scholarly journals Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Yolanda Guerrero Sánchez
Keyword(s):  
Infectious Disease ◽  
Disease Model ◽  
Mean Square ◽  
Artificial Neuron ◽  
Infectious Disease Model ◽  
Neuron Networks ◽  
Levenberg Marquardt ◽  
The Mean ◽  
Predator Model ◽  
Infected Prey

The aim of the current work is to perform the numerical investigation of the infectious disease based on the nonlinear fractional order prey-predator model using the Levenberg–Marquardt backpropagation (LMB) based on the artificial neuron networks (ANNs), i.e., LMBNNs. The fractional prey-predator model is classified into three categories, the densities of the susceptible, infected prey, and predator populations. The statistics proportions for solving three different variations of the infectious disease based on the fractional prey-predator model are designated for training 80% and 10% for both validation and testing. The numerical actions are performed using the LMBNNs to solve the infectious disease based on the fractional prey-predator model, and comparison is performed using the database Adams–Bashforth–Moulton approach. The infectious disease based on the fractional prey-predator model is solved using the LMBNNs to reduce the mean square error (M.S.E). In order to validate the exactness, capability, consistency, and competence of the proposed LMBNNs, the numerical procedures using the correlation, M.S.E, regression, and error histograms are drawn.

scholarly journals Numerical Investigations through ANNs for Solving COVID-19 Model

2021 ◽  
Vol 18 (22) ◽  
pp. 12192
Author(s):  
Muhammad Umar ◽  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Shumaila Javeed ◽  
Hijaz Ahmad ◽  
...  
Keyword(s):  
Numerical Solutions ◽  
Mean Square ◽  
Reference Dataset ◽  
Artificial Neuron ◽  
Numerical Investigations ◽  
Neuron Networks ◽  
Spreading Model ◽  
Sample Data ◽  
Levenberg Marquardt ◽  
Solution Dynamics

The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.

scholarly journals Obesity as an "infectious" disease

2021 ◽  
Vol 11 (9) ◽  
pp. 534-537
Author(s):  
Daria Żuraw ◽  
Paulina Oleksa ◽  
Mateusz Sobczyk
Keyword(s):  
Infectious Disease ◽  
Disease Model ◽  
Living Environment ◽  
Social Contagion ◽  
Social Discrimination ◽  
Obese People ◽  
Obesity Epidemic ◽  
Global Epidemic ◽  
Infectious Disease Model ◽  
The One

Introduction: Obesity has been recognized as a global epidemic by the WHO, followed by a wealth of empirical evidence supporting its contagiousness. However, the dynamics of the spread of obesity between individuals are rarely studied.  A distinguishing feature of the obesity epidemic is that it is driven by a process of social contagion that cannot be perfectly described by the infectious disease model. There is also social discrimination in the obesity epidemic. Social discrimination against obese people plays quite different roles in two cases: on the one hand, when obesity cannot be eliminated, social discrimination can reduce the number of obese people; on the other hand, when obesity is eradicable, social discrimination can cause it to explode.(1)   Materiał and methods: A literature analysis on obesity epidemic was carried out within the Pubmed, Google scholar and Research Gate platform. The following keywords were used in serach: obesity, epidemy, children, body max index.    Purpose of the work: The aim of the following analysis is to present an obesity as an infectious disease. The steadily increasing percentage of obese people, including children, shows that there is an obesity epidemic. This is the phenomenon of social contagion, which partially explains the concept of homophily, which involves the grouping of people with similar characteristics. Potential explanations are also provided by sharing a living environment with similar access to certain foods and similar opportunities for physical activity, which defines the occurrence of analogous health habits

An Efficient Computational Numerical Approach For Nonlinear Mathematical Influenza Disease Modelling

2021 ◽  
Author(s):  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Mohamed R. Ali ◽  
Adnène Arbi ◽  
Muhammad Kristiawan
Keyword(s):  
Numerical Scheme ◽  
Reference Data ◽  
Disease Model ◽  
Numerical Approach ◽  
Disease Modelling ◽  
Mean Square ◽  
Levenberg Marquardt ◽  
Effectiveness And Efficiency ◽  
Comparison Of The Results ◽  
And Training

Abstract In this study, an advanced computational numerical scheme based on the Levenberg-Marquardt backpropagation (LMB) neural network (NN) process, i.e., LMB-NN is presented for solving the nonlinear mathematical influenza disease model. The nonlinear mathematical influenza disease model depends on four categories named susceptible S(t), infected I(t), recovered R(t) and cross-immune individuals proportion C(t). Six different cases of the nonlinear mathematical influenza disease model have been numerically treated using the LMB-NN process and the comparison of the results has been presented by using the reference data-based solutions designed based on the Adams results. The numerically obtained results of the nonlinear mathematical influenza disease model using the verification, testing, and training procedures are calculated using the LMB-NN process to reduce the functions of mean square error (MSE). For the correctness, competence, effectiveness, and efficiency of the LMB-NN process, the proportional and analysis methods are performed using the analysis of correlation, MSE results, error histograms and regression.

Improving Computational Models and Practices

2012 ◽  
pp. 432-455
Author(s):  
Iain Barrass ◽  
Joanna Leng
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Computational Models ◽  
Disease Model ◽  
Significant Risk ◽  
Epidemiological Studies ◽  
Health Interventions ◽  
Computational Steering ◽  
Infectious Disease Model

Since infectious diseases pose a significant risk to human health many countries aim to control their spread. Public health bodies faced with a disease threat must understand the disease’s progression and its transmission process. From this understanding it is possible to evaluate public health interventions intended to decrease impacts on the population. Commonly, contingency planning has been achieved through epidemiological studies and the use of relatively simple models. However, computational methods increasingly allow more complex, and potentially more realistic, simulations of various scenarios of the control of the spread of disease. However, understanding computational results from more sophisticated models can pose considerable challenges. A case study of a system combining a complex infectious disease model with interactive visualization and computational steering tools shows some of the opportunities this approach offers to infectious disease control.

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