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Zeitschriftenartikel zum Thema "Epidemiology modeling tool"
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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 et al. „Structural equation modeling in epidemiology“. Cadernos de Saúde Pública 26, Nr. 12 (Dezember 2010): 2251–62. http://dx.doi.org/10.1590/s0102-311x2010001200004.
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Structural equation modeling (SEM) is an important statistical tool for evaluating complex relations in several research areas. In epidemiology, the use and discussion of SEM have been limited thus far. This article presents basic principles and concepts in SEM, including an application using epidemiological data analysis from a study on the determinants of cognitive development in young children, considering constructs related to organization of the child's home environment, parenting style, and the child's health status. The relations between the constructs and cognitive development were measured. The results showed a positive association between psychosocial stimulus at home and cognitive development in young children. The article presents the contributions by SEM to epidemiology, highlighting the need for an a priori theoretical model for improving the study of epidemiological questions from a new perspective.
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Delmaar, C., H. Bremmer und I. Tuinman. „Experimental Validation of the Consumer Exposure Modeling Tool ConsExpo“. Epidemiology 17, Suppl (November 2006): S182. http://dx.doi.org/10.1097/00001648-200611001-00460.
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Bell, Michelle. „AIR QUALITY MODELING AS A TOOL FOR HUMAN HEALTH RESEARCH“. Epidemiology 15, Nr. 4 (Juli 2004): S152. http://dx.doi.org/10.1097/00001648-200407000-00397.
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Kolesnichenko, Olga, Igor Nakonechniy und 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.
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The development of quantum epidemiology represents the next anticipated phase in epidemiology transformation, driven by the emergence of new quantum technologies. Epidemiology is currently transitioning into the digital era and undergoing a paradigm shift from a data-driven to a value-driven strategy. Epidemiology data are characterized by uncertainty, multidimensionality, and disconnection, thereby correlating with the preferential quantum approach for data exposition, value creation, and modeling. Examples of such complex epidemiology data include the data on DNA viruses with associated symptoms and diseases. The Quantum Data Lake concept is proposed and consists of several layers and quantum tools, including Robson semantic triples, Quantum Universal Exchange Language, Hyperbolic Dirac Net, “quantum ribosome” structure, quantum random access memory, teleportation, Quantum Query Language, non-Hermitian gates, and tensor networks (e.g., matrix product state, projected entangled pair state, and multiscale entanglement renormalization ansatz [MERA]), alongside PT-symmetry properties. PT-symmetry can serve as an intuitive modeling tool, and PT-symmetry breaking can detect the hidden shift in the information that is permanently updated in the Data Lake. The computational output is presented as PT-symmetry gain/loss equilibrium breaking in the form of a complex number, i.e., two possible variants of epidemic modeling. For MERA, non-Hermiticity with spontaneous PT-symmetry breaking can theoretically appear as a violation of the entanglement monotonicity and local entanglement gain, leading to a non-reversible character of the coarse-graining transformation. The duality of PT-symmetry equilibrium breaking can be compared to, for example, the estimation of the best and worst scenarios simultaneously, or the gain of entanglement can display a significant correlation between some studied parameters embedded into the data. The fundamental difference between digital and quantum epidemiology is the implementation of quantum logic and reliance on a quantum theory.
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Azimaee, Parisa, Mohammad Jafari Jozani und 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.
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Quantifying the tool–tissue interaction forces in surgery can be used in the training process of novice surgeons to help them better handle surgical tools and avoid exerting excessive forces. A significant challenge concerns the development of proper statistical learning techniques to model the relationship between the true force exerted on the tissue and several outputs read from sensors mounted on the surgical tools. We propose a nonparametric bootstrap technique and a Bayesian multilevel modeling methodology to estimate the true forces. We use the linear exponential loss function to asymmetrically penalize the over and underestimation of the applied forces to the tissue. We incorporate the direction of the force as a group factor in our analysis. A weighted approach is used to account for the nonhomogeneity of read voltages from the surgical tool. Our proposed Bayesian multilevel models provide estimates that are more accurate than those under the maximum likelihood and restricted maximum likelihood approaches. Moreover, confidence bounds are much narrower and the biases and root mean squared errors are significantly smaller in our multilevel models with the linear exponential loss function.
