Relevant bibliographies by topics / Disease model

Academic literature on the topic 'Disease model'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Disease model"

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Rifki Taufik, Muhammad, Dwi Lestari, and Tri Wijayanti Septiarini. "Mathematical Model for Vaccinated Tuberculosis Disease with VEIT Model." International Journal of Modeling and Optimization 5, no. 3 (June 2015): 192–97. http://dx.doi.org/10.7763/ijmo.2015.v5.460.

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Krishnaprasath, V. T., and J. Preethi. "Finite automata model for leaf disease classification." Agricultural Economics (Zemědělská ekonomika) 67, No. 6 (June 25, 2021): 220–26. http://dx.doi.org/10.17221/70/2020-agricecon.

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In this modern era, the detection of plant disease plays a vital role in the sustainability of agricultural ecosystem. Today, India being second in farming, well-timed information related to crop is still questioning. Indian Government's farmer portal is available for pesticides, fertilisers, and farm machinery. To alleviate this problem, the paper describes a model to validate the leaf image, predicting leaf disease and notifying the farmer in an effective way on the harvest failure to stabilise farming income. For specific consideration on the validation, a data set library with predefined, uniformly scaled, regular image patterns of leaf disease, is maintained. The research suggests that farmers utilising the model can predict the breakout of leaf disease predominantly acquiring 100% yield.
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Rani, K. Sandhya, M. Sai Manoj, and G. Suguna Mani. "A Heart Disease Prediction Model using Logistic Regression." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1463–66. http://dx.doi.org/10.31142/ijtsrd11401.

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Shimohama, Shun, Hideyuki Sawada, Yoshihisa Kitamura, and Takashi Taniguchi. "Disease model: Parkinson's disease." Trends in Molecular Medicine 9, no. 8 (August 2003): 360–65. http://dx.doi.org/10.1016/s1471-4914(03)00117-5.

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Ahmad-Sabry, Mohammad H. I. "Disease Model." Medicine 94, no. 15 (April 2015): e711. http://dx.doi.org/10.1097/md.0000000000000711.

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DISHMEMA, Elfrida, and Lulezim HANELLI. "A SIR Model for Measles Disease Case for Albania." International Journal of Innovative Research in Engineering & Management 6, no. 4 (July 2019): 38–43. http://dx.doi.org/10.21276/ijirem.2019.6.4.3.

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M., Inbavalli. "Fuzzy Inference Model for Computation and Prediction of Disease Pattern." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 672–79. http://dx.doi.org/10.5373/jardcs/v12sp4/20201533.

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Subramanian, Suresh, and Y. Angeline Christobel. "A Hybrid Machine Learning Model to Predict Heart Disease Accurately." Indian Journal of Science and Technology 15, no. 12 (March 27, 2022): 527–34. http://dx.doi.org/10.17485/ijst/v15i12.104.

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Wang, Xixin, Daniëlle Copmans, and Peter A. M. de Witte. "Using Zebrafish as a Disease Model to Study Fibrotic Disease." International Journal of Molecular Sciences 22, no. 12 (June 15, 2021): 6404. http://dx.doi.org/10.3390/ijms22126404.

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In drug discovery, often animal models are used that mimic human diseases as closely as possible. These animal models can be used to address various scientific questions, such as testing and evaluation of new drugs, as well as understanding the pathogenesis of diseases. Currently, the most commonly used animal models in the field of fibrosis are rodents. Unfortunately, rodent models of fibrotic disease are costly and time-consuming to generate. In addition, present models are not very suitable for screening large compounds libraries. To overcome these limitations, there is a need for new in vivo models. Zebrafish has become an attractive animal model for preclinical studies. An expanding number of zebrafish models of human disease have been documented, for both acute and chronic diseases. A deeper understanding of the occurrence of fibrosis in zebrafish will contribute to the development of new and potentially improved animal models for drug discovery. These zebrafish models of fibrotic disease include, among others, cardiovascular disease models, liver disease models (categorized into Alcoholic Liver Diseases (ALD) and Non-Alcoholic Liver Disease (NALD)), and chronic pancreatitis models. In this review, we give a comprehensive overview of the usage of zebrafish models in fibrotic disease studies, highlighting their potential for high-throughput drug discovery and current technical challenges.
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Rani, K. Sandhya, M. Sai Chaitanya, and G. Sai Kiran. "A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1467–70. http://dx.doi.org/10.31142/ijtsrd11402.

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Dissertations / Theses on the topic "Disease model"

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Harris, Bertha J. "Veteran Administration Disease Model to an Interdisciplinary Healthcare Model." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6574.

