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You searched for subject:(epidemiologic modeling). Showing records 1 – 2 of 2 total matches.

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Colorado State University

1. Reeves, Aaron. Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry.

Degree: PhD, Clinical Sciences, 2012, Colorado State University

Epidemiologic modeling is an increasingly common method of estimating the potential impact of outbreaks of highly contagious diseases, such as foot-and-mouth disease (FMD) and highly pathogenic avian influenza (HPAI), in populations of domesticated animals. Disease models are also used to inform policy decisions regarding disease control methods and outbreak response plans, to estimate the possible magnitude of an outbreak, and to estimate the resources needed for outbreak response. Although disease models are computationally sophisticated, the quality of the results of modeling studies depends on the quality and accuracy of the data on which they are based, and on the conceptual soundness and validity of the models themselves. For such models to be credibly applied, they should realistically represent the systems they are intended to reflect, should be based to as great an extent as possible on valid data, and should be subjected to careful and ongoing scrutiny. Two key steps in the evaluation of epidemiologic models are model verification and model validation. Verification is the demonstration that a computer-driven model is operating correctly, and conforms to its intended design. Validation refers to the process of determining how well a model corresponds to the system that it intended to represent. For a veterinary epidemiologic model, validation would address issues such as how well the model represents the dynamics of the disease in question in a population to which the model is applied, and how well the model represents the application of different measures for disease control. Among the steps that can be taken by epidemiologic modelers to facilitate the processes of model verification and validation are to clearly state the purpose, assumptions, and limitations of a model; to provide a detailed description of the conceptual model for use by everyone who might be tasked with evaluation of a model; document steps already taken to test the model; and thoroughly describe the data sources and the process used to produce model input parameters from data. The realistic representation of the dynamics of spread of disease within individual herds or flocks can have important implications for disease detection and surveillance, as well as for disease transmission between herds or flocks. We have developed a simulation model of within-unit (within-herd or within-flock) disease spread that operates at the level of the individual animal, and fully incorporates sources of individual-level variation such as variability in the durations of incubating and infectious periods, the stochastic nature of disease spread among individuals, and the effects of vaccination. We describe this stochastic model, along with the processes employed for verification and validation. The incorporation of this approach to modeling of within-unit disease dynamics into models of between-unit disease spread should improve the utility of these models for emergency preparedness and response planning by making it possible to assess the… Advisors/Committee Members: Salman, M. D. (advisor), Hill, Ashley E. (advisor), Keefe, Thomas J. (committee member), Wagner, Bruce A. (committee member).

Subjects/Keywords: epidemiologic modeling; foot-and-mouth disease; highly pathogenic avian influenza; simulation modeling; stochastic simulation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Reeves, A. (2012). Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/71580

Chicago Manual of Style (16th Edition):

Reeves, Aaron. “Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry.” 2012. Doctoral Dissertation, Colorado State University. Accessed April 16, 2021. http://hdl.handle.net/10217/71580.

MLA Handbook (7th Edition):

Reeves, Aaron. “Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry.” 2012. Web. 16 Apr 2021.

Vancouver:

Reeves A. Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry. [Internet] [Doctoral dissertation]. Colorado State University; 2012. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/10217/71580.

Council of Science Editors:

Reeves A. Construction and evaluation of epidemiologic simulation models for the within- and among-unit spread and control of infectious diseases of livestock and poultry. [Doctoral Dissertation]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/71580


University of New South Wales

2. Sannapaneni, Krishnaiah. Modeling the risks of age-related eye diseases in a population in South India.

Degree: Optometry & Vision Science, 2013, University of New South Wales

The objective of this research was to determine whether an artificial intelligence methodology such as artificial neural network (ANN), a new type of predictive model offers an increased performance over a conventional logistic regression model (LR) in predicting the ranking of risk factors for irreversible age-related chronic eye diseases age-related macular degeneration (AMD), diabetic retinopathy (DR), primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) in a South Indian population. The LR and ANN models were derived and validated for their respective models predictive accuracy based on a sample (n=3,723) aged >=40 years old by using a large scale population-based epidemiologic study. Sub-population data were drawn from this sample by appropriate standard techniques that used for modeling. The LR based risk score models (RS) were derived and the model fit was assessed in a standard manner including the bootstrap method for internal validity. The ANN model was built by using the multi-layer feed-forward back propagation network. The ANN models predictive ability was compared with that of traditional model with respect to the Area under the Receiver Operating Characteristic Curve (AUROC). The sensitivity and specificity of the fitted models with a threshold criterion ranged from 70% to nearly 99% overall for all models. The ANN model outperformed the traditional LR model in a sub-population analysis in predicting AMD and DR. The predictive accuracy of ANN and LR model in predicting AMD was statistically significant (AUROC=89% vs 79%; p<0.0001). Both the models revealed that the modifiable risk factors such as heavy smoking (RS ranged from 10 to 18), lower intake of antioxidants (RS ranged from 5 to 10), hypertension (RS ranged from 2 to 10) were in order of priority predictors for AMD and longer duration of diabetes >=10 year (RS ranged from 29 to 42) was a highest priority predictor for DR. The modifiable risk factor intraocular pressure was in order of highest priority predictor for POAG and PACG. Population attributable risk percentage and population attributable fractions revealed that there is an urgent need of prioritizing modifying the modifiable factors as a public health approach. This was supported by a sensitivity analysis of the ANN model which indicated the relative importance of prioritizing modifiable risk factors on which to base preventive interventions to reduce the impact of onset or progression of these diseases. Advisors/Committee Members: Keeffe, Jill, Honorary Professor, Department of Ophthalmology, University of Melbourne, VIC 8002, Australia, Gullapalli N, Rao, Chairman, L V Prasad Eye Institute, Banjara Hills, Hyderabad - 500034, India.

Subjects/Keywords: Statistical Model Validations; Neural Network Modeling; Ophthalmology; Epidemiologic Research; Population Attributable Risk; Irreversable Age-related Eye Diseases; South India

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sannapaneni, K. (2013). Modeling the risks of age-related eye diseases in a population in South India. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/52892 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11570/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Sannapaneni, Krishnaiah. “Modeling the risks of age-related eye diseases in a population in South India.” 2013. Doctoral Dissertation, University of New South Wales. Accessed April 16, 2021. http://handle.unsw.edu.au/1959.4/52892 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11570/SOURCE01?view=true.

MLA Handbook (7th Edition):

Sannapaneni, Krishnaiah. “Modeling the risks of age-related eye diseases in a population in South India.” 2013. Web. 16 Apr 2021.

Vancouver:

Sannapaneni K. Modeling the risks of age-related eye diseases in a population in South India. [Internet] [Doctoral dissertation]. University of New South Wales; 2013. [cited 2021 Apr 16]. Available from: http://handle.unsw.edu.au/1959.4/52892 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11570/SOURCE01?view=true.

Council of Science Editors:

Sannapaneni K. Modeling the risks of age-related eye diseases in a population in South India. [Doctoral Dissertation]. University of New South Wales; 2013. Available from: http://handle.unsw.edu.au/1959.4/52892 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11570/SOURCE01?view=true

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