scholarly journals ENGINEERING MATHEMATICAL MODELLING OF CORONA VIRUS (COVID-19) TRANSMISSION IN IRAQ

2021 ◽  
Vol 9 (3) ◽  
pp. 01-14
Author(s):  
Emad Kamil Hussein ◽  
Tayser Sumer Gaaz ◽  
Kussay Ahmed Subhi ◽  
Samir Ghouali ◽  
Mohammed Seghir Guellil
Keyword(s):  
Mathematical Model ◽  
Mathematical Modelling ◽  
Sir Model ◽  
Epidemic Disease ◽  
Reproduction Ratio ◽  
Model Based ◽  
Corona Virus ◽  
Specific Factors ◽  
Novel Virus

Purpose: As a result of a sudden spreading of an epidemic novel virus, scientifically named COVID-19, this paper has been done to present a contribution towards fighting this virus in Iraq.  Methodology: This investigation is focusing on constructing an engineering mathematical model based on the Suspected, Infected, and Recovered model (SIR), given by Kermack and McKendrick.  Main Findings: Iraqi people are facing and suffering from this COVID-19. Three governorates occupying the locally highest infection levels, plus the world's highest deaths to infected cases ratio of about 11%, are Baghdad, Sulaimani, and Karbala.  Implications: It is showed that the Reproduction ratio R0)K is positive (greater than 1) in the three nominated zones, which means that the epidemic disease will keep spreading in a broad manner and depending on many specific factors. Many effective recommendations are presented to avoid spreading this novel virus via many techniques.  Novelty: SIR model is used to assess epidemic levels in 3 zones. 

scholarly journals Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ting Liu ◽  
Yanling Bai ◽  
Mingmei Du ◽  
Yueming Gao ◽  
Yunxi Liu
Keyword(s):  
Mathematical Model ◽  
Deep Learning ◽  
Infection Control ◽  
Model Fitting ◽  
Sir Model ◽  
Hospital Infection ◽  
Model Based ◽  
Hospital Infection Control ◽  
Stochastic Sir ◽  
Novel Coronavirus

Objective. This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia. Methods. First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) model fitting to determine the estimated value of the COVID-19 removal intensity β, which was then used to do a determined SIR model and a stochastic SIR model fitting for the hospital. In addition, the reasonable β and γ estimates of the hospital were determined, and the spread of the epidemic in hospital was simulated, to discuss the impact of basal reproductive number changes, isolation, vaccination, and so forth on COVID-19. Results. There was a certain gap between the fitting of SIR to the remover and the actual data. The fitting of the number of infections was accurate. The growth rate of the number of infections decreased after measures, such as isolation, were taken. The effect of herd immunity was achieved after the overall immunity reached 70.9%. Conclusion. The SIR model based on deep learning and the stochastic SIR fitting model were accurate in judging the development trend of the epidemic, which can provide basis and reference for hospital epidemic infection control.

Optimal control strategy for cancer remission using combinatorial therapy: A mathematical model-based approach

2021 ◽  
Vol 145 ◽  
pp. 110789
Author(s):  
Parthasakha Das ◽  
Samhita Das ◽  
Pritha Das ◽  
Fathalla A. Rihan ◽  
Muhammet Uzuntarla ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Control Strategy ◽  
Optimal Control Strategy ◽  
Combinatorial Therapy ◽  
Model Based ◽  
Cancer Remission

A note on design-based versus model-based variance estimation in stereology

2002 ◽  
Vol 34 (03) ◽  
pp. 484-490 ◽  
Author(s):  
Asger Hobolth ◽  
Eva B. Vedel Jensen
Keyword(s):  
Mathematical Model ◽  
Original Problem ◽  
Variance Estimation ◽  
Point Of View ◽  
Systematic Sampling ◽  
Variance Estimator ◽  
Shape Model ◽  
Model Based ◽  
Versus Model ◽  
Special Case

Recently, systematic sampling on the circle and the sphere has been studied by Gual-Arnau and Cruz-Orive (2000) from a design-based point of view. In this note, it is shown that their mathematical model for the covariogram is, in a model-based statistical setting, a special case of the p-order shape model suggested by Hobolth, Pedersen and Jensen (2000) and Hobolth, Kent and Dryden (2002) for planar objects without landmarks. Benefits of this observation include an alternative variance estimator, applicable in the original problem of systematic sampling. In a wider perspective, the paper contributes to the discussion concerning design-based versus model-based stereology.