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Limburg, Hans, und 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 (Dezember 2009): 362–69. http://dx.doi.org/10.3109/09286580903312251.
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Casado-Vara, Roberto, Marcos Severt, Antonio Díaz-Longueira, Ángel Martín del Rey und 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.
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With the progress and evolution of the IoT, which has resulted in a rise in both the number of devices and their applications, there is a growing number of malware attacks with higher complexity. Countering the spread of malware in IoT networks is a vital aspect of cybersecurity, where mathematical modeling has proven to be a potent tool. In this study, we suggest an approach to enhance IoT security by installing security updates on IoT nodes. The proposed method employs a physically informed neural network to estimate parameters related to malware propagation. A numerical case study is conducted to evaluate the effectiveness of the mitigation strategy, and novel metrics are presented to test its efficacy. The findings suggest that the mitigation tactic involving the selection of nodes based on network characteristics is more effective than random node selection.
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Ben-Hassen, Céline, Catherine Helmer, Claudine Berr und 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 (09.11.2021): 453–64. http://dx.doi.org/10.1093/aje/kwab269.
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Abstract The progression of dementia prevalence over the years and the lack of efficient treatments to stop or reverse the cognitive decline make dementia a major public health challenge in the developed world. Identifying people at high risk of developing dementia could improve the treatment of these patients and help select the target population for preventive clinical trials. We used joint modeling to build a dynamic prediction tool of dementia based on the change over time of 2 neurocognitive tests (the Mini-Mental State Examination and the Isaacs Set Tests) as well as an autonomy scale (the Instrumental Activities of Daily Living). The model was estimated with data from the French cohort Personnes Agées QUID (1988–2015) and validated both by cross-validation and externally with data from the French Three City cohort (1999–2018). We evaluated its predictive abilities through area under the receiver operating characteristics curve and Brier score, accounting for right censoring and competing risk of death, and obtained an average area under the curve value of 0.95 for the risk of dementia in the next 5 or 10 years. This tool is able to discriminate a high-risk group of people from the rest of the population. This could be of help in clinical practice and research.
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Kunicki, Zachary J., Meghan L. Smith und 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 (April 2023): 251524592311560. http://dx.doi.org/10.1177/25152459231156085.
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Many psychological researchers use some form of a visual diagram in their research processes. Model diagrams used with structural equation models (SEMs) and causal directed acyclic graphs (DAGs) can guide causal-inference research. SEM diagrams and DAGs share visual similarities, often leading researchers familiar with one to wonder how the other differs. This article is intended to serve as a guide for researchers in the psychological sciences and psychiatric epidemiology on the distinctions between these methods. We offer high-level overviews of SEMs and causal DAGs using a guiding example. We then compare and contrast the two methodologies and describe when each would be used. In brief, SEM diagrams are both a conceptual and statistical tool in which a model is drawn and then tested, whereas causal DAGs are exclusively conceptual tools used to help guide researchers in developing an analytic strategy and interpreting results. Causal DAGs are explicitly tools for causal inference, whereas the results of a SEM are only sometimes interpreted causally. A DAG may be thought of as a “qualitative schematic” for some SEMs, whereas SEMs may be thought of as an “algebraic system” for a causal DAG. As psychology begins to adopt more causal-modeling concepts and psychiatric epidemiology begins to adopt more latent-variable concepts, the ability of researchers to understand and possibly combine both of these tools is valuable. Using an applied example, we provide sample analyses, code, and write-ups for both SEM and causal DAG approaches.
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Oleson, Jacob J., Joseph E. Cavanaugh, J. Bruce Tomblin, Elizabeth Walker und 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.
