Готові списки джерел за темами / Legionnaires' Disease Mathematical models

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Добірка наукової літератури з теми "Legionnaires' Disease Mathematical models"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 1 лютого 2022

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Статті в журналах з теми "Legionnaires' Disease Mathematical models"

Cassell, Kelsie, Paul Gacek, Therese Rabatsky-Ehr, Susan Petit, Matthew Cartter, and Daniel M. Weinberger. "Estimating the True Burden of Legionnaires’ Disease." American Journal of Epidemiology 188, no. 9 (June 21, 2019): 1686–94. http://dx.doi.org/10.1093/aje/kwz142.

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Abstract Over the past decade, the reported incidence of Legionnaires’ disease (LD) in the northeastern United States has increased, reaching 1–3 cases per 100,000 population. There is reason to suspect that this is an underestimate of the true burden, since LD cases may be underdiagnosed. In this analysis of pneumonia and influenza (P&I) hospitalizations, we estimated the percentages of cases due to Legionella, influenza, and respiratory syncytial virus (RSV) by age group. We fitted mixed-effects models to estimate attributable percents using weekly time series data on P&I hospitalizations in Connecticut from 2000 to 2014. Model-fitted values were used to calculate estimates of numbers of P&I hospitalizations attributable to Legionella (and influenza and RSV) by age group, season, and year. Our models estimated that 1.9%, 8.8%, and 5.1% of total (all-ages) inpatient P&I hospitalizations could be attributed to Legionella, influenza, and RSV, respectively. Only 10.6% of total predicted LD cases had been clinically diagnosed as LD during the study period. The observed incidence rate of 1.2 cases per 100,000 population was substantially lower than our estimated rate of 11.6 cases per 100,000 population. Our estimates of numbers of P&I hospitalizations attributable to Legionella are comparable to those provided by etiological studies of community-acquired pneumonia and emphasize the potential for underdiagnosis of LD in clinical settings.

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Dobson, A. "Mathematical models for emerging disease." Science 346, no. 6215 (December 11, 2014): 1294–95. http://dx.doi.org/10.1126/science.aaa3441.

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Bakshi, Suruchi, Vijayalakshmi Chelliah, Chao Chen, and Piet H. van der Graaf. "Mathematical Biology Models of Parkinson's Disease." CPT: Pharmacometrics & Systems Pharmacology 8, no. 2 (November 2, 2018): 77–86. http://dx.doi.org/10.1002/psp4.12362.

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Grassly, Nicholas C., and Christophe Fraser. "Mathematical models of infectious disease transmission." Nature Reviews Microbiology 6, no. 6 (May 13, 2008): 477–87. http://dx.doi.org/10.1038/nrmicro1845.

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KLEIN, EILI, RAMANAN LAXMINARAYAN, DAVID L. SMITH, and CHRISTOPHER A. GILLIGAN. "Economic incentives and mathematical models of disease." Environment and Development Economics 12, no. 5 (October 2007): 707–32. http://dx.doi.org/10.1017/s1355770x0700383x.

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The fields of epidemiological disease modeling and economics have tended to work independently of each other despite their common reliance on the language of mathematics and exploration of similar questions related to human behavior and infectious disease. This paper explores the benefits of incorporating simple economic principles of individual behavior and resource optimization into epidemiological models, reviews related research, and indicates how future cross-discipline collaborations can generate more accurate models of disease and its control to guide policy makers.

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Meltzer, M. I., and R. A. I. Norval. "Mathematical models of tick-borne disease transmission." Parasitology Today 9, no. 8 (August 1993): 277–78. http://dx.doi.org/10.1016/0169-4758(93)90116-w.

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Donovan, Graham M. "Multiscale mathematical models of airway constriction and disease." Pulmonary Pharmacology & Therapeutics 24, no. 5 (October 2011): 533–39. http://dx.doi.org/10.1016/j.pupt.2011.01.003.

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Medley, Graham F. "Mathematical models of tick-borne disease transmission: Reply." Parasitology Today 9, no. 8 (August 1993): 292. http://dx.doi.org/10.1016/0169-4758(93)90123-w.

