scholarly journals Caprine Arthritis Encephalitis Virus Disease Modelling Review

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1457
Author(s):  
Karina Brotto Rebuli ◽  
Mario Giacobini ◽  
Luigi Bertolotti
Keyword(s):  
Virus Disease ◽  
Sexual Transmission ◽  
Statistical Modelling ◽  
Dairy Production ◽  
Epidemiological Modelling ◽  
Incurable Disease ◽  
Modelling Studies ◽  
Modelling Techniques ◽  
Disease Spreading ◽  
Modelling Assumptions

Mathematical modelling is used in disease studies to assess the economical impacts of diseases, as well as to better understand the epidemiological dynamics of the biological and environmental factors that are associated with disease spreading. For an incurable disease such as Caprine Arthritis Encephalitis (CAE), this knowledge is extremely valuable. However, the application of modelling techniques to CAE disease studies has not been significantly explored in the literature. The purpose of the present work was to review the published studies, highlighting their scope, strengths and limitations, as well to provide ideas for future modelling approaches for studying CAE disease. The reviewed studies were divided into the following two major themes: Mathematical epidemiological modelling and statistical modelling. Regarding the epidemiological modelling studies, two groups of models have been addressed in the literature: With and without the sexual transmission component. Regarding the statistical modelling studies, the reviewed articles varied on modelling assumptions and goals. These studies modelled the dairy production, the CAE risk factors and the hypothesis of CAE being a risk factor for other diseases. Finally, the present work concludes with further suggestions for modelling studies on CAE.

scholarly journals Proof and Uncertainty in Causal Claims

2018 ◽  
Vol 5 (2) ◽  
pp. 72-89
Author(s):  
Martine Jayne Barons ◽  
Rachel L Wilkerson
Keyword(s):  
Graphical Models ◽  
Mathematical Proof ◽  
Statistical Modelling ◽  
Causal Mechanisms ◽  
Science Domain ◽  
History Of ◽  
New Methodologies ◽  
Mathematical Sciences ◽  
Modelling Assumptions ◽  
Causal Theories

Causal questions drive scientific enquiry. From Hume to Granger, and Rubin to Pearl the history of science is full of examples of scientists testing new theories in an effort to uncover causal mechanisms. The difficulty of drawing causal conclusions from observational data has prompted developments in new methodologies, most notably in the area of graphical models. We explore the relationship between existing theories about causal mechanisms in a social science domain, new mathematical and statistical modelling methods, the role of mathematical proof and the importance of accounting for uncertainty. We show that, while the mathematical sciences rely on their modelling assumptions, dialogue with the social sciences calls for continual extension of these models. We show how changing model assumptions lead to innovative causal structures and more nuanced casual explanations. We review differing techniques for determining cause in different disciplines using causal theories from psychology, medicine, and economics.

Use of Statistical Modelling Techniques for Simulation

1993 ◽  
Author(s):  
Ebrahim Mazharsolook ◽  
David C. Robinson ◽  
Jonathan D. Casey
Keyword(s):  
Quality Control ◽  
Statistical Modelling ◽  
Test Results ◽  
Final Test ◽  
Product Model ◽  
Design Changes ◽  
Discrete Manufacturing ◽  
Similar Product ◽  
Modelling Techniques

Abstract Statistical methods are explored for the use in modelling of discrete manufacturing. The developed methodologies based on Design of Experiments (DOE) and stepwise regression to obtain the product model are described. This model is then embedded within a software system which is used for simulation of design changes, process changes and disturbances. The software is used to predict final test results in respect of up-stream parameter changes. A case study is presented o show the implementation of this method of modelling in Quality Control of manufacture. This case study has successfully been implemented. The system is currently assisting the company in design of similar product. Feasibility of applying Artificial Intelligen (AI) techniques to Model-Based Quality Control (MBQC) is investigated. An outline of the future development of Hybrid MBQC is then presented.

scholarly journals Reflections on modern methods: Statistics education beyond ‘significance’: novel plain English interpretations to deepen understanding of statistics and to steer away from misinterpretations

2020 ◽  
Author(s):  
Hilary C Watt
Keyword(s):  
Statistics Education ◽  
Statistical Modelling ◽  
Study Data ◽  
Statistical Interpretation ◽  
P Values ◽  
Experience Teaching ◽  
Random Samples ◽  
The Common ◽  
Modelling Assumptions ◽  
Study Participants

Abstract Concerns have been expressed over standards of statistical interpretation. Results with P <0.05 are often referred to as ‘significant’ which, in plain English, implies important. This leads some people directly into the misconception that this provides proof that associations are clinically relevant. There are calls for statistics educators to respond to these concerns. This article provides novel plain English interpretations that are designed to deepen understanding. Experience teaching postgraduates at Imperial College is discussed. A key issue with focusing on ‘significance’ is the common inappropriate practice of implying no association exists, simply because P >0.05. Referring to strengths of association in ‘study participants’ gives them gravitas, which may help to avoid this. This contrasts with the common practice of focusing on imprecision, by referring to the ‘sample’ and to ‘point estimates’. Unlike formal statistical definitions, interpretations developed and presented here are rooted in the application of statistics. They are based on one set of study participants (not many random samples). Precision of strengths of association are based on using strengths in study participants to estimate strengths of association in the population (from which participants were selected by probability random sampling). Reference to ‘compatibility with study data, dependent on statistical modelling assumptions’ reminds us of the importance of data quality and modelling assumptions. A straightforward graph shows the relationship between P-values and test statistics. This figure and associated interpretations were developed to illuminate the continuous nature of P-values. This is designed to discourage focus on whether P <0.05, and encourage interpretation of exact P-values.

Reserving for catastrophe reinsurance

1994 ◽  
Vol 121 (1) ◽  
pp. 135-160 ◽  
Author(s):  
D. H. Craighead
Keyword(s):  
Normal Development ◽  
Statistical Modelling ◽  
Development Pattern ◽  
Catastrophe Losses ◽  
Modelling Techniques ◽  
Oil Rig ◽  
Catastrophe Reinsurance ◽  
A Company

AbstractThe paper sets out the method required to be followed when estimating reserves for a Company or a Lloyd's Syndicate which has accepted reinsurance treaties that have given rise to catastrophe losses, sufficiently large to upset the normal development pattern and to affect the gross account quite differently from the net account. The losses may be caused by single factors such as aircraft crashes or oil rig disasters, or by the aggregation of claims resulting from a windstorm or an earthquake. The paper discusses two possible approaches to estimation of the gross losses; via exposure totals or via statistical modelling techniques.

scholarly journals A systematic review of early modelling studies of Ebola virus disease in West Africa – CORRIGENDUM

2017 ◽  
Vol 145 (9) ◽  
pp. 1961-1961
Author(s):  
Z. S. Y. WONG ◽  
C. M. BUI ◽  
A. A. CHUGHTAI ◽  
C. R. MACINTYRE
Keyword(s):  
Systematic Review ◽  
West Africa ◽  
Ebola Virus ◽  
Virus Disease ◽  
Ebola Virus Disease ◽  
Modelling Studies

Statistical Modelling Techniques

Biometrics ◽  
1982 ◽  
Vol 38 (3) ◽  
pp. 871
Author(s):  
Paul Davies ◽  
S. S. Shapiro ◽  
A. J. Gross
Keyword(s):  
Statistical Modelling ◽  
Modelling Techniques
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