Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates.

We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Thickness accuracy of virtually designed patient-specific implants for large neurocranial defects.

The combination of computer-aided design (CAD) techniques based on computed tomography (CT) data to generate patient-specific implants is in use for decades. However, persisting disadvantages are complicated design procedures and rigid reconstruction protocols, for example, for tailored implants mimicking the patient-specific thickness distribution of missing cranial bone. In this study we used two different approaches, CAD- versus thin-plate spline (TPS)-based implants, to reconstruct extensive unilateral and bilateral cranial defects in three clinical cases. We used CT data of three complete human crania that were virtually damaged according to the missing regions in the clinical cases. In total, we carried out 132 virtual reconstructions and quantified accuracy from the original to the generated implant and deviations in the resulting implant thickness as root-mean-square error (RMSE). Reconstructions using TPS showed an RMSE of 0.08-0.18 mm in relation to geometric accuracy. CAD-based implants showed an RMSE of 0.50-1.25 mm. RMSE in relation to implant thickness was between 0.63 and 0.70 mm (TPS) while values for CAD-based implants were significantly higher (0.63-1.67 mm). While both approaches provide implants showing a high accuracy, the TPS-based approach additionally provides implants that accurately reproduce the patient-specific thickness distribution of the affected cranial region.

Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy.

A known technique to obtain subpixel resolution by using object tracking through cross-correlation consists of interpolating the obtained correlation function and then refining peak location. Although the technique provides accurate results, peak location is usually biased toward the closest integer coordinate. This effect is known as the peak-locking error and it strongly limits this calculation technique's experimental accuracy. This error may differ depending on the scene and algorithm used to fit and interpolate the correlation peak, but in general, it may be attributed to a sampling problem and the presence of aliasing. Many studies in the literature analyze this effect in the Fourier domain. Here, we propose an alternative analysis on the spatial domain. According to our interpretation, the peak-locking error may be produced by a non-symmetrical sample distribution, thus provoking a bias in the result. According to this, the peak interpolant function, the size of the local domain and low-pass filters play a relevant role in diminishing the error. Our study explores these effects on different samples taken from the DIC Challenge database, and the results show that, in general, peak fitting with a Gaussian function on a relatively large domain provides the most accurate results.

Consideration of sample motion in cryo-tomography based on alignment residual interpolation.

Recently, it has been shown that the resolution in cryo-tomography could be improved by considering the sample motion in tilt-series alignment and reconstruction, where a set of quadratic polynomials were used to model this motion. One requirement of this polynomial method is the optimization of a large number of parameters, which may limit its practical applicability. In this work, we propose an alternative method for modeling the sample motion. Starting from the standard fiducial-based tilt-series alignment, the method uses the alignment residual as local estimates of the sample motion at the 3D fiducial positions. Then, a scattered data interpolation technique characterized by its smoothness and a closed-form solution is applied to model the sample motion. The motion model is then integrated in the tomographic reconstruction. The new method improves the tomogram quality similar to the polynomial one, with the important advantage that the determination of the motion model is greatly simplified, thereby overcoming one of the major limitations of the polynomial model. Therefore, the new method is expected to make the beam-induced motion correction methodology more accessible to the cryoET community.

Magnetometer Calibration and Field Mapping through Thin Plate Splines.

While the undisturbed Earth's magnetic field represents a fundamental information source for orientation purposes, magnetic distortions have been mostly considered as a source of error. However, when distortions are temporally stable and spatially distinctive, they could provide a unique magnetic landscape that can be used in different applications, from indoor localization to sensor fusion algorithms for attitude estimation. The main purpose of this work, therefore, is to present a method to characterize the 3D magnetic vector in every point of the measurement volume. The possibility of describing the 3D magnetic field map through Thin Plate Splines (TPS) interpolation is investigated and demonstrated. An algorithm for the simultaneous estimation of the parameters related to magnetometer calibration and those describing the magnetic map, is proposed and tested on both simulated and real data. Results demonstrate that an accurate description of the local magnetic field using TPS interpolation is possible. The proposed procedure leads to errors in the estimation of the local magnetic direction with a standard deviation lower than 1 degree. Magnetometer calibration and magnetic field mapping could be integrated into different algorithms, for example to improve attitude estimation in highly distorted environments or as an aid to indoor localization.

A deformable template method for describing and averaging the anatomical variation of the human nasal cavity.