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DUNN, C. E., B. ROWLINGSON, R. S. BHOPAL, and P. DIGGLE. "Meteorological conditions and incidence of Legionnaires' disease in Glasgow, Scotland: application of statistical modelling." Epidemiology and Infection 141, no. 4 (June 12, 2012): 687–96. http://dx.doi.org/10.1017/s095026881200101x.

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SUMMARYThis study investigated the relationships between Legionnaires' disease (LD) incidence and weather in Glasgow, UK, by using advanced statistical methods. Using daily meteorological data and 78 LD cases with known exact date of onset, we fitted a series of Poisson log-linear regression models with explanatory variables for air temperature, relative humidity, wind speed and year, and sine-cosine terms for within-year seasonal variation. Our initial model showed an association between LD incidence and 2-day lagged humidity (positive, P = 0·0236) and wind speed (negative, P = 0·033). However, after adjusting for year-by-year and seasonal variation in cases there were no significant associations with weather. We also used normal linear models to assess the importance of short-term, unseasonable weather values. The most significant association was between LD incidence and air temperature residual lagged by 1 day prior to onset (P = 0·0014). The contextual role of unseasonably high air temperatures is worthy of further investigation. Our methods and results have further advanced understanding of the role which weather plays in risk of LD infection.

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De Gaetano, Andrea, Thomas Hardy, Benoit Beck, Eyas Abu-Raddad, Pasquale Palumbo, Juliana Bue-Valleskey, and Niels Pørksen. "Mathematical models of diabetes progression." American Journal of Physiology-Endocrinology and Metabolism 295, no. 6 (December 2008): E1462—E1479. http://dx.doi.org/10.1152/ajpendo.90444.2008.

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Few attempts have been made to model mathematically the progression of type 2 diabetes. A realistic representation of the long-term physiological adaptation to developing insulin resistance is necessary for effectively designing clinical trials and evaluating diabetes prevention or disease modification therapies. Writing a good model for diabetes progression is difficult because the long time span of the disease makes experimental verification of modeling hypotheses extremely awkward. In this context, it is of primary importance that the assumptions underlying the model equations properly reflect established physiology and that the mathematical formulation of the model give rise only to physically plausible behavior of the solutions. In the present work, a model of the pancreatic islet compensation is formulated, its physiological assumptions are presented, some fundamental qualitative characteristics of its solutions are established, the numerical values assigned to its parameters are extensively discussed (also with reference to available cross-sectional epidemiologic data), and its performance over the span of a lifetime is simulated under various conditions, including worsening insulin resistance and primary replication defects. The differences with respect to two previously proposed models of diabetes progression are highlighted, and therefore, the model is proposed as a realistic, robust description of the evolution of the compensation of the glucose-insulin system in healthy and diabetic individuals. Model simulations can be run from the authors' web page.

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Дисертації з теми "Legionnaires' Disease Mathematical models"

Wilmot, Peter Nicholas. "Modelling cooling tower risk for Legionnaires' Disease using Bayesian Networks and Geographic Information Systems." Title page, contents and conclusion only, 1999. http://web4.library.adelaide.edu.au/theses/09SIS.M/09sismw744.pdf.

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Includes bibliographical references (leaves 115-120) Establishes a Bayesian Belief Network (BBN) to model uncertainty of aerosols released from cooling towers and Geographic Information Systems (GIS) to create a wind dispersal model and identify potential cooling towers as the source of infection. Demonstrates the use of GIS and BBN in environmental epidemiology and the power of spatial information in the area of health.

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Roberts, Paul Allen. "Mathematical models of the retina in health and disease." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:385f61c4-4ff1-45d3-bdb2-41338c174025.

