pandemic end date. To predict the future suspect infection and death cases, we categorized the approaches found in the literature into: first, a purely data-driven approach, whose goal is to build a mathematical model that relates the data variables including outputs with inputs to detect general patterns. The discovered patterns can then be used to predict the future infected cases without any expert input. The second approach is partially data-driven; it uses historical data, but allows expert input such as the SIR epidemic algorithm. This approach assumes that the epidemic will end according to medical reasoning. In this paper, we compare the purely data-driven and partially-data driven approaches by applying them to data from three countries having different past pattern behavior. The countries are the US, Jordan, and Italy. It is found that those two prediction approaches yield significantly different results. Purely data-driven approach depends totally on the past behavior and does not show any decline in the number of the infected cases if the country did not experience any decline in the number of cases. On the other hand, a partially data-driven approach guarantees a timely decline of the infected curve to reach zero. Using the two approaches highlights the importance of human intervention in pandemic prediction to guide the learning process as opposed to the purely data-driven approach that predicts future cases based on the pattern detected in the data.

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Keeling, Matt J., Edward M. Hill, Erin E. Gorsich, Bridget Penman, Glen Guyver-Fletcher, Alex Holmes, Trystan Leng, et al. "Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies." PLOS Computational Biology 17, no. 1 (January 22, 2021): e1008619. http://dx.doi.org/10.1371/journal.pcbi.1008619.

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Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.

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Evdokimov, Dmitry. "Approaches to Assessing the Socio-Economic Consequences of the COVID-19 Pandemic Using Computer Simulation." Artificial societies 17, no. 3 (2022): 0. http://dx.doi.org/10.18254/s207751800021929-0.

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In the context of the coronavirus pandemic, there is an increasing need to develop methods for scientifically based assessment of the consequences both at the level of the country's economy and at the regional level. One of the acute problems of the development of the Russian economy in the context of the coronavirus pandemic is the conflict between measures to protect the life and health of people and the fall in economic activity. To support the economy, countries are taking anti-crisis measures, which are aimed primarily at overcoming serious consequences in the most vulnerable sectors. As part of the study, to assess the socio-economic consequences of the epidemic and reproduce forecasts, modern simulation tools are used - agent-based modeling. Agent-based models allow you to use software of various classes, including neural networks, mathematical models, 3D-4D add-ons and other technologies that can visualize the results of scenario predictive estimates and computational experiments. The aim of the study is to develop methods and techniques for forecasting and scenario modeling of the socio-economic consequences of viral epidemics. For the study, a detailed statistical and analytical database was formed, adaptive blocks were developed with the possibility of additional inclusion of indicators. The software implementation included three functional blocks: demographic, economic and epidemiological, as well as three categories of agents within each subject of the Russian Federation with individual characteristics based on accepted world practice. The software tool chosen to implement the research objectives is the platform for creating agent-based models "AnyLogic". The study was carried out on the example of the following subjects of the Russian Federation: Murmansk region, Krasnodar region, Sverdlovsk, Samara and Voronezh regions. Based on the results of the study, an architecture of an agent-based model was developed, which makes it possible to evaluate restrictive measures and regulations in terms of the socio-economic consequences of a pandemic. As a result of the study, methods and algorithms for agent-based modeling of the socio-economic consequences of viral epidemics were developed, taking into account spatial and communicative interactions. To fulfill the objectives of the study, at the first stage, an analysis of scientific methods for forecasting and building various models for assessing the consequences of macroeconomic decisions and models for the spread of viral epidemics was carried out. At the second stage, an agent-based model was developed, which took into account structured and unstructured information, including the socio-demographic and economic characteristics of the regions, such as morbidity and mortality, employment rates, as well as measures taken by the regions to counter the spread of COVID-19. In terms of social interaction between agents, the study implemented a dynamic multi-relational (MRN) social network of agents, the structure of which changes during the introduction of quarantine measures that limit the degree of interaction between them. The introduction of different specific values of individual characteristics within a population of agents of the same type makes it possible to assess the socio-economic consequences of viral epidemics with the maximum degree of detail - at the level of individuals. Further development of this area of research will include refinement of the developed model for analyzing the consequences of the spread of viral epidemics in terms of the socio-economic development of territorial systems based on the obtained forecast scenarios.

