Mathematical Forecasting Model Research Articles

IBC-Oxford University2023_BJSPoster_16The burden of emergency re-operations post bariatric surgery: experience from a tertiary centre

Abstract Background Although bariatric surgery is available through NHS, lack of funding and long waiting times, lead to patients choosing private care within UK or abroad. This study presents data on emergency re-interventions post bariatric surgery (UK and abroad) at a high-volume centre in UK performing between 330-450 bariatric resections/year. Methods Data on all patients undergoing emergency intervention following bariatric surgery between Jan 2018 and Nov 2022 were collected. Results A total of 155 patients underwent 201 procedures after having had prior bariatric surgery of whom 79.7% were female. The median age was 43 (IQR 35-50 years) and the median BMI was 35 (IQR 28-45). Roux-En-Y Gastric Bypass (RYGB) and Gastric Band (GB) were the commonest index operations (40% and 24.5% respectively) and 22% had their index procedure outside of the UK. The commonest emergency interventions were OGD (n=13) and re-do jejuno-jejunal anastomosis(n=9). The median length of stay was 4 days (IQR 2-13) with mortality of 2%. Per year, the median number of patients admitted as an emergency was 34 (19-45). However, temporal regression analysis forecasts that admissions will steadily rise to 58 (95% CI 46-76) in 2023, 66 (95% CI 54-79) in 2024 and 75 (95% CI 62-88) in 2025 (p=0.013). Conclusions An increasing number of patients are attending our hospital having had surgery in either another unit or abroad. Mathematical forecasting models suggest that the number of patients will likely increase every year in the near future. With increasing demand, emphasis falls on the importance of established regional networks.

Municipal emission pathways and economic performance toward net-zero emissions: A case study of Nakhon Ratchasima municipality, Thailand

The transition to net-zero emissions (NZEs) in developing countries is challenging and requires the immediate adoption of comprehensive climate policy packages, strong collaboration among all sectors and stakeholders, and timely financial and technological assistance for developing economies. This research aims to analyze and evaluate the pathways to realize an NZE scheme at the municipality level. Nakhon Ratchasima (NR) Municipality, Thailand, is selected as the case study for this research. The Global Protocol for Community-Scale GHG Emission Inventories (GPC) is applied as the robust framework to assess the city’s GHG emission profile. A mathematical forecasting model and the participatory multicriteria decision-making (MCDM) approach were adopted to support evidence-based local climate action planning based on four different scenarios: the business-as-usual (BAU), nationally determined contribution (NDC), carbon neutrality (CN), and NZE scenarios. The roles of stakeholders at the local community level across all sectors in mitigation actions and investment costs were investigated, and cost-effectiveness was evaluated to understand the economic performance of the adoption and implementation of local climate policy packages. The results indicate that by employing solely conventional technologies, a residential city that is also a hub for trade and land transportation will be unable to achieve its net-zero targets. It is imperative to seek additional low-carbon businesses and decarbonizing technologies that accompany substantial investments. According to the case of NR Municipality, the implementation costs to attain the NZE target by 2050 would range between 974.40 and 4.131.96 million USD. A pivotal driver of the municipal NZE pathway is the successful mobilization private sector investments to propel the transition toward climate-friendly technologies. Cost-effectiveness analysis significantly bolsters the municipality’s transitional plan preparation, holistically encompassing economic, social, and environmental considerations. By preparing these aspects together, we ensure a smooth and equitable transition to net zero, avoid conflicts and economic harm and leave no one behind. This approach ensures a harmonious balance between a net-zero future, economic growth, and environmentally friendly living for all.

Analyzing the impact of Russia-Ukraine crisis on the Indian economy using input-output modeling

PurposeThe conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.Design/methodology/approachThis research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.FindingsThis study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.Research limitations/implicationsIn future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.Originality/valueRussia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

Finding the Best Mathematical Forecasting Model for Ordering Non FMCG Items at an FMCG Retailer

The future of a company can be judged by precise planning and predictions. This is helpful to steer clear of hypothetical product reserves. Also, it supports to create a good schedule for production to attain demand of customers and various other factors of market. Similarly, the forecasting can help non-FMCG companies. The current research possesses tools, organization form and method that gain supply chain. Moreover, it aims on products that are expired, damages, and come back from customer. Without question, return product had huge influence on income and deficit. Consequently, this trouble can be solved by assessment of supply chain. By set up through various important parameters and by modifying accounting practice. Following analysis phase, innovative models of world about returning process were examined and in design phase these items can be incorporated into alternative solution. During production life cycle, it assists in finding company performance. To know about forecasting, in present work, we have adopted a mathematical model. In this modal, the forecast is under sales estimate then the bias is considered as the negative for company. Time series method provides an ideal solution for the problem for correlated data.

