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Artykuły w czasopismach na temat "Epidemiology modeling tool"
Amorim, Leila Denise Alves Ferreira, Rosemeire L. Fiaccone, Carlos Antônio S. T. Santos, Tereza Nadya dos Santos, Lia Terezinha L. P. de Moraes, Nelson F. Oliveira, Silvano O. Barbosa i in. "Structural equation modeling in epidemiology". Cadernos de Saúde Pública 26, nr 12 (grudzień 2010): 2251–62. http://dx.doi.org/10.1590/s0102-311x2010001200004.
Pełny tekst źródłaDelmaar, C., H. Bremmer i I. Tuinman. "Experimental Validation of the Consumer Exposure Modeling Tool ConsExpo". Epidemiology 17, Suppl (listopad 2006): S182. http://dx.doi.org/10.1097/00001648-200611001-00460.
Pełny tekst źródłaBell, Michelle. "AIR QUALITY MODELING AS A TOOL FOR HUMAN HEALTH RESEARCH". Epidemiology 15, nr 4 (lipiec 2004): S152. http://dx.doi.org/10.1097/00001648-200407000-00397.
Pełny tekst źródłaKolesnichenko, Olga, Igor Nakonechniy i Yuriy Kolesnichenko. "From digital to quantum epidemiology: The Quantum Data Lake concept for big data related to viral infectious diseases". Global Health Economics and Sustainability 2, nr 1 (20.03.2024): 2148. http://dx.doi.org/10.36922/ghes.2148.
Pełny tekst źródłaAzimaee, Parisa, Mohammad Jafari Jozani i Yaser Maddahi. "Calibration of surgical tools using multilevel modeling with LINEX loss function: Theory and experiment". Statistical Methods in Medical Research 30, nr 6 (13.04.2021): 1523–37. http://dx.doi.org/10.1177/09622802211003620.
Pełny tekst źródłaLimburg, Hans, i Jan E. E. Keunen. "Blindness and low vision in The Netherlands from 2000 to 2020—modeling as a tool for focused intervention". Ophthalmic Epidemiology 16, nr 6 (grudzień 2009): 362–69. http://dx.doi.org/10.3109/09286580903312251.
Pełny tekst źródłaCasado-Vara, Roberto, Marcos Severt, Antonio Díaz-Longueira, Ángel Martín del Rey i Jose Luis Calvo-Rolle. "Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach". Mathematics 12, nr 2 (12.01.2024): 250. http://dx.doi.org/10.3390/math12020250.
Pełny tekst źródłaBen-Hassen, Céline, Catherine Helmer, Claudine Berr i Hélène Jacqmin-Gadda. "Five-Year Dynamic Prediction of Dementia Using Repeated Measures of Cognitive Tests and a Dependency Scale". American Journal of Epidemiology 191, nr 3 (9.11.2021): 453–64. http://dx.doi.org/10.1093/aje/kwab269.
Pełny tekst źródłaKunicki, Zachary J., Meghan L. Smith i Eleanor J. Murray. "A Primer on Structural Equation Model Diagrams and Directed Acyclic Graphs: When and How to Use Each in Psychological and Epidemiological Research". Advances in Methods and Practices in Psychological Science 6, nr 2 (kwiecień 2023): 251524592311560. http://dx.doi.org/10.1177/25152459231156085.
Pełny tekst źródłaOleson, Jacob J., Joseph E. Cavanaugh, J. Bruce Tomblin, Elizabeth Walker i Camille Dunn. "Combining growth curves when a longitudinal study switches measurement tools". Statistical Methods in Medical Research 25, nr 6 (11.07.2016): 2925–38. http://dx.doi.org/10.1177/0962280214534588.
Pełny tekst źródłaRozprawy doktorskie na temat "Epidemiology modeling tool"
Guifo, Fodjo A. Yvan. "Séparation des préoccupations dans les modèles compartimentaux étendus". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS262.
Pełny tekst źródłaMathematical modeling and computer simulation have very often contributed to improving our understanding, prediction, and decision making in the face of epidemics. However, a problem that is often encountered in the development and implementation of epidemiological models is the mixing of different aspects of the model. Indeed, epidemiological models become more and more complex as new concerns are taken into account (age, gender, spatial heterogeneity, containment or vaccination policies, etc.). These aspects, which are usually intertwined, make models difficult to extend, modify or reuse. In mathematical modeling applied to epidemiology, two main approaches are considered. The first one, the "compartmental models", has proven to be robust and provides fairly good results for many diseases. However, it does not take into account some sources of heterogeneity. The second approach, based on "contact networks", has proven to be intuitive to represent contacts between individuals and brings very good results concerning the prediction of epidemics. However, this approach requires more effort during the implementation. A solution to this problem has been proposed: Kendrick. It is a modeling and simulation tool and approach that has shown promising results in separating epidemiological concerns, by defining them as stochastic automata (continuous time markov chain), which can then be combined using an associative and pseudo commutative tensor sum operator. However, a significant limitation of this approach is its restricted application to compartmental models. Taking into account the particularities and shortcomings of each approach, in this research work, we propose a combined approach between compartmental models and contact network models. The aim is to generalize the Kendrick approach to take into account certain aspects of contact networks in order to improve the predictive quality of models with significant heterogeneity in the structure of the contacts, while maintaining the simplicity of compartmental models. To achieve this, this extension of compartmental models is made possible by applying the infection force formalism of Bansal et al (2007) and the behavioral Template Method Design Pattern. The result is an approach that is easy to define, analyze and simulate. We validated this approach on different techniques to generalize compartmental models. Simulation results showed that our approach succeeds in capturing the aspects of contact network models within the compartmental framework while improving the prediction quality of the Kendrick tool and does not deviate from a typical simulation approach on a contact network model
Stevens, Kim Barbra. "Risk-based decision making tools for highly pathogenic avian influenza virus (H5N1) in domestic poultry in Asia : a comparison of spatial-modelling methods". Thesis, Royal Veterinary College (University of London), 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701672.