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When longitudinal studies are performed to investigate the growth of traits in children, the measurement tool being used to quantify the trait may need to change as the subjects’ age throughout the study. Changing the measurement tool at some point in the longitudinal study makes the analysis of that growth challenging which, in turn, makes it difficult to determine what other factors influence the growth rate. We developed a Bayesian hierarchical modeling framework that relates the growth curves per individual for each of the different measurement tools and allows for covariates to influence the shapes of the curves by borrowing strength across curves. The method is motivated by and demonstrated by speech perception outcome measurements of children who were implanted with cochlear implants. Researchers are interested in assessing the impact of age at implantation and comparing the growth rates of children who are implanted under the age of two versus those implanted between the ages of two and four.
Dissertationen zum Thema "Epidemiology modeling tool"
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Guifo, Fodjo A. Yvan. „Séparation des préoccupations dans les modèles compartimentaux étendus“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS262.
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La modélisation mathématique et la simulation informatique ont très souvent contribué à améliorer notre degré de compréhension, de prédiction et de prise de décision face aux épidémies. Cependant, un problème très souvent rencontré lors de l’élaboration et la mise en œuvre des modèles épidémiologiques est le mélange des différents aspects du modèle. En effet, les modèles épidémiologiques deviennent de plus en plus complexes au fur et à mesure que de nouvelles préoccupations sont prises en compte (âge, sexe, hétérogénéité spatiale, politiques de confinement ou de vaccination, etc.). Ces aspects, qui sont généralement entrelacés, rendent les modèles difficiles à étendre, à modifier ou à réutiliser. En modélisation mathématique appliquée à épidémiologie deux principales approches sont considérées. La première, celle des "modèles compartimentaux" s’est révélée robuste et apporte d’assez bons résultats pour de nombreuses maladies. Cependant elle prend difficilement en compte certaines sources d’hétérogénéités. La seconde approche, celle basée sur les "réseaux de contacts", s’est montrée intuitive à représenter les contacts entre individus et apporte de très bons résultats concernant la prédiction des épidémies. Toutefois, cette approche nécessite plus d’efforts lors de la mise en œuvre. Une solution a été proposée pour y faire face : il s’agit de Kendrick. C'est un outil et une démarche de modélisation et de simulation ayant montré des résultats prometteurs pour séparer les préoccupations épidémiologiques, en les définissant comme des automates stochastiques (chaîne de Markov à temps continu), qu’il est ensuite possible de combiner à partir d'un opérateur de somme tensorielle associatif et pseudo commutatif. Cependant, une limite significative à cette démarche est son application restreinte aux modèles compartimentaux. Compte tenu des particularités et des insuffisances de chaque approche, dans ces travaux de recherche, nous proposons une approche combinée entre modèles compartimentaux et modèles de réseaux de contacts. Il s'agit de généraliser la démarche Kendrick pour prendre en compte certains aspects des réseaux de contacts afin améliorer la qualité prédictive des modèles présentant une hétérogénéité significative dans la structure des contacts, tout en conservant la simplicité des modèles compartimentaux. Pour y parvenir, cette extension des modèles compartimentaux est rendu possible à partir de l'application du formalisme de la force d'infection de Bansal et al (2007) et du patron comportemental Template Method Design Pattern. Il en résulte une démarche facile à définir, à analyser et à simuler. Nous avons validé cette démarche sur différentes techniques pour généraliser les modèles compartimentaux. Les résultats de simulation ont montré que notre démarche parvient à capturer les aspects des modèles de réseaux de contacts au sein du cadre compartimental tout en améliorant la qualité de prédiction de l'outil Kendrick et ne s'éloignent pas d'une approche typique de simulation sur un modèle de réseaux de contactsMathematical 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
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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.
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Bruaset, Are Magnus, Glenn Terje Lines und Joakim Sundnes. „Chapter 7 Data aggregation and anonymization for mathematical modeling and epidemiological studies“. In Simula SpringerBriefs on Computing, 121–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05466-2_7.