Forecasting of COVID-19 incidence in Ukraine using the method of exponential smoothing.

Coronavirus infection (COVID-19) is a highly infectious disease of viral etiology. SARS-CoV-2 virus was first identified during the investigation of the outbreak of respiratory disease in Wuhan, China in December 2019. And already on March 11, 2020 COVID-19 in the world was characterized by the WHO as a pandemic. In Ukraine the situation with incidence COVID-19 remains difficult. The purpose of this study is to to develop a mathematical forecasting model for COVID-19 incidence in Ukraine using an exponential smoothing method. The article analyzes reports on basic COVID-19 incidence rates from 29.02.2019 to 01.10.2021. In order to determine the forecast levels of statistical indicators that characterize the epidemic process of COVID-19 the method of exponential smoothing was used. It is expected that from 29.02.2019 to 01.10.2021 the epidemic situation of COVID-19 incidence will stabilize. The indicator of "active patients" will range from 159.04 to 353.63 per 100 thousand people. The indicator of "hospitalized patients" can reach 15.43 and "fatalities" ‒ 1.87. The use of the method of exponential smoothing based on time series models for modeling the dynamics of COVID-19 incidence allows to develop and implement scientifically sound methods in order to prevent, quickly prepare health care institutions for hospitalization.

Mathematical model for forecasting of violations in the field of veterinary medicine using the Markov chain

Activities within the field of veterinary medicine require an efficient governmental administration, including planning, programming, accounting and result analysis, in addition to control and supervision. As a result, it is crucial to have a theoretical understanding of the new forms of interaction between executive authorities at different levels and the economic entities carrying out these activities. Therefore, the improvement of the regulations governing this field is highly required. The solution is also linked to the improvement of the current legislation governing socio-economic processes, using a new reliable model. In this context, the main goal of this study is to develop proposals to improve the current legal regulation of administrative infractions in the field of veterinary medicine. The authors of this paper applied a multi-agent approach to identify groups of law subjects. They also developed a mathematical model using Markov chains to predict infractions in the field of veterinary medicine. The model is formed of “transition matrices” and takes into account the various reaction strategies of regulatory authorities. Such a strategy shows the likelihood of committing infractions and can form a basis for reforming the current legislation ruling the conduct of control and surveillance procedures using a risk-based approach.

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Risk factors that determine the severity of bronchial obstruction syndrome of acute respiratory viral infections in children

The article deals the possibilities of using early clinical and anamnestic data for the diagnosis of severe bronchial obstruction in children.The aim of the study was to search for informative indicators that affect the severity of bronchial obstruction against the background of acute respiratory viral infections, and to develop a mathematical predictive model for use at the stages of hospitalization.Materials and methods. The study was based on the examination and observation of 386 children aged 1 month to 5 years who were treated in the respiratory department and the intensive care unit with a diagnosis of bronchial obstruction syndrome. To search for informative indicators, 34 anamnestic signs were studied, including those in mother-child pairs. Comparison of indicators was carried out according to Student's t-test for independent samples and у 2 test to assess categorical differences. To assess the effect of each independent variable on the outcomes of interest, variables reaching P ≤ 0.25 in bivariate analysis were included in a multivariate logistic regression model.Results. Mathematical processing showed that only 1 9 factors were identified as significantly influencing the severity of the disease (p < 0.001), while others were not potential determinants of severity. For the main ones, diagnostic values were calculated and a mathematical forecasting model was built with a high level of reliability — 95%.