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There is a growing need for healthcare teams within the Veterans Administration (VA) healthcare system to effectively collaborate and communicate to improve patient outcomes. The need to improve patient care in the Patient Aligned Care Team (PACT) has been well established. The scholarly literature does not provide evidence whether using the primary care PACT model on communication and teamwork by an interdisciplinary medical team ameliorates these communication breakdowns. Bronstein's design for interdisciplinary collaboration provided the overarching framework for this study. The purpose of this qualitative case study was to investigate the use of the PACT model on communication and teamwork by an interdisciplinary medical team as well as the perceived processes and results that the interdisciplinary collaborative approach has on production data. 18 participants consisted of licensed medical professionals and other licensed and non-licensed support personnel who were part of the PACT team. There were several challenges associated with the model, such as (a) a lack of clearly defined roles, (b) lack of communication and collaboration, and (c) division between the clerical and medical staff that created a hostile work environment. Other participants felt there were benefits associated with the PACT model, included (a) improved communication between team members, (b) increased collaboration among team members, and (c) enhanced care for patients using a comprehensive team approach. These findings may help leaders create policies, improve patient care, and create perceived processes to affect successful long-term programs for the future implementation of the PACT model.
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Tepper, Sherri. "A biopsychosocial model of Alzheimer's disease /." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=59861.

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Research on the etiological characteristics of Alzheimer's disease has yielded inconsistent results. It is suggested that this may be due to the unidirectional focus on biomedical attributes, and the failure to consider psychosocial factors in combination with the biomedical characteristics. A biopsychosocial model of Alzheimer's disease, which integrates the biomedical dimension with psychosocial stressors and social support is proposed and tested in a sample of 172 geriatric patients using polychotomous logistic regression. Results find support for the implication of stress in the disease process, but fail to find a relationship between social support and Alzheimer's disease. It is concluded that the ultimate value of a biopsychosocial model of Alzheimer's disease rests in its identification of psychosocial factors, that could result in the prevention of the development of the disease.
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Lawson, Kenneth Daniel. "The Scottish cardiovascular disease policy model." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4695/.

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This thesis is concerned with economic evaluation in the primary prevention of cardiovascular disease. Policymakers are increasingly focussed on reducing the health and economic burden of CVD and to reduce health inequalities. However, the approach to primary prevention suffers from fundamental weaknesses that this research intends to help address. There is general lack of effectiveness and cost effectiveness evidence underpinning current primary prevention interventions. First, there is a policy impetus towards mass screening strategies to target individuals at high risk of developing CVD when more focussed approaches may be more cost effective. Second, clinicians prioritise individuals on the basis of 10-year risk scores, which are strongly driven by age, and not the potential benefits (or costs) from treatment. Third, targeted and population interventions are often still treated as competing approaches, whereas the key issue is how they might best combine. The key premise of this thesis is that the aims of primary prevention are the avoidance of premature morbidity, mortality and to close health inequalities - subject to a budget constraint. A CVD Policy Model was created using the same nine risk factors as used in the ASSIGN 10-year risk score, currently used in clinical practice in Scotland, to estimate life expectancy, quality adjusted life expectancy and lifetime hospital costs. This model can be employed to estimate the cost effectiveness of interventions and the impact on health inequalities. The model performed well in a comprehensive validation process in terms of face validity, internal validity, and external validity. Life expectancy predictions were re-calibrated to contemporary lifetables. This generic modelling approach (i.e. using a wide range of inputs and producing a wide range of outputs) is intended to avoid the need to build bespoke models for different interventions aimed at particular risk factors or to produce particular outputs. In application, the CVD Policy Model is intended to assist clinicians and policymakers to develop a more coherent approach to primary prevention, namely: to design more efficient screening strategies; prioritise individuals for intervention on the basis of potential benefit (rather than risk); and to assess the impact of both individually targeted and population interventions on a consistent basis. Using the model in these ways may enable primary prevention approaches to be more consistent with guidelines from health sector reimbursement agencies, which may result in a more efficient use of scarce resources.
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Leung, Wai Keung. "Effects of Treponema denticola on an oral epithelial cell model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0009/NQ34575.pdf.

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Thompson, Brett Morinaga. "Development, Implementation, and Analysis of a Contact Model for an Infectious Disease." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc9824/.

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With a growing concern of an infectious diseases spreading in a population, epidemiology is becoming more important for the future of public health. In the past epidemiologist used existing data of an outbreak to help them determine how an infectious disease might spread in the future. Now with computational models, they able to analysis data produced by these models to help with prevention and intervention plans. This paper looks at the design, implementation, and analysis of a computational model based on the interactions of the population between individuals. The design of the working contact model looks closely at the SEIR model used as the foundation and the two timelines of a disease. The implementation of the contact model is reviewed while looking closely at data structures. The analysis of the experiments provide evidence this contact model can be used to help epidemiologist study the spread of an infectious disease based on the contact rate of individuals.
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Costa, Marc Michael John Da. "A zebrafish model of motor neurone disease." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548470.