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AbstractAn important secondary purpose of the Smittestopp development was to provide aggregated data sets describing mobility and social interactions in Norway’s population. The data were to be used to monitor the effect of government regulations and recommendations, provide input to advanced computational models to predict the pandemic’s spread, and provide input to fundamental epidemiology research. In this chapter we describe the challenges and technical solutions of Smittestopp’s data aggregation, as well as preliminary results from the time period when the app was active.We first give a detailed overview of the requirements, specifying the types of data to be collected and the level of spatial and temporal aggregation. We then proceed to describe the concepts for anonymization via :-anonymity and Y-differential privacy (Y-DP ), and the technical solutions for collecting and aggregating data from the database. In particular, we present details of how GPS- and Bluetooth events were mapped to geographical regions and points of interest, and the solutions employed for efficient data retrieval and processing. The preliminary results demonstrate how the recorded GPS- and Bluetooth events match with expected temporal and spatial variations in mobility and social interactions, and indicate the usefulness of the aggregated data as a tool for pandemic monitoring and research. One of the main criticisms of Smittestopp concerns the centralized storage of individuals’ movements, even if such data were used and presented only at an aggregated and anonymized level. In this chapter, we also outline a completely different approach, where the GPS data do not leave the user’s phone but are, instead, pre-processed to a much higher level of privacy before being dispatched to a server-side data aggregation algorithm. This approach, which would make the app significantly less intrusive, is made possible by recent advances in determining close contacts from Bluetooth data, either by a revised Smittestopp algorithm or by means of the Google/Apple Exposure Notification framework.
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Perry, Brian, Bernard Bett, Eric Fèvre, Delia Grace und Thomas Fitz Randolph. „Veterinary epidemiology at ILRAD and ILRI, 1987-2018.“ In The impact of the International Livestock Research Institute, 208–38. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789241853.0208.
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Abstract This chapter describes the activities of the International Livestock Research Institute (ILRI) and its predecessor, the International Laboratory for Research on Animal Diseases (ILRAD) from 1987 to 2018. Topics include scientific impacts; economic impact assessment; developmental impacts; capacity development; partnerships; impacts on human resources capacity in veterinary epidemiology; impacts on national animal health departments and services; impacts on animal health constraints in developing countries; impacts on ILRI's research and strategy; the introduction of veterinary epidemiology and economics at ILRAD; field studies in Kenya; tick-borne disease dynamics in eastern and southern Africa; heartwater studies in Zimbabwe; economic impact assessments of tick-borne diseases; tick and tick-borne disease distribution modelling; modelling the infection dynamics of vector-borne diseases; economic impact of trypanosomiasis; the epidemiology of resistance to trypanocides; the development of a modelling technique for evaluating control options; sustainable trypanosomiasis control in Uganda and in the Ghibe Valley of Ethiopia; spatial modelling of tsetse distributions; preventing and containing trypanocide resistance in the cotton zone of West Africa; rabies research; the economic impacts of rinderpest control; applying economic impact assessment tools to foot and mouth disease (FMD) control, the southern Africa FMD economic impact study; economic impacts of FMD in Peru, Colombia and India; economic impacts of FMD control in endemic settings in low- and middle-income countries; the global FMD research alliance (GFRA); Rift Valley fever; economic impact assessment of control options and calculation of disability-adjusted life years (DALYs); RVF risk maps for eastern Africa; land-use change and RVF infection and disease dynamics; epidemiology of gastrointestinal parasites; priorities in animal health research for poverty reduction; the Wellcome Trust Epidemiology Initiatives; the broader economic impact contributions; the responses to highly pathogenic avian influenza; the International Symposium on Veterinary Epidemiology and Economics (ISVEE) experience, the role of epidemiology in ILRAD and ILRI and the impacts of ILRAD and ILRI's epidemiology; capacity development in veterinary epidemiology and impact assessment; impacts on national animal health departments and services; impacts on animal health constraints in developing countries and impacts on ILRI's research and strategy.
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Richards, Marcus, und Rebecca Hardy. „Life course epidemiology“. In Practical Psychiatric Epidemiology, 389–404. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198735564.003.0023.