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The retina is the ocular tissue responsible for the detection of light. Its extensive demand for oxygen, coupled with a concomitant elevated supply, renders this tissue prone to both hypoxia and hyperoxia. In this thesis, we construct mathematical models of the retina, formulated as systems of reaction-diffusion equations, investigating its oxygen-related dynamics in healthy and diseased states. In the healthy state, we model the oxygen distribution across the human retina, examining the efficacy of the protein neuroglobin in the prevention of hypoxia. It has been suggested that neuroglobin could prevent hypoxia, either by transporting oxygen from regions where it is rich to those where it is poor, or by storing oxygen during periods of diminished supply or increased uptake. Numerical solutions demonstrate that neuroglobin may be effective in preventing or alleviating hypoxia via oxygen transport, but that its capacity for oxygen storage is essentially negligible, whilst asymptotic analysis reveals that, contrary to the prevailing assumption, neuroglobin's oxygen affinity is near optimal for oxygen transport. A further asymptotic analysis justifies the common approximation of a piecewise constant oxygen uptake across the retina, placing existing models upon a stronger theoretical foundation. In the diseased state, we explore the effect of hyperoxia upon the progression of the inherited retinal diseases, known collectively as retinitis pigmentosa. Both numerical solutions and asymptotic analyses show that this mechanism may replicate many of the patterns of retinal degeneration seen in vivo, but that others are inaccessible to it, demonstrating both the strengths and weaknesses of the oxygen toxicity hypothesis. It is shown that the wave speed of hyperoxic degeneration is negatively correlated with the local photoreceptor density, high density regions acting as a barrier to the spread of photoreceptor loss. The effects of capillary degeneration and treatment with antioxidants or trophic factors are also investigated, demonstrating that each has the potential to delay, halt or partially reverse photoreceptor loss. In addition to answering questions that are not accessible to experimental investigation, these models generate a number of experimentally testable predictions, forming the first loop in what has the potential to be a fruitful experimental/modelling cycle.

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Oduro, Bismark. "Mathematical Models of Triatomine (Re)infestation." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1458563770.

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Zhang, Xu-Sheng. "Mathematical models of plant disease epidemics that involve virus interactions." Thesis, University of Greenwich, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327341.

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Bell, Sally Sue. "Mathematical models assessing the importance of disease on ecological invasions." Thesis, Heriot-Watt University, 2010. http://hdl.handle.net/10399/2316.

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A general understanding of the role that both shared disease and competition may play in ecological invasions is lacking. We develop a theoretical framework to determine the role of disease, in addition to competition, in invasions. We first investigate the e ect of disease characteristics on the replacement time of a native species by an invader. The outcome is critically dependent on the relative e ects that the disease has on the two species and less dependent on the basic epidemiological characteristics of the interaction. This framework is extended to investigate the e ect of disease on the spatial spread of an invader and indicates that a wave of disease spreads through a native population in advance of the replacement. A probabilistic simulation model is developed to examine the particular example of the replacement of red squirrels by grey squirrels in the United Kingdom. This model is used to examine conservation strategies employed within red squirrel refuges and compared to observations from Sefton Coast Red Squirrel Refuge. Our findings indicate that culling greys may be e ective at protecting red populations from replacement, but none of the conservation strategies currently employed can prevent periodic outbreaks of infection within red squirrel refuges.

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Korobeinikov, Andrei. "Stability and bifurcation of deterministic infectious disease models." Thesis, University of Auckland, 2001. http://wwwlib.umi.com/dissertations/fullcit/3015611.

