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In this paper, we have proposed a two-phase procedure (combining discrete graphs and wavelets) for constructing true epidemic growth. In the first phase, a graph-theory-based approach was developed to update partial data available and in the second phase, we used this partial data to generate plausible complete data through wavelets. We have provided two numerical examples. This procedure is novel and implementable and adaptable to machine learning modeling framework.
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Epidemias , Aprendizado de Máquina , Modelos BiológicosRESUMO
Computation of basic reproductive numbers is one of the primary goals of epidemic modelers. There are several challenges in such computations, especially when the data from the virus transmission networks are not so easy to collect; this makes model validation almost impossible. We provide a technical comment on the precautions to be taken while computing model-based basic reproductive numbers so that the ground realities of such computation are maintained. Basic reproductive numbers need to be adjusted retrospectively to compensate for reporting errors within the epidemic spread networks. Such an adjustment would lead to revised pandemic preparedness and mitigation plans.
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We consider the structure of the novel coronavirus (SARS-Cov-2) in terms of the number of spikes that are critical in bonding with the cells in the host. Bonding formation is considered for selection criteria with and without any treatments. Functional mappings from the discrete space of spikes and cells and their analysis are performed. We found that careful mathematical constructions help in understanding the treatment impacts, and the role of vaccines within a host. Smale's famous 2-D horseshoe examples inspired us to create 3-D visualizations and understand the topological diffusion of spikes from one human organ to another organ. The pharma industry will benefit from such an analysis for designing efficient treatment and vaccine strategies.
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We describe topological dynamics over a space by starting from a simple ODE emerging out of two coupled variables. We describe the dynamics of the evolution of points in space within the deterministic and stochastic frameworks. Historically dynamical systems were associated with celestial mechanics. The core philosophies of two kinds of dynamics emerging from Poincaré and Lyapunov are described. Smale's contributions are highlighted. Markovian models are considered. Semi-group actions are a tool in this study.
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We highlight the advantages of virtual tourism and the use of data science to improve existing television and internet-based experiences with new technologies. Information geometry and conformal mappings can improve audiovisual experiences based on drone recordings. The data collection, assimilation, and transformation requirements for a seamless and user-friendly service are discussed along with the precautions to ensure that this technology is used appropriately to protect human safety and the environment.
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COVID-19 , Número Básico de Reprodução , Controle de Doenças Transmissíveis , Humanos , Pandemias , SARS-CoV-2Assuntos
Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Modelos Estatísticos , Pandemias/estatística & dados numéricos , Pneumonia Viral/diagnóstico , Pneumonia Viral/epidemiologia , Adolescente , Adulto , Idoso , Betacoronavirus , COVID-19 , China/epidemiologia , França/epidemiologia , Alemanha/epidemiologia , Humanos , Irã (Geográfico)/epidemiologia , Itália/epidemiologia , Pessoa de Meia-Idade , República da Coreia/epidemiologia , Estudos Retrospectivos , SARS-CoV-2 , Espanha/epidemiologia , Análise de Ondaletas , Adulto JovemRESUMO
PURPOSE: The purpose of this research was to develop a novel quantitative method of describing calvarial shape by using ellipsoid geometry. The pilot application of Ellipsoid Analysis was to compare calvarial form among individuals with untreated unilateral coronal synostosis, metopic synostosis, and sagittal synostosis and normal subjects. METHODS: The frontal, parietal, and occipital bones of 10 preoperative patients for each of the four study groups were bilaterally segmented into six regions using three-dimensional skull reconstructions generated by ANALYZE imaging software from high-resolution computed tomography scans. Points along each segment were extracted and manipulated using a MATLAB-based program. The points were fit to the least-squares nearest ellipsoid. Relationships between the six resultant right and left frontal, parietal, and occipital ellipsoidal centroids (FR, FL, PR, PL, OR, and OL, respectively) were tested for association with a synostotic group. RESULTS: Results from the pilot study showed meaningful differences between length ratio, angular, and centroid distance relationships among synostotic groups. The most substantial difference was exhibited in the centroid distance PL-PR between patients with sagittal synostosis and metopic synostosis. The measures most commonly significant were centroid distances FL-PR and FL-PL and the angle OR-FR-PR. Derived centroid relationships were reproducible. CONCLUSION: Ellipsoid Analysis may offer a more refined approach to quantitative analysis of cranial shape. Symmetric and asymmetric forms can be compared directly. Relevant shape information between traditional landmarks is characterized. These techniques may have wider applicability in quantifying craniofacial morphology with increase in both specificity and general applicability over current methods.