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20

Penenko, V. V., and E. A. Tsvetova. "Mathematical models of environmental forecasting." Journal of Applied Mechanics and Technical Physics 48, no. 3 (May 2007): 428–36. http://dx.doi.org/10.1007/s10808-007-0053-4.

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Rai, L. P., Naresh Kumar, and C. K. Goel. "Forecasting Internet demand using mathematical models." Journal of Discrete Mathematical Sciences and Cryptography 7, no. 1 (January 2004): 37–48. http://dx.doi.org/10.1080/09720529.2004.10697987.

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Miller, James J., Cynthia S. McCahon, and Judy L. Miller. "Foodservice Forecasting Using Simple Mathematical Models." Hospitality Research Journal 15, no. 1 (February 1991): 43–58. http://dx.doi.org/10.1177/109634809101500105.

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Nielsen, Bent, and Jens P. Nielsen. "Identification and Forecasting in Mortality Models." Scientific World Journal 2014 (2014): 1–24. http://dx.doi.org/10.1155/2014/347043.

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Mortality models often have inbuilt identification issues challenging the statistician. The statistician can choose to work with well-defined freely varying parameters, derived as maximal invariants in this paper, or with ad hoc identified parameters which at first glance seem more intuitive, but which can introduce a number of unnecessary challenges. In this paper we describe the methodological advantages from using the maximal invariant parameterisation and we go through the extra methodological challenges a statistician has to deal with when insisting on working with ad hoc identifications. These challenges are broadly similar in frequentist and in Bayesian setups. We also go through a number of examples from the literature where ad hoc identifications have been preferred in the statistical analyses.

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24

Hansen, Hendrik. "The forecasting performance of mortality models." AStA Advances in Statistical Analysis 97, no. 1 (December 21, 2011): 11–31. http://dx.doi.org/10.1007/s10182-011-0186-x.

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Shair, Syazreen, Sachi Purcal, and Nick Parr. "Evaluating Extensions to Coherent Mortality Forecasting Models." Risks 5, no. 1 (March 10, 2017): 16. http://dx.doi.org/10.3390/risks5010016.

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Feng, Lingbing, and Yanlin Shi. "Forecasting mortality rates: multivariate or univariate models?" Journal of Population Research 35, no. 3 (July 9, 2018): 289–318. http://dx.doi.org/10.1007/s12546-018-9205-z.

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Enchev, Vasil, Torsten Kleinow, and Andrew J. G. Cairns. "Multi-population mortality models: fitting, forecasting and comparisons." Scandinavian Actuarial Journal 2017, no. 4 (January 27, 2016): 319–42. http://dx.doi.org/10.1080/03461238.2015.1133450.

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Han, Jeongwoo, and Vijay P. Singh. "Forecasting of droughts and tree mortality under global warming: a review of causative mechanisms and modeling methods." Journal of Water and Climate Change 11, no. 3 (April 13, 2020): 600–632. http://dx.doi.org/10.2166/wcc.2020.239.

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Abstract Droughts of greater severity are expected to occur more frequently at larger space-time scales under global warming and climate change. Intensified drought and increased rainfall intermittency will heighten tree mortality. To mitigate drought-driven societal and environmental hazards, reliable long-term drought forecasting is critical. This review examines causative mechanisms for drought and tree mortality, and synthesizes stochastic, statistical, dynamical, and hybrid statistical-dynamical drought forecasting models as well as theoretical, empirical, and mechanistic tree mortality forecasting models. Since an increase in global mean temperature changes the strength of sea surface temperature (SST) teleconnections, forecasting models should have the flexibility to incorporate the varying causality of drought. Some of the statistical drought forecasting models, which have nonlinear and nonstationary natures, can be merged with dynamical models to compensate for their lack of stochastic structure in order to improve forecasting skills. Since tree mortality is mainly affected by a hydraulic failure under drought conditions, mechanistic forecasting models, due to their capacity to track the percentage of embolisms against available soil water, are adequate to forecast tree mortality. This study also elucidates approaches to improve long-term drought forecasting and regional tree mortality forecasting as a future outlook for drought studies.