A fractional order SITR mathematical model for forecasting of transmission of COVID-19 of India with lockdown effect

In this paper, we consider a mathematical model to explain, understanding, and to forecast the outbreaks of COVID-19 in India. The model has four components leading to a system of fractional order differential equations incorporating the refuge concept to study the lockdown effect in controlling COVID-19 spread in India. We investigate the model using the concept of Caputo fractional-order derivative. The goal of this model is to estimate the number of total infected, active cases, deaths, as well as recoveries from COVID-19 to control or minimize the above issues in India. The existence, uniqueness, non-negativity, and boundedness of the solutions are established. In addition, the local and global asymptotic stability of the equilibrium points of the fractional-order system and the basic reproduction number are studied for understanding and prediction of the transmission of COVID-19 in India. The next step is to carry out sensitivity analysis to find out which parameter is the most dominant to affect the disease’s endemicity. The results reveal that the parameters η,μ and ρ are the most dominant sensitivity indices towards the basic reproductive number. A numerical illustration is presented via computer simulations using MATLAB to show a realistic point of view.

Prediction Modelling of Cold Chain Logistics Demand Based on Data Mining Algorithm

Modern information technologies such as big data and cloud computing are increasingly important and widely applied in engineering and management. In terms of cold chain logistics, data mining also exerts positive effects on it. Specifically, accurate prediction of cold chain logistics demand is conducive to optimizing management processes as well as improving management efficiency, which is the main purpose of this research. In this paper, we analyze the existing problems related to cold chain logistics in the context of Chinese market, especially the aspect of demand prediction. Then, we conduct the mathematical calculation based on the neural network algorithm and grey prediction. Two forecasting models are constructed with the data from 2013 to 2019 by R program 4.0.2, aiming to explore the cold chain logistics demand. According to the results estimated by the two models, we find that both of models show high accuracy. In particular, the prediction of neural network algorithm model is closer to the actual value with smaller errors. Therefore, it is better to consider the neural network algorithm as the first choice when constructing the mathematical forecasting model to predict the demand of cold chain logistic, which provides a more accurate reference for the strategic deployment of logistics management such as optimizing automation and innovation in cold chain processes to adapt to the trend.

РОЗРОБЛЕННЯ МОДЕЛЕЙ ПРОГНОЗУВАННЯ ОБСЯГУ РЕАЛІЗАЦІЇ ПРОДУКЦІЇ СІЛЬСЬКОГОСПОДАРСЬКОГО ПІДПРИЄМСТВА

The financial and economic activities of economic entities in modern terms require constant plans comparison with real developments. Taking into account the peculiarities of the economic situation in the country, comparing the supply and demand, analysis of financial results, etc. The development of market relations increases the responsibility and independence of enterprises in making decisions about strategy and tactics for the future. That is why, while taking substantiating management decisions the application of techniques and methods of economic and mathematical modeling is relevant. One of the types of communication between the producer and the consumer is sales volume. It directly affects the amount of costs, profits and profitability of the enterprise. Therefore, the analysis and forecasting of such an indicator is important to increase the competitiveness of economic entities. Statistical data of one of the agricultural enterprises of Poltava region are analyzed, several influential factors are highlighted. It is proposed to perform a regression analysis, develop a linear multifactor model and build an adaptive model for forecasting the volume of sales of agricultural enterprises to select the optimal model for forecasting the studied indicator for the future. The author performed calculations on the obtained models. The analysis of the obtained results confirmed model’s adequacy, so they can be used for forecasting. As a result of comparison forecasts of sales volumes at the enterprise the adaptive model is recommended for practical application as it has the best quality of the forecast and the minimum sum of deviations squares. This model giving more weight to the latest statistics provides an optimistic forecast for the future. Thus, developing economic and mathematical forecasting models will provide an opportunity to improve the operational planning of production the certain types of products, as well as allow (after receiving new statistics) to quickly change the production monthly plans. Thus, conditions will be created to improve the quality of management, increase competitiveness and ensure the stabilization of the financial state at agricultural enterprises.