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Autonomous deterministic epidemiological models are known to be asymptotically stable. Asymptotic stability of these models contradicts observations. In this thesis we consider some factors which were suggested as able to destabilise the system. We consider discrete-time and continuous-time autonomous epidemiological models. We try to keep our models as simple as possible and investigate the impact of different factors on the system behaviour. Global methods of dynamical systems theory, especially the theory of bifurcations and the direct Lyapunov method are the main tools of our analysis. Lyapunov functions for a range of classical epidemiological models are introduced. The direct Lyapunov method allows us to establish their boundedness and asymptotic stability. It also helps investigate the impact of such factors as susceptibles' mortality, horizontal and vertical transmission and immunity failure on the global behaviour of the system. The Lyapunov functions appear to be useful for more complicated epidemiological models as well. The impact of mass vaccination on the system is also considered. The discrete-time model introduced here enables us to solve a practical problem-to estimate the rate of immunity failure for pertussis in New Zealand. It has been suggested by a number of authors that a non-linear dependence of disease transmission on the numbers of infectives and susceptibles can reverse the stability of the system. However it is shown in this thesis that under biologically plausible constraints the non-linear transmission is unable to destabilise the system. The main constraint is a condition that disease transmission must be a concave function with respect to the number of infectives. This result is valid for both the discrete-time and the continuous-time models. We also consider the impact of mortality associated with a disease. This factor has never before been considered systematically. We indicate mechanisms through which the disease-induced mortality can affect the system and show that the disease-induced mortality is a destabilising factor and is able to reverse the system stability. However the critical level of mortality which is necessary to reverse the system stability exceeds the mortality expectation for the majority of human infections. Nevertheless the disease-induced mortality is an important factor for understanding animal diseases. It appears that in the case of autonomous systems there is no single factor able to cause the recurrent outbreaks of epidemics of such magnitudes as have been observed. It is most likely that in reality they are caused by a combination of factors.
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Ning, Yao, and 宁耀. "The use of stochastic models of infectious disease transmission for public health: schistosomiasis japonica." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B4553097X.

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Bingham, Adrienna N. "Controlling Infectious Disease: Prevention and Intervention Through Multiscale Models." W&M ScholarWorks, 2019. https://scholarworks.wm.edu/etd/1582642581.

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Controlling infectious disease spread and preventing disease onset are ongoing challenges, especially in the presence of newly emerging diseases. While vaccines have successfully eradicated smallpox and reduced occurrence of many diseases, there still exists challenges such as fear of vaccination, the cost and difficulty of transporting vaccines, and the ability of attenuated viruses to evolve, leading to instances such as vaccine derived poliovirus. Antibiotic resistance due to mistreatment of antibiotics and quickly evolving bacteria contributes to the difficulty of eradicating diseases such as tuberculosis. Additionally, bacteria and fungi are able to produce an extracellular matrix in biofilms that protects them from antibiotics/antifungals. Mathematical models are an effective way of measuring the success of various control measures, allowing for cost savings and efficient implementation of those measures. While many models exist to investigate the dynamics on a human population scale, it is also beneficial to use models on a microbial scale to further capture the biology behind infectious diseases. In this dissertation, we develop mathematical models at several spatial scales to help improve disease control. At the scale of human populations, we develop differential equation models with quarantine control. We investigate how the distribution of exposed and infectious periods affects the control efficacy and suggest when it is important for models to include realistically narrow distributions. At the microbial scale, we use an agent-based stochastic spatial simulation to model the social interactions between two yeast strains in a biofilm. While cheater strains have been proposed as a control strategy to disrupt the harmful cooperative biofilm, some yeast strains cooperate only with other cooperators via kin recognition. We study under what circumstances kin recognition confers the greatest fitness benefit to a cooperative strain. Finally, we look at a multiscale, two-patch model for the dynamics between wild-type (WT) poliovirus and defective interfering particles (DIPs) as they travel between organs. DIPs are non-viable variants of the WT that lack essential elements needed for reproduction, causing them to steal these elements from the WT. We investigate when DIPs can lower the WT population in the host.

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Kwong, Kim-hung, and 鄺劍雄. "Spatio-temporal transmission modelling of an infectious disease: a case study of the 2003 SARS outbreak in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45693900.

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Venkatachalam, Sangeeta. "Modeling Infectious Disease Spread Using Global Stochastic Field Simulation." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5335/.

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Susceptibles-infectives-removals (SIR) and its derivatives are the classic mathematical models for the study of infectious diseases in epidemiology. In order to model and simulate epidemics of an infectious disease, a global stochastic field simulation paradigm (GSFS) is proposed, which incorporates geographic and demographic based interactions. The interaction measure between regions is a function of population density and geographical distance, and has been extended to include demographic and migratory constraints. The progression of diseases using GSFS is analyzed, and similar behavior to the SIR model is exhibited by GSFS, using the geographic information systems (GIS) gravity model for interactions. The limitations of the SIR and similar models of homogeneous population with uniform mixing are addressed by the GSFS model. The GSFS model is oriented to heterogeneous population, and can incorporate interactions based on geography, demography, environment and migration patterns. The progression of diseases can be modeled at higher levels of fidelity using the GSFS model, and facilitates optimal deployment of public health resources for prevention, control and surveillance of infectious diseases.