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Kansagra, Susan M. "The Modern Crystal Ball: Influenza Forecasting With Mathematical Models." Annals of Internal Medicine 151, no. 12 (December 15, 2009): 886. http://dx.doi.org/10.7326/0000605-200912150-00154.

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30

Nuryaman, A., M. A. A. F. Pamungkas, and S. Saidi. "Forecasting of Bandar Lampung’s Population Using Growth Mathematical Models." Journal of Physics: Conference Series 1338 (October 2019): 012037. http://dx.doi.org/10.1088/1742-6596/1338/1/012037.

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Kumar, Vinoth, Yasushi Harada, Soumen Dey, and Mohan Delampady. "Mathematical examination of structural changes in load forecasting models." IFAC-PapersOnLine 51, no. 28 (2018): 268–73. http://dx.doi.org/10.1016/j.ifacol.2018.11.713.

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Eglit, Y. Y., К. Y. Eglit, M. A. Shapovalova, and D. G. Semina. "FORECASTING THE PERFORMANCE OF THE TRANSPORT SYSTEM." System analysis and logistics 4, no. 26 (December 17, 2020): 72–79. http://dx.doi.org/10.31799/2007-5687-2020-4-72-79.

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The article is devoted to the development of a methodology for predicting the performance of the transport system using mathematical models that can be used to effectively analyze and predict the functioning of complex systems. Key words: transport system, fleet, ships, analysis, forecasting, mathematical models.

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Yan, Hongxuan, Gareth W. Peters, and Jennifer S. K. Chan. "MULTIVARIATE LONG-MEMORY COHORT MORTALITY MODELS." ASTIN Bulletin 50, no. 1 (December 23, 2019): 223–63. http://dx.doi.org/10.1017/asb.2019.35.

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AbstractThe existence of long memory in mortality data improves the understandings of features of mortality data and provides a new approach for establishing mortality models. The findings of long-memory phenomena in mortality data motivate us to develop new mortality models by extending the Lee–Carter (LC) model to death counts and incorporating long-memory model structure. Furthermore, there are no identification issues arising in the proposed model class. Hence, the constraints which cause many computational issues in LC models are removed. The models are applied to analyse mortality death count data sets from three different countries divided according to genders. Bayesian inference with various selection criteria is applied to perform the model parameter estimation and mortality rate forecasting. Results show that multivariate long-memory mortality model with long-memory cohort effect model outperforms multivariate extended LC cohort model in both in-sample fitting and out-sample forecast. Increasing the accuracy of forecasting of mortality rates and improving the projection of life expectancy is an important consideration for insurance companies and governments since misleading predictions may result in insufficient funds for retirement and pension plans.

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Зимин, М. И., О. А. Кумукова, and М. М. Зимин. "Mathematical Model and Software for Avalanche Forecasting." Успехи кибернетики / Russian Journal of Cybernetics, no. 1(1) (March 31, 2020): 68–86. http://dx.doi.org/10.51790/2712-9942-2020-1-1-9.

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Описано математическое и программное обеспечение для прогнозирования возможности схода снежных лавин. Учитываются данные о возникновении этих склоновых процессов с конкретных склонов. Описана база данных. The study presents mathematical models and software for avalanche forecasting. They take into account the avalanche occurrence rate for specific slopes. The database is also presented.

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Atance, David, Ana Debón, and Eliseo Navarro. "A Comparison of Forecasting Mortality Models Using Resampling Methods." Mathematics 8, no. 9 (September 10, 2020): 1550. http://dx.doi.org/10.3390/math8091550.

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The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies’ profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee–Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.

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Santos, Douglas Matheus das Neves, Yuri Antônio da Silva Rocha, Danúbia Freitas, Paulo Beltrão, Paulo Santos Junior, Glauber Marques, Otavio Chase, and Pedro Campos. "Time-series forecasting models." International Journal for Innovation Education and Research 9, no. 8 (August 1, 2021): 24–47. http://dx.doi.org/10.31686/ijier.vol9.iss8.3239.