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Types of psychiatric disorders vary with respect to age of onset, temporal continuity, and impact. Life course epidemiology provides powerful tools for understanding these complexities. This discipline broadly distinguishes ‘sensitive period’ and ‘risk accumulation’ models. The former refers to optimum windows for exposure (e.g. early life for some psychoses, in contrast to proximal exposures for depression). Accumulation refers to additive or multiplicative effects of multiple exposures, exemplified by stress process and chain of risk models. The preeminent study design for these approaches is the prospective longitudinal birth cohort study, especially where multiple cohorts help to distinguish period and cohort effects. However, limitations such as balancing the need for repeated versus age-appropriate measurement, and non-random missing data, must be carefully considered. While the statistical workhorse for life course epidemiology is general linear modelling, this discipline also requires advanced tools such as random effects, path, latent class, and latent growth modelling.
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Klepac, Petra, und C. Jessica E. Metcalf. „Demographic methods in epidemiology“. In Demographic Methods across the Tree of Life, 351–62. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198838609.003.0022.
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Demography is both shaped by and shapes infectious disease dynamics. Infectious pathogens can increase host mortality. Host birth rates introduce new susceptible individuals into the population, which allows infections to persist in the face of the depletion of susceptible individuals that can result from mortality or immunity that can follow infection. Many important processes in infectious disease epidemiology, from transmission to vaccination, vary as a function of age or life stage. Epidemiology thus requires demographic methods. This chapter introduces broad expectations for patterns emerging from the intersection between demography and epidemiology and presents a set of structured population modelling tools that can be used to dissect important processes, including next generation methods, and estimation of R0 in the context of stage structure and with important differences in time-scale between host demography and pathogen life cycle.
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Prince, Martin. „Statistical methods in psychiatric epidemiology 2: an epidemiologist’ s perspective“. In Practical Psychiatric Epidemiology, 275–90. Oxford University Press, 2003. http://dx.doi.org/10.1093/med/9780198515517.003.0015.
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We hope that these two chapters, while providing students with greater confidence in approaching the analysis of their data sets, will also have raised as many questions as they have provided answers. It should by now be evident that there is no single, set, correct way to analyse a given data set; many will argue with some of the approaches advocated in these chapters. The important thing is for students to be aware of the diversity of methods currently available, and to proceed judiciously in the analysis and inferencing of their data, constantly aware of the strengths and limitations of the techniques that they are using. Also, it is important to recognize that biostatistics is a constantly and rapidly evolving discipline. The introduction of logistic regression in the 1970s revolutionized modern epidemiology, influencing the design of our studies as well as the methods used to analyse them. The more recent development of multi-level modelling is likely to have a similarly profound effect upon the type of research questions that we formulate, as well as the designs that we use to test these new hypotheses. Statistical methods are therefore not just the tools that statisticians, working with epidemiologists use to analyse data. They also, as they develop drive the research agenda and influence all aspects of methodology. Ever increasing collaboration between biostatisticians, epidemiologists, and clinical researchers is therefore essential if the full creative potential of this momentum is to be realized.
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Bueno-Sancho, Vanessa, Clare M. Lewis und Diane G. O. Saunders. „Advances in understanding the biology and epidemiology of rust diseases of cereals“. In Achieving durable disease resistance in cereals, 15–38. Burleigh Dodds Science Publishing, 2021. http://dx.doi.org/10.19103/as.2021.0092.02.
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Rust fungi (order: Pucciniales) constitute the largest group of plant parasitic fungi and include many species of agricultural importance. This includes the three wheat rust fungi (Puccinia graminis f. sp. tritici, Puccinia striiformis f. sp. tritici and Puccinia triticina) that have posed a threat to crop production throughout history. This chapter provides an overview of the wheat rust pathogen lifecycle that has been critical to the design of effective disease management strategies and discusses recent integration of basic biological knowledge and genomic-led tools within an epidemiological framework. Furthermore, we include a case study on the “field pathogenomics” technique, illustrating the value of genomic-based tools in disease surveillance activities. Bringing together advances in understanding basic pathogen biology, developments in modelling for disease forecasting and identification, alongside genomic-led advances in surveillance and resistance gene cloning, holds great promise for curtailing the threat of these notorious pathogens.