Pełny tekst źródłaCzęści książek na temat "Epidemiology modeling tool"
Bruaset, Are Magnus, Glenn Terje Lines i Joakim Sundnes. "Chapter 7 Data aggregation and anonymization for mathematical modeling and epidemiological studies". W Simula SpringerBriefs on Computing, 121–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05466-2_7.
Pełny tekst źródłaPerry, Brian, Bernard Bett, Eric Fèvre, Delia Grace i Thomas Fitz Randolph. "Veterinary epidemiology at ILRAD and ILRI, 1987-2018." W The impact of the International Livestock Research Institute, 208–38. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789241853.0208.
Pełny tekst źródłaRichards, Marcus, i Rebecca Hardy. "Life course epidemiology". W Practical Psychiatric Epidemiology, 389–404. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198735564.003.0023.
Pełny tekst źródłaKlepac, Petra, i C. Jessica E. Metcalf. "Demographic methods in epidemiology". W Demographic Methods across the Tree of Life, 351–62. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198838609.003.0022.
Pełny tekst źródłaPrince, Martin. "Statistical methods in psychiatric epidemiology 2: an epidemiologist’ s perspective". W Practical Psychiatric Epidemiology, 275–90. Oxford University Press, 2003. http://dx.doi.org/10.1093/med/9780198515517.003.0015.
Pełny tekst źródłaBueno-Sancho, Vanessa, Clare M. Lewis i Diane G. O. Saunders. "Advances in understanding the biology and epidemiology of rust diseases of cereals". W Achieving durable disease resistance in cereals, 15–38. Burleigh Dodds Science Publishing, 2021. http://dx.doi.org/10.19103/as.2021.0092.02.
Pełny tekst źródłaLamberton, Poppy H. L., Thomas Crellen, James A. Cotton i Joanne P. Webster. "Modelling the Effects of Mass Drug Administration on the Molecular Epidemiology of Schistosomes". W Mathematical Models for Neglected Tropical Diseases: Essential Tools for Control and Elimination, Part A, 293–327. Elsevier, 2015. http://dx.doi.org/10.1016/bs.apar.2014.12.006.
Pełny tekst źródłaVlach, Marek. "Network Modeling of the Spread of Disease". W The Oxford Handbook of Archaeological Network Research, 512–27. Oxford University Press, 2023. http://dx.doi.org/10.1093/oxfordhb/9780198854265.013.29.
Pełny tekst źródłaJosé Becerra, Melgris, i Mariano Araujo Bernardino da Rocha. "Applications of Geotechnologies in the Field of Public Health". W Geographic Information Systems - Data Science Approach. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1003867.
Pełny tekst źródłaWilson, Andrew. "Positioning Computational Modelling in Roman Studies". W Simulating Roman Economies, 308–24. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192857828.003.0012.
Pełny tekst źródłaStreszczenia konferencji na temat "Epidemiology modeling tool"
Mokros, Jan, Jacob Sagrans i Pendred Noyce. "Data science for youth in the time of COVID". W IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.hmtse.
Pełny tekst źródłaSvensson, Elisabeth. "Experiencing the complexity of reality before graduation". W Next Steps in Statistics Education. IASE international Association for Statistical Education, 2009. http://dx.doi.org/10.52041/srap.09202.
Pełny tekst źródłaSautner, J. B., M. L. Maslia i M. M. Aral. "Water-Distribution System Modeling as a Tool to Enhance Epidemiologic Case-Control Investigations: A Case Study–The Dover Township (Toms River) Childhood Cancer Investigation". W 29th Annual Water Resources Planning and Management Conference. Reston, VA: American Society of Civil Engineers, 1999. http://dx.doi.org/10.1061/40430(1999)51.
Pełny tekst źródłaRaporty organizacyjne na temat "Epidemiology modeling tool"
Millington, Kerry, i Samantha Reddin. COVID-19 Health Evidence Summary No.112. Institute of Development Studies (IDS), luty 2021. http://dx.doi.org/10.19088/k4d.2021.021.
Pełny tekst źródłaMillington, Kerry, i Samantha Reddin. COVID-19 Health Evidence Summary No.107. Institute of Development Studies (IDS), styczeń 2021. http://dx.doi.org/10.19088/k4d.2021.002.
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