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Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.

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37

BISWAS, B. C. "Forecasting for agricultural application." MAUSAM 41, no. 2 (February 22, 2022): 188–93. http://dx.doi.org/10.54302/mausam.v41i2.2630.

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Methods for agro-meteorological forecasts are mainly based on crop-weather relationship and statistical/mathematical models. Models developed from historic data make it possible to obtain the expected values fairly in advance so that appropriate action may be taken to avail of beneficial aspect of weather and minimise or avoid detrimental effect. Validity of these models under different conditions is imperative as the climatic conditions of general field may be quite different from those of experimental one. This paper discusses the work done on the above aspects.

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38

Petrusevich, D. A. "ANALYSIS OF MATHEMATICAL MODELS USED FOR ECONOMETRICAL TIME SERIES FORECASTING." Russian Technological Journal 7, no. 2 (May 16, 2019): 61–73. http://dx.doi.org/10.32362/2500-316x-2019-7-2-61-73.

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Anoikin, Roman K. "Analysis of Mathematical Models Used for Forest Ground Fires Forecasting." Технологии гражданской безопасности 17, no. 2 (2020): 58–60. http://dx.doi.org/10.54234/cst.19968493.2020.17.2.64.10.58.

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Travin, S. O., Yu I. Skurlatov, and A. V. Roshchin. "Capabilities and Limitations of Mathematical Models in Ecological Safety Forecasting." Russian Journal of Physical Chemistry B 14, no. 1 (January 2020): 86–99. http://dx.doi.org/10.1134/s1990793120010315.

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Currie, Iain D. "Constraints, the identifiability problem and the forecasting of mortality." Annals of Actuarial Science 14, no. 2 (March 16, 2020): 537–66. http://dx.doi.org/10.1017/s1748499520000020.

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AbstractModels of mortality often require constraints in order that parameters may be estimated uniquely. It is not difficult to find references in the literature to the “identifiability problem”, and papers often give arguments to justify the choice of particular constraint systems designed to deal with this problem. Many of these models are generalised linear models, and it is known that the fitted values (of mortality) in such models are identifiable, i.e., invariant with respect to the choice of constraint systems. We show that for a wide class of forecasting models, namely ARIMA $(p,\delta, q)$ models with a fitted mean and $\delta = 1$ or 2, identifiability extends to the forecast values of mortality; this extended identifiability continues to hold when some model terms are smoothed. The results are illustrated with data on UK males from the Office for National Statistics for the age-period model, the age-period-cohort model, the age-period-cohort-improvements model of the Continuous Mortality Investigation and the Lee–Carter model.

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Prysiazhnyi, Andrii H., Volodymyr V. Kukhar, Vadym Hornostai, Ekaterina Kudinova, Maryna Korenko, and Oleksandr S. Anishchenko. "Mathematical Models for Forecasting of 10Mn2VNb Steel Heavy Plates Mechanical Properties." Materials Science Forum 1045 (September 6, 2021): 237–45. http://dx.doi.org/10.4028/www.scientific.net/msf.1045.237.

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The problem urgency for determining the optimal rolling and heat treatment schedules for providing the required indices of heavy plates physical and mechanical properties is shown. The use of statistical mathematical models for solving this problem is substantiated and the methodology for their design is described. Statistical mathematical models were designed using the mathematical statistics methods and Data Mining tools to determine the yield strength, ultimate tensile strength and percent elongation for 10Mn2VNb steel plates rolled under 3600 heavy plate mill conditions. Software for the numerical implementation of these statistical mathematical models has been developed. Applied software has been developed for the numerical implementation of the statistical mathematical models for predicting the heavy plate’s mechanical properties, and high calculation accuracy has been confirmed with the ones help: 95.82% for the yield strength, 96.78% for the ultimate tensile strength, and 91.48% for the percent elongation. The regularities of the influence for finish rolling factual temperature in the finishing stand of 3600 heavy plate mill and the plate thickness on 10Mn2VNb pipe steel physical and mechanical properties were identified by processing the database and using the designed software.