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Hoffman, Lori A. "Disease Gene Mapping Under The Coalescent Model." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1282058674.

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Huang, Shuang, and Shuang Huang. "Regularized Markov Model for Modeling Disease Transitioning." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625633.

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In longitudinal studies of chronic diseases, the disease states of individuals are often collected at several pre-scheduled clinical visits, but the exact states and the times of transitioning from one state to another between observations are not observed. This is commonly referred to as "panel data". Statistical challenges arise in panel data in regard to identifying predictors governing the transitions between different disease states with only the partially observed disease history. Continuous-time Markov models (CTMMs) are commonly used to analyze panel data, and allow maximum likelihood estimations without making any assumptions about the unobserved states and transition times. By assuming that the underlying disease process is Markovian, CTMMs yield tractable likelihood. However, CTMMs generally allow covariate effect to differ for different transitions, resulting in a much higher number of coefficients to be estimated than the number of covariates, and model overfitting can easily happen in practice. In three papers, I develop a regularized CTMM using the elastic net penalty for panel data, and implement it in an R package. The proposed method is capable of simultaneous variable selection and estimation even when the dimension of the covariates is high. In the first paper (Section 2), I use elastic net penalty to regularize the CTMM, and derive an efficient coordinate descent algorithm to solve the corresponding optimization problem. The algorithm takes advantage of the multinomial state distribution under the non-informative observation scheme assumption to simplify computation of key quantities. Simulation study shows that this method can effectively select true non-zero predictors while reducing model size. In the second paper (Section 3), I extend the regularized CTMM developed in the previous paper to accommodate exact death times and censored states. Death is commonly included as an endpoint in longitudinal studies, and exact time of death can be easily obtained but the state path leading to death is usually unknown. I show that exact death times result in a very different form of likelihood, and the dependency of death time on the model requires significantly different numerical methods for computing the derivatives of the log likelihood, a key quantity for the coordinate descent algorithm. I propose to use numerical differentiation to compute the derivatives of the log likelihood. Computation of the derivatives of the log likelihood from a transition involving a censored state is also discussed. I carry out a simulation study to evaluate the performance of this extension, which shows consistently good variable selection properties and comparable prediction accuracy compared to the oracle models where only true non-zero coefficient are fitted. I then apply the regularized CTMM to the airflow limitation data to the TESAOD (The Tucson Epidemiological Study of Airway Obstructive Disease) study with exact death times and censored states, and obtain a prediction model with great size reduction from a total of 220 potential parameters. Methods developed in the first two papers are implemented in an R package markovnet, and a detailed introduction to the key functionalities of the package is demonstrated with a simulated data set in the third paper (Section 4). Finally, some conclusion remarks are given and directions to future work are discussed (Section 5). The outline for this dissertation is as follows. Section 1 presents an in-depth background regarding panel data, CTMMs, and penalized regression methods, as well as an brief description of the TESAOD study design. Section 2 describes the first paper entitled "Regularized continuous-time Markov model via elastic net'". Section 3 describes the second paper entitled "Regularized continuous-time Markov model with exact death times and censored states"'. Section 4 describes the third paper "Regularized continuous-time Markov model for panel data: the markovnet package for R"'. Section 5 gives an overall summary and a discussion of future work.
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Whitehead, Daisy. "Hallucinations in Parkinson's disease : a psychological model." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415741.

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Gupta, Amrita. "Unsupervised learning of disease subtypes from continuous time Hidden Markov Models of disease progression." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54364.

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The detection of subtypes of complex diseases has important implications for diagnosis and treatment. Numerous prior studies have used data-driven approaches to identify clusters of similar patients, but it is not yet clear how to best specify what constitutes a clinically meaningful phenotype. This study explored disease subtyping on the basis of temporal development patterns. In particular, we attempted to differentiate infants with autism spectrum disorder into more fine-grained classes with distinctive patterns of early skill development. We modeled the progression of autism explicitly using a continuous-time hidden Markov model. Subsequently, we compared subjects on the basis of their trajectories through the model state space. Two approaches to subtyping were utilized, one based on time-series clustering with a custom distance function and one based on tensor factorization. A web application was also developed to facilitate the visual exploration of our results. Results suggested the presence of 3 developmental subgroups in the ASD outcome group. The two subtyping approaches are contrasted and possible future directions for research are discussed.
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Books on the topic "Disease model"

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Marsden, Michael A. Sensitivity of the western root disease model: Inventory of root disease. Fort Collins, CO: USDA, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1992.