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Книги з теми "Legionnaires' Disease Mathematical models"

Center for Emerging Issues (U.S.). Overview of predictive infectious-disease modeling. Washington, D.C.]: United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Center for Emerging Issues, 2005.

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Courant Institute of Mathematical Sciences, ed. Mathematical methods for analysis of a complex disease. New York: Courant Institute of Mathematical Sciences, 2011.

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Karakawa, Masanori. A mathematical approach to cardiovascular disease: Mechanics of blood circulation. Tokyo: Kokuseido Pub. Co., 1998.

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Modelling Disease Ecology With Mathematics. Springfield, MO: American Institute of Mathematical Sciences, 2008.

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Roy, Priti Kumar. Mathematical Models for Therapeutic Approaches to Control HIV Disease Transmission. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-852-6.

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Zawołek, M. W. A physical theory of focus development in plant disease. Wageningen, Netherlands: Agricultural University, 1989.

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Kremer, Michael. Integrating behavioral choice into epidemiological models of AIDS. Cambridge, MA: National Bureau of Economic Research, 1996.

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Gumel, Abba B. Modeling paradigms and analysis of disease transmission models. Providence, R.I: American Mathematical Society, 2010.

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Stanecki, De Lay Karen, ed. The demographic impact of an AIDS epidemic on an African country: Application of the iwgAIDS model. Washington, D.C: Center for International Research, U.S. Bureau of the Census, 1991.

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International Association for the Study of Insurance Economics. General Assembly. AIDS and insurance: Documents and texts from the panel of the 15th General Assembly of the Geneva Association. Genève: "Association", 1988.

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Частини книг з теми "Legionnaires' Disease Mathematical models"

Brown, Andrew S., Ian R. van Driel, and Elizabeth L. Hartland. "Mouse Models of Legionnaires’ Disease." In Current Topics in Microbiology and Immunology, 271–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/82_2013_349.

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Kretzschmar, Mirjam, and Jacco Wallinga. "Mathematical Models in Infectious Disease Epidemiology." In Modern Infectious Disease Epidemiology, 209–21. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-93835-6_12.

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Dietz, K., and D. Schenzle. "Mathematical Models for Infectious Disease Statistics." In A Celebration of Statistics, 167–204. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4613-8560-8_8.

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Kühl, Michael, Barbara Kracher, Alexander Groß, and Hans A. Kestler. "Mathematical Models of Wnt Signaling Pathways." In Wnt Signaling in Development and Disease, 153–60. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118444122.ch11.

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Roy, Priti Kumar. "Mathematical Models in Stochastic Approach." In Mathematical Models for Therapeutic Approaches to Control HIV Disease Transmission, 183–213. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-852-6_8.

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Nanni, P. G., G. Castellani, P. Pettazzoni, G. Pallotti, and C. Pallotti. "Limits of mathematical models in biology and medicine." In Atherosclerosis and Cardiovascular Disease, 232–36. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0731-7_31.

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Mohapatra, R. N., Donald Porchia, and Zhisheng Shuai. "Compartmental Disease Models with Heterogeneous Populations: A Survey." In Mathematical Analysis and its Applications, 619–31. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2485-3_51.

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Qi, Zhen, Gary W. Miller, and Eberhard O. Voit. "Mathematical Models of Dopamine Metabolism in Parkinson’s Disease." In Systems Biology of Parkinson's Disease, 151–71. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3411-5_8.

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Boutayeb, Abdesslam, Mohamed E. N. Lamlili, and Wiam Boutayeb. "A Review of Compartmental Mathematical Models Used in Diabetology." In Disease Prevention and Health Promotion in Developing Countries, 217–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34702-4_14.

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Dioguardi, N., P. Mussio, M. Zuin, and A. Lovati. "Mathematical Models for the Study of Hepatic Metabolism: A New Strategy." In Assessment and Management of Hepatobiliary Disease, 9–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-72631-6_2.