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Lamberton, Poppy H. L., Thomas Crellen, James A. Cotton und Joanne P. Webster. „Modelling the Effects of Mass Drug Administration on the Molecular Epidemiology of Schistosomes“. In 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.
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Vlach, Marek. „Network Modeling of the Spread of Disease“. In The Oxford Handbook of Archaeological Network Research, 512–27. Oxford University Press, 2023. http://dx.doi.org/10.1093/oxfordhb/9780198854265.013.29.
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Abstract The presence of various epidemic diseases can be expected within past human populations. They are well attested through vivid narratives of literary-rich civilizations such as the Roman empire as well as the 2020 pandemic. Traditionally, much of the study of such phenomena has been anchored in paleopathological evidence from skeletal remains. Nevertheless, like the integration of methodological tools such as social network analysis in archaeological studies, network concepts have become important for modeling in epidemiology. Epidemiological modeling has developed various methodological approaches after nearly a century of development. Early approaches were dominated by so-called compartmental models that used various forms and concepts of population structure, which have been gradually complemented with analyses of more complex structures through network analyses. Heterogeneous contact patterns of connections have already proven that the structure of communication networks significantly conditions the resulting epidemic dynamics and its impact. Therefore, methodological intersections between network analyses and epidemiological models render great potential for future studies of past epidemics. Formalization of the featuring entities (e.g. individuals, communities, or entire cities) through their position within a multilevel social network provides a framework to analyze our qualitative and quantitative assumptions about disease transmission. Despite the presence of empirical paleopathological datasets, independent validation of network models using this data is still scarce. New possibilities in pathogen identification—e.g. genomics—could help to bridge future gaps between our theoretical models and empirical data.
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José Becerra, Melgris, und Mariano Araujo Bernardino da Rocha. „Applications of Geotechnologies in the Field of Public Health“. In Geographic Information Systems - Data Science Approach. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1003867.
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This chapter discusses the role of epidemiology and the importance of spatial analysis in understanding patterns of disease occurrence in human populations. Epidemiologists use inductive and deductive approaches to investigate the relationships between risk factors and health outcomes, using advanced techniques such as factor analysis, multilevel modeling, and causal diagrams. Spatial analysis plays a crucial role in medical geography by identifying a disease’s spatial and temporal distribution. Methods such as point, line, and surface patterns are used to analyze spatial clusters, connections, and trends in disease distribution. These techniques provide valuable information for public health decision-making. The COVID-19 pandemic has highlighted the importance of spatial analysis, using geographic information systems and web-based tools to track the spread of the virus. Advances in geoprocessing techniques, particularly geographic information system (GIS), have transformed medical geography. GIS makes it possible to describe, analyze, and predict spatial patterns by integrating data from different sources and spatial scales. These tools facilitate the creation of spatially explicit variables and allow the exploration of spatial groupings and relationships in the data. Considering the spatial context is essential to understand the determinants of health and the influence of the environment on individual and collective health.
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Wilson, Andrew. „Positioning Computational Modelling in Roman Studies“. In Simulating Roman Economies, 308–24. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192857828.003.0012.