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Heather, Nick, Matt Field, Antony C. Moss, and Sally Satel. Evaluating the Brain Disease Model of Addiction. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003032762.

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Bolton, Derek, and Grant Gillett. The Biopsychosocial Model of Health and Disease. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11899-0.

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Beyond the disease model of mental disorders. Westport, Conn: Praeger, 1999.

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R, Stage Albert, and Intermountain Research Station (Ogden, Utah), eds. User's manual for western root disease model. Ogden, Utah: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1990.

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Vithoulkas, George. A new model for health and disease. Mill Valley, Calif: Health and Habitat, 1991.

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Imperial Cancer Research Fund (Great Britain). Naturally entering tumours in animals as a model for human disease. Edited by Onions David E and Jarrett O. (Oswald). Oxford, U.K: Published for the Imperial Cancer Research Fund by Oxford University Press, 1987.

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G, Hnath John, Horner Rodney W, Eshenroder Randy L, and Great Lakes Fishery Commission, eds. Great lakes fish disease control policy and model program. Ann Arbor, MI: Great Lakes Fishery Commission, 1993.

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Medaka: A model for organogenesis, human disease, and evolution. Tokyo: Springer, 2011.

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G, Hnath John, ed. Great Lakes fish disease control policy and model program. Mattawan, Mich: Michigan Dept. of Natural Resources, Wolf Lake State Fish Hatchery, 1985.

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Book chapters on the topic "Disease model"

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Dung, Vuu My, and Dang Thi Phuong Thao. "Parkinson’s Disease Model." In Advances in Experimental Medicine and Biology, 41–61. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0529-0_4.

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Liu, Xinzhi, and Peter Stechlinski. "The Switched SIR Model." In Infectious Disease Modeling, 43–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53208-0_3.

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Uchida, Kaoru, and Masaaki Kitahara. "An Animal Model: Ménière’s Disease Attack." In Ménière’s Disease, 65–71. Tokyo: Springer Japan, 1990. http://dx.doi.org/10.1007/978-4-431-68111-3_7.

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Higgins, Linda S., and Barbara Cordell. "Transgenic Mice as a Model of Alzheimer’s Disease." In Alzheimer Disease, 385–89. Boston, MA: Birkhäuser Boston, 1994. http://dx.doi.org/10.1007/978-1-4615-8149-9_64.

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Dormi, A., G. L. Magri, G. Mannino, and G. C. Descovich. "Mathematical model: interpolation and simulation." In Atherosclerosis and Cardiovascular Disease, 228–31. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0731-7_30.

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Hall, Wayne, Adrian Carter, and Cynthia Forlini. "Brain Disease Model of Addiction." In Evaluating the Brain Disease Model of Addiction, 125–26. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003032762-15.

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Volkow, Nora D., and George Koob. "Brain Disease Model of Addiction." In Evaluating the Brain Disease Model of Addiction, 122–24. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003032762-14.

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Fan, Jiang-lin, Cesare Castellini, Clive P. Page, and Domenico Spina. "The rabbit as a biomedical model." In The genetics and genomics of the rabbit, 310–25. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781780643342.0018.

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Abstract In this chapter, a brief introduction and some guidelines on using the rabbits as models for studying human diseases such as cardiovascular disease, respiratory disease, immune-related diseases, osteoarthritis, ocular research and Alzheimer's disease, along with reproductive physiology were presented. The chapter will not be comprehensive but rather concise; therefore, readers are also encouraged to read other chapters of this book and other references for further details.
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Golden, Angela. "Chronic Disease Model for Obesity." In Treating Obesity in Primary Care, 91–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48683-9_6.

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McHugh, Paul R. "Schizophrenia and the Disease Model." In What Is Schizophrenia?, 73–80. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4613-9157-9_6.

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Conference papers on the topic "Disease model"

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Pinto, Carla M. A., and J. A. Tenreiro Machado. "Fractional Model for Malaria Disease." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12946.

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In this paper we study a fractional order model for malaria transmission. It is considered the integer order model proposed by Chitnis et al [1] and we generalize it up to become a fractional model. The new model is simulated for distinct values of the fractional order. Are considered two initial conditions and a set of parameter values satisfying a value of the reproduction number, R0, less than one, for the integer model. In this case, there is co-existence of a stable disease free equilibrium and an endemic equilibrium. The results are in agreement with the integer order model and reveal that we can extend the dynamical evolution up to new types of transients. Future work will focus on analytically prove some of the results obtained.
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ALDILA, DIPO, EDY SOEWONO, and TRI JULIANSYAH. "Epidemic Model For Ebola Disease." In Second International Conference on Advances in Applied Science and Environmental Technology - ASET 2015. Institute of Research Engineers and Doctors, 2015. http://dx.doi.org/10.15224/978-1-63248-075-0-41.