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Тези доповідей конференцій з теми "Legionnaires' Disease Mathematical models"

COLLINO, SIMONA, EZIO VENTURINO, LUCA FERRERI, LUIGI BERTOLOTTI, SERGIO ROSATI, and MARIO GIACOBINI. "MODELS FOR TWO STRAINS OF THE CAPRINE ARTHRITIS ENCEPHALITIS VIRUS DISEASE." In 15th International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813141919_0019.

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Chang, Albert Ling Sheng, Chong Khim Phin, and Ho Chong Mun. "Comparing nonlinear models in describing disease progress curve of cocoa black pod." In PROCEEDING OF THE 25TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM25): Mathematical Sciences as the Core of Intellectual Excellence. Author(s), 2018. http://dx.doi.org/10.1063/1.5041682.

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Nielsen, B. F., M. Lysaker, C. Tarrou, M. C. MacLachlan, A. Abildgaard, and A. Tveito. "On the use of st-segment shifts and mathematical models for identifying ischemic heart disease." In Computers in Cardiology, 2005. IEEE, 2005. http://dx.doi.org/10.1109/cic.2005.1588280.

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Elliott, Novak S. J. "Cerebrospinal Fluid-Structure Interactions: The Development of Mathematical Models Accessible to Clinicians." In ASME 2014 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/pvp2014-29096.

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Анотація:

Physical scientists work with clinicians on biomechanical problems, yet the predictive capabilities of mathematical models often remain elusive to clinical collaborators. This is due to both conceptual differences in the research methodologies of each discipline, and the perceived complexity of even simple models. This limits expert medical input, affecting the applicability of the results. Moreover, a lack of understanding undermines the medical practitioner’s confidence in modeling predictions, hampering its clinical application. In this paper we consider the disease syringomyelia, which involves the fluid-structure interaction of pressure vessels and pipes, as a paradigm of the nexus between the modeling approaches of physical scientists and clinicians. The observations made are broadly applicable to cross-disciplinary research between engineers and non-technical specialists, such as may occur in academic-industrial collaborations.

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Takada, M., M. Sugimoto, N. Masuda, H. Iwata, K. Kuroi, H. Yamashiro, S. Ohno, H. Ishiguro, T. Inamoto, and M. Toi. "Abstract P4-21-24: Development of mathematical prediction models to identify disease-free survival events for HER2-positive primary breast cancer patients treated by neoadjuvant chemotherapy and trastuzumab." In Abstracts: 2016 San Antonio Breast Cancer Symposium; December 6-10, 2016; San Antonio, Texas. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.sabcs16-p4-21-24.

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Lundberg, Hannah J., Kharma C. Foucher, Thomas P. Andriacchi, and Markus A. Wimmer. "Comparison of Numerically Modeled Knee Joint Contact Forces to Instrumented Total Knee Prosthesis Forces." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206791.

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Total knee replacement (TKR) surgery decreases pain and increases functional mobility for patients with joint disease. As primary TKRs are implanted in patients who are younger, heavier, and more active (1), increases in wear and TKR revision rates are expected. Preclinical analysis of TKRs with mathematical models and experimental tests require accurate in vivo kinetic and kinematic input data. Kinematics can be obtained with gait analysis, but in vivo force data are just beginning to become available from instrumented TKRs from only a few patients (2). Patient gait is highly variable both within and between individuals and can be influenced by a variety of factors including the progression and history of joint disease, surgical procedure, and TKR design. Variation in patient gait and activities results in subsequent contact force and polyethylene wear variability. A validated mathematical model which calculates contact forces for alternate input data could add valuable insight for preclinical testing. A problem facing mathematical modeling is that there are too many unknowns to directly solve for contact forces. In order to approach this problem, we have developed a knee mathematical model that allows parametric variation of muscle activation levels (3) and calculates a solution space of physically possible contact forces.

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Rugonyi, Sandra, and Kent Thornburg. "Modeling the Effect of Hemodynamics on Cardiac Growth During Embryonic Development." In ASME 2010 First Global Congress on NanoEngineering for Medicine and Biology. ASMEDC, 2010. http://dx.doi.org/10.1115/nemb2010-13171.