BACKGROUND: Understanding airflow through human airways is of importance in drug delivery and development of assisted breathing methods. In this work, we focus on development of a new method to obtain an averaged upper airway geometry from computed tomography (CT) scans of many individuals. This geometry can be used for air flow simulation. We examine the geometry resulting from a data set consisting of 26 airway scans. The methods used to achieve this include nasal cavity segmentation and a deformable template matching procedure. METHODS: The method uses CT scans of the nasal cavity of individuals to obtain a segmented mesh, and coronal cross-sections of this segmented mesh are taken. The cross-sections are processed to extract the nasal cavity, and then thinned ('skeletonized') representations of the airways are computed. A reference template is then deformed such that it lies on this thinned representation. The average of these deformations is used to obtain the average geometry. Our procedure tolerates a wider variety of nasal cavity geometries than earlier methods. RESULTS: To assess the averaging method, key landmark points on each of the input scans as well as the output average geometry are located and compared with one another, showing good agreement. In addition, the cross-sectional area (CSA) profile of the nasal cavities of the input scans and average geometry are also computed, showing that the CSA of the average model falls within the variation of the population. CONCLUSIONS: The use of a deformable template method for aligning and averaging the nasal cavity provides an improved, detailed geometry that is unavailable without using deformation.

Medical image alignment based on landmark- and approximate contour-matching.

Purpose: Our goal is to propose a landmark- and contour-matching (LCM) registration method that uses both landmark information and approximate point correspondences to boost the similarity between image pairs with sparse landmark information. Approach: A model for registering two-dimensional (2D) medical images with landmark information and contour-approximating landmarks was proposed. The model was also extended to accommodate the registration of three-dimensional (3D) cardiac images. We validated the LCM method on 2D hand x-rays and 3D porcine cardiac magnetic resonance images. The following metrics were used to assess the quality of specific aspects of the registered images: Dice similarity coefficient for the overall image overlap, target registration error for pointwise correspondence, and interior angle for local curvature. Results: Target registrations were reduced from 27.12 to 0.01 mm post-LCM registration. Implementing the proposed algorithm also led to a 112% average improvement in image similarity in terms of Dice coefficients. In addition, interior angle measurements indicate that the proposed method preserved the local curvature at major reference landmarks and mitigated the appearance of deformities in the registered images. Conclusions: The proposed method addressed several issues associated with purely landmark-based techniques, such as iterative closest point registration and thin plate spline interpolation. Furthermore, it provided accurate registration results even in the presence of landmark localization errors.

Flexible Bayesian excess hazard models using low-rank thin plate splines.

Excess hazard models became the preferred modelling tool in population-based cancer survival research. In this setting, the model is commonly formulated as the additive decomposition of the overall hazard into two components: the excess hazard due to the cancer of interest and the population hazard due to all other causes of death. We introduce a flexible Bayesian regression model for the log-excess hazard where the baseline log-excess hazard and any non-linear effects of covariates are modelled using low-rank thin plate splines. Using this type of splines will ensure that the log-likelihood function retains tractability not requiring numerical integration. We demonstrate how to derive posterior distributions for the excess hazard and for net survival, a population-level measure of cancer survival that can be derived from excess hazard models. We illustrate the proposed model using survival data for patients diagnosed with colon cancer during 2009 in London, England.

Parametrization of white matter manifold-like structures using principal surfaces.

In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. The problem is approached by finding a geometrically motivated surface-based representation of the corpus callosum and visualized fractional anisotropy (FA) values projected onto the surface. The method also applies to any other diffusion summary. An algorithm is proposed that 1) constructs the principal surface of a corpus callosum; 2) flattens the surface into a parametric 2D map; 3) projects associated FA values on the map. The algorithm is applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.

A physically based trunk soft tissue modeling for scoliosis surgery planning systems.

One of the major concerns of scoliotic patients undergoing spinal correction surgery is the trunk's external appearance after the surgery. This paper presents a novel incremental approach for simulating postoperative trunk shape in scoliosis surgery. Preoperative and postoperative trunk shapes data were obtained using three-dimensional medical imaging techniques for seven patients with adolescent idiopathic scoliosis. Results of qualitative and quantitative evaluations, based on the comparison of the simulated and actual postoperative trunk surfaces, showed an adequate accuracy of the method. Our approach provides a candidate simulation tool to be used in a clinical environment for the surgery planning process.

Reduced-Rank Spatio-Temporal Modeling of Air Pollution Concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution.

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO x )-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated R2. Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.