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Abstract This final discussion chapter attempts to set out a way forward for the use of computational models in studies of the Roman economy, and more widely. In 2016 Greg Woolf correctly identified a lack of useful datasets for Social Network Analysis (SNA) as one of the limitations of that field. But for modelling the Roman economy, many more datasets are available than for SNA, and recent years have seen an increase in their numbers and comprehensiveness. This trend will only be helped by the Open Access movement, by moves towards Open Archaeology Data, and by the development of Linked Open Data protocols which allow cross-searching across multiple databases from different sources. Model libraries are available not only to enable researchers to test other modellers’ claims but also to encourage model re-use, improvement, adaptation, and extension. Computing power, accessible software, a growing array of datasets, and model libraries all combine to lower the barriers to getting into this kind of research. Computational modelling of important aspects of the Roman economy is already possible, as the chapters in this volume show, and the field offers a powerful set of tools to analyse an array of variables with a bearing on questions of movement, distribution, connectivity, production, trade, coin circulation, etc. The purpose of the discussion here is not to attempt to summarize the findings of earlier chapters but to sketch some avenues for how some of these questions might be pursued further, especially under the headings of: agriculture; transport, distribution, connectivity, and trade; demography; and epidemiology. A review of useful source datasets is given, together with a discussion of desiderata.
Konferenzberichte zum Thema "Epidemiology modeling tool"
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Mokros, Jan, Jacob Sagrans und Pendred Noyce. „Data science for youth in the time of COVID“. In 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.
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Through the “COVID-Inspired Data Science through Epidemiology Education” project, 400 underserved middle-school youth across the United States are engaging in a 20-hour out-of-school data club centered on a novel. The narrative is integrated with hands-on data activities and modeling (e.g., creating graphs of infections over time in CODAP; modeling disease transmission rates in NetLogo). Youth learn to: 1) Use data tools to track the spread of a variety of infectious diseases; 2) Ask and address their own questions of data; and 3) Use data to communicate to local audiences about epidemiological patterns and challenges. The project breaks new ground in integrating data science with epidemiology education for 11–14-year-old youth.
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Svensson, Elisabeth. „Experiencing the complexity of reality before graduation“. In Next Steps in Statistics Education. IASE international Association for Statistical Education, 2009. http://dx.doi.org/10.52041/srap.09202.
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The curriculum for undergraduate students offers not only basic statistics courses but also optional courses regarding statistical methods for times series, modelling, epidemiology, econometrics and other topics potentially useful in a statistician’s career. This means that the students believe that they will get a comprehensive statistical toolbox for solving a variety of real life problems after graduation. But can they use the tools in a complex reality? The aim is to present the use of inter-disciplinary statistical problem solving courses for introducing the complexity of reality to statistics students before graduation. Experiences of the discordance between students’ theoretical and practical skills regarding statistical description, analysis and understanding will be given.
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Sautner, J. B., M. L. Maslia und 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“. In 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Epidemiology modeling tool"
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Millington, Kerry, und Samantha Reddin. COVID-19 Health Evidence Summary No.112. Institute of Development Studies (IDS), Februar 2021. http://dx.doi.org/10.19088/k4d.2021.021.
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This weekly COVID-19 health evidence summary (HES) is based on 3.5 hours of desk-based research. The summary is not intended to be a comprehensive summary of available evidence on COVID-19 but aims to make original documents easily accessible to decision-makers which, if relevant to them, they should go to before making decisions. This summary covers publications on Epidemiology and modelling; Therapeutics; Vaccines; Indirect impact of COVID-19; Comments, Editorials, Opinions, Blogs, News; Guidelines, Statements & Tools; Dashboards & Trackers; C19 Resource Hubs; and Online learning & events.
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Millington, Kerry, und Samantha Reddin. COVID-19 Health Evidence Summary No.107. Institute of Development Studies (IDS), Januar 2021. http://dx.doi.org/10.19088/k4d.2021.002.
Der volle Inhalt der QuelleAnnotation:
This weekly COVID-19 health evidence summary (HES) is based on 3.5 hours of desk-based research. The summary is not intended to be a comprehensive summary of available evidence on COVID-19 but aims to make original documents easily accessible to decision-makers which, if relevant to them, they should go to before making decisions. This summary covers publications on Clinical characteristics and management; Epidemiology and modelling; Infection Prevention and Control; Therapeutics; Vaccines; Indirect impact of COVID-19; Social Science; Comments, Editorials, Opinions, Blogs, News; Guidelines, Statements & Tools; Dashboards & Trackers; C19 Resource Hubs; and Online learning & events