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Congenital heart disease (CHD) affects about 1% of newborn babies in the US, and is the leading cause of non-infectious death in children. Abnormal blood flow dynamics during early development can lead to CHD. Although the effect of hemodynamic conditions on cardiac development — even under normal conditions — has been widely accepted, the mechanisms by which blood flow influences cardiac cell responses are only starting to emerge. Mathematical models of cardiac growth could then help elucidate key aspects of cardiac development.

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Bazilo, Constantine, Alvydas Zagorskis, Oleg Petrishchev, Yulia Bondarenko, Vasyl Zaika, and Yulia Petrushko. "Modelling of Piezoelectric Transducers for Environmental Monitoring." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.008.

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World Health Organization (WHO) defined health as being “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity”. Physical factors (noise, vibration, electromagnetic fields, ionized radiation, etc.) may have a negative influence on both the environment and the health of population. Piezoelectric sensors have been employed in different fields such as medical analysis, environmental monitoring, etc. The object of the research is piezoelectric sensors for environmental monitoring and their simulation. Currently, there are no reliable and valid methods of constructing of mathematical models of piezoelectric transducers, which could be used as a theoretical basis for calculating characteristics and parameters of this class of functional elements of modern piezoelectronics. In most papers the described methods of transformers simulation are mostly based on the use of equivalent electrical circuits and it does not allow analysing stress-strain state of solids with piezoelectric effects. The final goal of mathematical modelling of vibrating piezoelectric elements is a qualitative and quantitative description of characteristics and parameters of existing electrical and elastic fields. Physical processes in piezoelectric transducers which occur using axially symmetric radial oscillations of piezoceramic disk are considered.

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Chagdes, James R., Joshua J. Liddy, Jessica E. Huber, Howard N. Zelaznik, Shirley Rietdyk, Arvind Raman, and Jeffrey M. Haddad. "Dynamic Instabilities Induced Through Altered Visual Cues and Their Relationship to Postural Response Latencies." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60248.

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Mathematical models predict limit cycle oscillations (LCOs) in postural sway when the combination of neuromuscular time-delay and feedback gains are excessively large. LCOs have been observed in the standing posture of various populations known to have longer time-delays including concussed young adults and adults with neuromuscular impairment such as multiple sclerosis (MS) and Parkinsons disease (PD) but not healthy controls. However, the relationship between feedback gain and time-delay that leads to these LCOs has yet to be explored experimentally. In this study, we examine the relationship between the time-delay of healthy adults and the onset of LCOs under altered visual feedback. We find that there is an inversely proportional correlation between feedback gain and intrinsic neuromuscular time-delay for which LCOs arise. This finding has implications for the assessment and diagnosis of neuromuscular related balance issues through a simple and invasive protocol similar to that used in this study.

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Hossain, Md Shahadat, Bhavin Dalal, Ian S. Fischer, Pushpendra Singh, and Nadine Aubry. "Modeling of Blood Flow in the Human Brain." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30554.

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The non-Newtonian properties of blood, i.e., shear thinning and viscoelasticity, can have a significant influence on the distribution of Cerebral Blood Flow (CBF) in the human brain. The aim of this work is to quantify the role played by the non-Newtonian nature of blood. Under normal conditions, CBF is autoregulated to maintain baseline levels of flow and oxygen to the brain. However, in patients suffering from heart failure (HF), Stroke, or Arteriovenous malformation (AVM), the pressure in afferent vessels varies from the normal range within which the regulatory mechanisms can ensure a constant cerebral flow rate, leading to impaired cerebration in patients. It has been reported that the change in the flow rate is more significant in certain regions of the brain than others, and that this might be relevant to the pathophysiological symptoms exhibited in these patients. We have developed mathematical models of CBF under normal and the above disease conditions that use direct numerical simulations (DNS) for the individual capillaries along with the experimental data in a one-dimensional model to determine the flow rate and the methods for regulating CBF. The model also allows us to determine which regions of the brain would be affected relatively more severely under these conditions.

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