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43

Levantesi, Susanna, and Virginia Pizzorusso. "Application of Machine Learning to Mortality Modeling and Forecasting." Risks 7, no. 1 (February 26, 2019): 26. http://dx.doi.org/10.3390/risks7010026.

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Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.

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44

Alexopoulos, Angelos, Petros Dellaportas, and Jonathan J. Forster. "Bayesian forecasting of mortality rates by using latent Gaussian models." Journal of the Royal Statistical Society: Series A (Statistics in Society) 182, no. 2 (November 20, 2018): 689–711. http://dx.doi.org/10.1111/rssa.12422.

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Doukhan, P., D. Pommeret, J. Rynkiewicz, and Y. Salhi. "A class of random field memory models for mortality forecasting." Insurance: Mathematics and Economics 77 (November 2017): 97–110. http://dx.doi.org/10.1016/j.insmatheco.2017.08.010.

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Erbas, Bircan, Rob J. Hyndman, and Dorota M. Gertig. "Forecasting age-specific breast cancer mortality using functional data models." Statistics in Medicine 26, no. 2 (2006): 458–70. http://dx.doi.org/10.1002/sim.2306.

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Wang, Chou-Wen, Jinggong Zhang, and Wenjun Zhu. "NEIGHBOURING PREDICTION FOR MORTALITY." ASTIN Bulletin 51, no. 3 (May 12, 2021): 689–718. http://dx.doi.org/10.1017/asb.2021.13.

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AbstractWe propose a new neighbouring prediction model for mortality forecasting. For each mortality rate at age x in year t, mx,t, we construct an image of neighbourhood mortality data around mx,t, that is, Ꜫmx,t (x1, x2, s), which includes mortality information for ages in [x-x1, x+x2], lagging k years (1 ≤ k ≤ s). Combined with the deep learning model – convolutional neural network, this framework is able to capture the intricate nonlinear structure in the mortality data: the neighbourhood effect, which can go beyond the directions of period, age, and cohort as in classic mortality models. By performing an extensive empirical analysis on all the 41 countries and regions in the Human Mortality Database, we find that the proposed models achieve superior forecasting performance. This framework can be further enhanced to capture the patterns and interactions between multiple populations.

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Li, Han, and Colin O’Hare. "Mortality Forecasting: How Far Back Should We Look in Time?" Risks 7, no. 1 (February 22, 2019): 22. http://dx.doi.org/10.3390/risks7010022.

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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.

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Сметанников, Yu Smetannikov, Коптев, N. Koptev, Зайцев, V. Zaytsev, Лукашев, and E. Lukashev. "Mathematical Forecasting Model of Water Sources’ Ecological Safety." Safety in Technosphere 2, no. 3 (June 25, 2013): 41–45. http://dx.doi.org/10.12737/450.

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Mathematical modeling techniques of self-cleaning separate mechanisms, and also the models, which consider the impact of toxicants on the course of biological processes are offered. The functions reflecting a system reaction nature on internal and external factors are received. The obtained results qualitatively correctly describe the "biomass – resource" system evolution, which allows during comparison of model equations’ solutions and experimental data to calculate the kinetic constants of model.

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Flici, Farid. "Coherent Mortality Forecasting for the Algerian Population." Assurances et gestion des risques 87, no. 3-4 (March 31, 2021): 209–31. http://dx.doi.org/10.7202/1076125ar.

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Mortality forecasting is much needed for population projections and actuarial calculations. Forecasting mortality of males and females in an independent way leads in most of cases to some incoherence regarding the expected male-female mortality evolution. To avoid a possible unrealistic convergence/divergence in this sense, a coherent mortality forecasting is required. In this paper, we compare the performance of two coherent models, namely the model of Li and Lee (2005) and that of Hyndman et al. (2013) on forecasting male and female mortality of the Algerian population. Results show that the first model provides better goodness-of-fit but less coherence compared to the second one.

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

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