Моделювання робочої зони локальної RTLS-системи району надзвичайної ситуації

A mathematical model of a differential-rangefinder local positioning system has been developed, which in real time makes it possible to predict the working area of the system, taking into account the construction barriers of the emergency zone. The conditions of propagation of high-frequency signals, which determine the quality of positioning and the accuracy characteristics of the system, are taken into account. To simplify the simulation, a number of assumptions were made regarding the parameters of the obstacles and the conditions of radio wave propagation, which make it possible to simplify the prediction process. On the basis of the geometric criterion and the criterion of maximum removal, a modified geometry coefficient (zone coefficient) was obtained, which is proposed to be used as the basis for a mathematical forecasting model. Using this criterion, a computational algorithm and a program for operational forecasting of the working area of local positioning have been developed, which make it possible to take into account the influence of the number of beacons, the geometry of the system and the location of building barriers to the propagation of radio signals on the shape of the working area. In the process of modeling, both geometric and general physical laws of the formation of the field of radio navigation support were taken into account. A study of the operation of the modeling system was carried out in the presence of several radio beacons, in the absence and in the presence of several construction barriers within the emergency zone. The developed mathematical model makes it possible to calculate the size of the positioning zones with the determination of the boundary conditions for the reliability and accuracy of the navigation support of rescuers. Taking into account the process of predicting the impact of obstacles in the emergency zone on the type and size of the working area of the positioning system allows the head of emergency response to make the right management decision, ensure safe working conditions for rescuers and optimize emergency response.

An investigation of three mathematical models on the COVID-19 pandemic: the case of Turkey

Mathematical modelling plays a major role in assessing, controlling, and forecasting potential outbreaks. This article consists of implementing mathematical forecasting models by Liang [14], Ma [16], and the curve fitting method with the least squares as a standard approach in regression analysis and compares those calculate results obtained from the Covid-19 outbreak data, which is announced by the Turkish Ministry of Health, between March 10, 2020, and May 15, 2020, in Turkey. For this purpose, we presented an overview of three mathematical models and finally, demonstrated their applications using the Ministry of Health of Turkey’s publicly reported data.

Marine Economic Prediction Based on Mathematical Models

Ma, Y.; Wang, Q.; Bai; X., and Chen, G., 2020. Marine Economic Prediction Based on Mathematical Models. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 224-227. Coconut Creek (Florida), ISSN 0749-0208.The mathematical forecasting model has the characteristics of low data requirements, but high accuracy and good effect on medium and short-term forecasts. Its application range is very wide. Based on the marine economic data from 2010 to 2014, establish the mathematics of the marine economic added value. The model was tested and verified to prove that the model was available, and the marine economic added value was predicted. By comparing the predicted value with the initial value data, it was found that the added value did not match the increase of the marine production added value. The rate of increase in value is slower than that of marine production, which shows that when attracting marine economic talents, capital, technology, etc., the corresponding marine scientific research, education, and management services are not paid enough attention. Neither the absolute amount nor the relative proportion does not match. Finally, conclusions and suggestions are given.

Using mathematical modeling in predicting the economic efficiency of lake-commercial fish farming in cross-border areas of northern Kazakhstan

On the basis of the developed mathematical forecasting model, the prospects for developing the lake commercial fish farming. Also, its effectiveness in the cross-border regions of northern Kazakhstan is considered in details.

Demographic features of patients with colorectal carcinoma based on 14 years of experience at Jordan University Hospital.

BACKGROUNDColorectal cancer (CRC) is the third most common cancer worldwide. In the West, the incidence has stabilized or decreased. There are only occasional published studies that describe the epidemiology of CRC and its changing trends in Jordan and other Middle Eastern countries.OBJECTIVESDescribe the epidemiological features of CRC, predict future trends and compare the results with those from other Arab and Middle Eastern countries and the West.DESIGNRetrospective epidemiologic study.SETTINGTertiary center, teaching hospital.PATIENTS AND METHODSA retrospective study covering 14 years (2003 to 2016). All cases of CRC were retrieved from the computerized system. Demographic data were recorded and analyzed using Mathematica 11.2 and IBM SPSS version 23 software. Mathematical grey forecasting models were used to predict future trends.MAIN OUTCOME MEASURESNumber of cases and accumulated average over time, percentages of demographic variables and results of mathematical forecasting models.SAMPLE SIZE970.RESULTSThe male-to-female ratio was 1.5:1 and 97.4% were adenocarcinomas. The accumulated mean number of diagnosed cases doubled from 44.8 between 2003 and 2007 to 82.9 from 2008 to 2016. The accumulated annual average increased beginning in 2008. The forecasting models predicted a further increase in CRC. The mean age was 60.5 years and the median 62.0. Half of the cases presented at an advanced stage (TNM stage III or IV).CONCLUSIONCRC is increasing and is expected to increase further. Better health care planning that includes education and screening is needed to reverse these rising trends and to improve early detection.LIMITATIONSSingle institution study.