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Khairani, Nerli, Tiur Malasari Siregar, Suci Frisnoiry, Sudianto Manullang, and Novita Indah Hasibuan. "SEIR Model in Spread Disease." In Proceedings of the 4th International Conference on Innovation in Education, Science and Culture, ICIESC 2022, 11 October 2022, Medan, Indonesia. EAI, 2022. http://dx.doi.org/10.4108/eai.11-10-2022.2325320.

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Wenxin, Xu. "Heart Disease Prediction Model Based on Model Ensemble." In 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2020. http://dx.doi.org/10.1109/icaibd49809.2020.9137483.

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Jo-Avila, Miguel, Ahmed Al-Jumaily, and Jun Lu. "Predictive Cardiovascular Model With Blood Flow Measurements." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51993.

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Cardiovascular disease is one of the leading causes of death in the world, accounting for 30% of all deaths worldwide and 40% of those occurring in New Zealand. In recent years, engineers and scientists have collaborated with the medical community to find new methodologies and approaches for assessing, investigating and understanding the development of cardiovascular diseases. Elements such as computational models developed with fluid dynamic elements (CFD/FE) have become excellent tools for this purpose. One of the important approaches is developing devices for investigating the central blood flow and pressure, and correlating the results to different heart diseases. Higher-valued changes in central blood flow and pressure mean that the heart must work harder. A computational model capable of predicting inlet and outlet locations of a blockage would be helpful to determine different stages of cardiovascular disease. By using reflection signals from the central blood flow that are detected at locations such as the brachial artery or subclavian artery, it is possible to model the effect of flow and pressure differences on heart diseases.
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Barhak, Jacob. "The Reference Model for Disease Progression." In Python in Science Conference. SciPy, 2012. http://dx.doi.org/10.25080/majora-54c7f2c8-007.

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Pare, Philip E., Ji Liu, Carolyn L. Beck, and Tamer Basar. "Networked Infectious Disease–Contaminated Water Model." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8795741.

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Sharathchandra, D., and M. Raghu Ram. "ML Based Interactive Disease Prediction Model." In 2022 IEEE Delhi Section Conference (DELCON). IEEE, 2022. http://dx.doi.org/10.1109/delcon54057.2022.9752947.

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Yu, Song, XiaoLie Lin, XueMeng Yu, Masafumi Kinoshita, Xue Zhang, and Yong Jun Chen. "Improvements on the Disease Migration Model." In 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN). IEEE, 2022. http://dx.doi.org/10.1109/icicn56848.2022.10006424.

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Krzeminski, Michal. "Markov Model of Disease Development and Recovery." In 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO). IEEE, 2016. http://dx.doi.org/10.1109/smrlo.2016.76.

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Reports on the topic "Disease model"

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Ray, Jaideep, Katherine Regina Cauthen, Sophia Lefantzi, and Lynne Burks. Conditioning multi-model ensembles for disease forecasting. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492995.

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Gordon, Terry. Transgenic Mouse Model of Chronic Beryllium Disease. Office of Scientific and Technical Information (OSTI), May 2009. http://dx.doi.org/10.2172/953218.

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Edmunds, T. Agent-based Disease Surveillance and Transmission Model. Office of Scientific and Technical Information (OSTI), December 2021. http://dx.doi.org/10.2172/1835684.

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Cheng, Karen, David Crary, J. Rodriguez, and Darren R. Oldson. A Small-World Network Model of Disease Transmission. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada555260.

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Kowall, Neil W. Alpha Synuclein Aggregation in a Neurotoxic Model of Parkinson's Disease. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada393984.

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Kowall, Neil W. Alpha Synuclein Aggregation in a Neurotoxic Model of Parkinson's Disease. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada397690.

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Kordower, Jeffrey H. Gene Therapy in a Nonhuman Primate Model of Parkinson's Disease. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada398254.

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8

Frankel, Susan J. User's Guide to the Western Root Disease Model, Version 3.0. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 1998. http://dx.doi.org/10.2737/psw-gtr-165.

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9

Kowall, Neil W. Alpha Synuclein Aggregation in a Neurotoxic Model of Parkinson's Disease. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada415870.

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10

Kordower, Jeffrey H. Gene Therapy in a Nonhuman Primate Model of Parkinson's Disease. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada417090.

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