scholarly journals 27 years of the HIV epidemic amongst men having sex with men in the Netherlands: An in depth mathematical model-based analysis

Epidemics ◽  
2010 ◽  
Vol 2 (2) ◽  
pp. 66-79 ◽  
Author(s):  
Daniela Bezemer ◽  
Frank de Wolf ◽  
Maarten C. Boerlijst ◽  
Ard van Sighem ◽  
T. Deirdre Hollingsworth ◽  
...  
Keyword(s):  
Mathematical Model ◽  
The Netherlands ◽  
Model Based ◽  
Hiv Epidemic ◽  
Sex With Men ◽  
Model Based Analysis

Fitting Spherical Surface and Evaluating Sphericity Error Based on the Minimum Zone Principle

2014 ◽  
Vol 687-691 ◽  
pp. 1373-1376 ◽  
Author(s):  
Lei Zhang ◽  
Li Li Liu ◽  
Chuan Hui Huang ◽  
Xing Hua Lu ◽  
Gen Sun
Keyword(s):  
Mathematical Model ◽  
Spherical Surface ◽  
Least Square ◽  
Model Based ◽  
Minimum Zone

To address the fitting spherical surface and evaluating sphericity error, a mathematical model based on the minimum zone principle is presented. And the presented model is answered by GA. An example shows the performance of the proposed method by comparison with the methods based on the least square principle.

Mathematical Modelling the Process of Cup Wheel Precision Grinding of Rotating Concave Paraboloid

2016 ◽  
Vol 693 ◽  
pp. 837-842
Author(s):  
Fu Yi Xia ◽  
Li Ming Xu ◽  
De Jin Hu
Keyword(s):  
Mathematical Model ◽  
Mathematical Modelling ◽  
Abrasive Particle ◽  
Influence Factors ◽  
Machining Process ◽  
Quadric Surface ◽  
Precision Grinding ◽  
Cup Wheel ◽  
The Mathematical Model ◽  
Trajectory Equation

A novel principle of cup wheel grinding of rotating concave quadric surface was proposed. The mathematical model of machining process was established to prove the feasibility of precision grinding of rotating concave paraboloid based on the introduced principle. The conditions of non-interference grinding of concave paraboloid were mathematically derived. The processing range and its influence factors were discussed. The trajectory equation of abrasive particle was concluded. Finally, the math expressions of numerical controlled parameters was put forward in the process of grinding of the concave paraboloid.

Robotic chemotherapy compounding: A multicenter productivity approach

2021 ◽  
pp. 107815522199284
Author(s):  
Ana C Riestra ◽  
Carmen López-Cabezas ◽  
Marion Jobard ◽  
Mertxe Campo ◽  
María J Tamés ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Correlation Analysis ◽  
Hospital Pharmacy ◽  
Multiple Regression Model ◽  
Key Factors ◽  
Independent Variables ◽  
Specific Factors ◽  
Automated Process ◽  
Productivity Indicators ◽  
The Mathematical Model

Introduction The aim of this study is to compare productivity of the KIRO Oncology compounding robot in three hospital pharmacy departments and identify the key factors to predict and optimize automatic compounding time. Methods The study was conducted in three hospitals. Each hospital compounding workload and workflow were analyzed. Data from the robotic compounding cycles from August 2017 to July 2018 were retrospectively obtained. Nine cycle specific parameters and five productivity indicators were analysed in each site. One-to-one differences between hospitals were evaluated. Next, a correlation analysis between cycle specific factors and productivity indicators was conducted; the factors presenting a highest correlation to automatic compounding time were used to develop a multiple regression model (afterwards validated) to predict the automatic compounding time. Results A total of 2795 cycles (16367 preparations) were analysed. Automatic compounding time showed a relevant positive correlation (ǀrs|>0.40) with the number of preparations, number of vials and total volume per cycle. Therefore, these cycle specific parameters were chosen as independent variables for the mathematical model. Considering cycles lasting 40 minutes or less, predictability of the model was high for all three hospitals (R2:0.81; 0.79; 0.72). Conclusion Workflow differences have a remarkable incidence in the global productivity of the automated process. Total volume dosed for all preparations in a cycle is one of the variables with greater influence in automatic compounding time. Algorithms to predict automatic compounding time can be useful to help users in order to plan the cycles launched in KIRO Oncology.

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