Design and Decomposition of Waste Prognostic Model with Hierarchical Structures

The waste management is a dynamically progressive area, with the current trend leading to circular economy scheme. The development in this area requires quality prognosis reflecting the analysed timeframe. The forecast of the waste production and composition of waste is an important aspect with regards to the planning in waste management. However, the regular prognostic methods are not appropriate for these purposes due to short time series of historical data and unavailability of socio-economic data. The paper proposes a general approach via mathematical model for forecasting of future waste-related parameters based on spatially distributed data with hierarchical structure. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The decomposition of the model into subtasks is performed in order to simpler implementation and reasonable time solvability. The individual algorithm steps are applied to municipal waste production data in the Czech Republic.

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

City Carbon Footprint Evaluation and Forecasting Case Study: Dan Sai Municipality

In this research, City Carbon Footprint (CCF) of Dan Sai municipality was evaluated according to the Global Protocol for Community-scale Greenhouse Gas Emission Inventories (GPC) guideline. Related activity data in 2015 were collected and analyzed which presented into 3 scopes (Scope1, 2, 3). As the results, the total CCF of Dan Sai Municipality is 8,528.04 tCO2eq which contributed from scope 1, scope 2 and scope 3 of 5,524 tCO2eq, 2,164 tCO2eq, 1,140 tCO2eq, respectively. Fossil fuel combustion from industrial sub-sector in scope 1 showed the greatest contribution of 40% of the total, followed by electricity consumption in scope 2, solid waste treatment in scope 1 accounting for 25% and 13%, respectively. The data of CCF in 2015 was used as baseline (Business as Usual: BAU) in order to predict city emissions in 2030 using mathematical forecasting model. The result indicated that CCF of Dan Sai can be reach up to 11,662.39 t CO2eq (27%). Consequently, applying mitigation options to reduce the emission for Dan Sai has been proposed. It was found that implementation of reduction projects including installation of solar rooftop, composting organic waste, producing RDF from waste and convert waste to energy by using RDF hybrid ORC could reduce CCF accounting for 20%, 0.53%, 1% and 3%, respectively. This study can provide benefits and offer better solutions for maximizing the potential of low carbon city and minimizing the climate change problem issues for municipality in the near future.

USING THE ECONOMIC AND MATHEMATICAL MODELS FOR DETERMINING THE MARKET CAPACITY

The subject matter of the article is economic and mathematical forecasting models of the market capacity based on the system of influential factors. The goal is to systematize and substantiate theoretical and methodological approaches to forecasting the market capacity in the conditions of the current unstable state of the economy. The following tasks are solved: the factors that influence on the development and amount of the market capacity are completely systematized; peculiarities, advantages and disadvantages of applying available economic and mathematical forecasting models of the market capacity are considered; the necessity for taking into account the structural changes in a number of consumers by age groups is emphasized; the features of the influence of the consumption of luxury goods and goods of prime necessity were is revealed taking into account the return on the change in the market capacity. The methods used are – the system analysis, the factor analysis, the regression and correlation analysis, the methods of cumulative curves. The following results are obtained –the principles of building economic and mathematical forecasting models of the market capacity are considered; the factors that influence on the market capacity at micro and macro level and have a random or systematic nature of the impact were systematized; advantages, disadvantages and peculiarities of multi-factor and single-factor models of the market capacity, in particular, cumulative curves are revealed; compulsory registration of demographic factors (changing the structure of age groups in time) in multi-factor models is suggested; the alternative use of cumulative curves of demand on the groups of luxury goods and goods of prime necessity depending on income is shown. Conclusions. Taking into consideration structural changes in a number of consumers in time, the features of consumption of luxury goods and goods of prime necessity using the factors of the elasticity of demand on income and other significant factors enable mathematical describing the consumption of the basic groups of goods and services, forecasting the amount of their market capacity, determining the critical values of the factors that can completely stop consuming products of a certain group or reduce the consumption to the minimum level.