Spatial analysis of COVID-19 and socio-economic factors in Sri Lanka

Using data from the Epidemiological Department of Sri Lanka, a cluster analysis was carried out based on COVID-19 data and demographic data of districts, towards developing a mathematical model that can identify and describe socio-economic factors related to pandemic measures. Population and population density, monthly expenditure, and education level are suggested as main factors for policy makers consideration. Findings can support future evidence-based COVID-19 policies, and further utilized as a foundation for other epidemiological models. A challenge in the study was the presumed disparity between actual COVID-19 cases and observed COVID-19 cases, thereby depicting an inaccurate measure of COVID-19 severity

A theory for ecological survey methods to map individual distributions

Spatially explicit approaches are widely recommended for ecosystem management. The quality of the data, such as presence/absence or habitat maps, affects the management actions recommended and is, therefore, key to management success. However, available data are often biased and incomplete. Previous studies have advanced ways to resolve data bias and missing data, but questions remain about how we design ecological surveys to develop a dataset through field surveys. Ecological surveys may have multiple spatial scales, including the spatial extent of the target ecosystem (observation window), the resolution for mapping individual distributions (mapping unit), and the survey area within each mapping unit (sampling unit). We developed an ecological survey method for mapping individual distributions by applying spatially explicit stochastic models. We used spatial point processes to describe individual spatial placements using either random or clustering processes. We then designed ecological surveys with different spatial scales and individual detectability. We found that the choice of mapping unit affected the presence mapped fraction, and the fraction of the total individuals covered by the presence mapped patches. Tradeoffs were found between these quantities and the map resolution, associated with equivalent asymptotic behaviors for both metrics at sufficiently small and large mapping unit scales. Our approach enabled consideration of the effect of multiple spatial scales in surveys, and estimation of the survey outcomes such as the presence mapped fraction and the number of individuals situated in the presence detected units. The developed theory may facilitate management decision-making and inform the design of monitoring and data gathering

Prediction of the flowing bottom-hole pressure using advanced data analytics

Flowing bottom-hole pressure (FBHP) is a key metric for optimising coal seam gas well performance and enhancement of production. Downhole pressure gauges are increasingly being used to measure the FBHP. However, they are impractical, expensive, and complex to install and maintain. Consequently, reliable measurement and prediction of the FBHP, required to forecast well production, remains a challenge. This paper aims to predict the flowing bottom-hole pressure in coal seam gas wells by taking advantage of the temporal data and advanced analytics. Data-driven models have been developed to predict the FBHP by leveraging the temporal data gathered at the surface in order to control the performance of the wells. The data used in the study was obtained from five coal seam gas wells containing seven sensor measurements gathered over 15-18 months production period. For the prediction of FBHP, we applied linear regression and neural network-based approaches. Overall, neural networks resulted in the best predictions with the root mean squared error (RMSE) within 198-450 kPa for the five wells

Beyond Brownian motion and the ornstein-uhlenbeck process: stochastic diffusion models for the evolution of quantitative characters

Gaussian processes, such as Brownian motion and the Ornstein-Uhlenbeck process, have been popular models for the evolution of quantitative traits and are widely used in phylogenetic comparative methods. However, they have drawbacks that limit their utility. Here we describe new, non-Gaussian stochastic differential equation (diffusion) models of quantitative trait evolution. We present general methods for deriving new diffusion models and develop new software for fitting non-Gaussian evolutionary models to trait data. The theory of stochastic processes provides a mathematical framework for understanding the properties of current and future phylogenetic comparative methods. Attention to the mathematical details of models of trait evolution and diversification may help avoid some pitfalls when using stochastic processes to model macroevolution

Nonlinear features for single-channel diagnosis of sleep-disordered breathing diseases

Studies have shown that algorithms based on single-channel airflow records are effective in screening for sleep-disordered breathing diseases (SDB). In this study, we investigate the diagnostic effectiveness of a classifier trained on a set of features derived from single-channel airflow measurements. The features considered are based on recurrence quantification analysis (RQA) of the measurement time series and are optionally augmented with single measurements of neck circumference and body mass index. The airflow measurement utilized is the nasal pressure (NP). The study used an overnight recording from each of 77 patients undergoing PSG testing. Mixture discriminant analysis was used to obtain a classifier, which predicts whether or not a measurement segment contains an SDB event. Patients were diagnosed as having SDB disease if the recording contained measurement segments predicted to include an SDB event at a rate exceeding a threshold value. A patient can be diagnosed as having SDB disease if the rate of SDB events per hour of sleep, the respiratory disturbance index (RDI), is >= 15 or sometimes >= 5. Here we trained and evaluated the classifier under each assumption, obtaining areas under receiver operating curves using fivefold cross-validation of 0.96 and 0.93, respectively. We used a two-layer structure to select the optimal operating point and assess the resulting classifier to avoid unbiased estimates. The resulting estimates for diagnostic sensitivity/specificity were 71.5%/89.5% for disease classification when RDI >= 15 and 63.3%/100% for RDI >= 5. These results were found assuming that the costs of misclassifying healthy and diseased subjects are equal, but we provide a framework to vary these costs. The results suggest that a classifier based on RQA features derived from NP measurements could be used in an automated SDB screening device

A very fast algorithm for matrix factorization

We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undertake the statistical analysis of interest. Our algorithm uses a gradient-based approach which can be used with an arbitrary loss function provided the latter is differentiable. The speed and effectiveness of our algorithm for dimension reduction is demonstrated in the context of supervised classification of some real high-dimensional data sets from the bioinformatics literature.Matrix factorization Nonnegative matrix factorization High-dimensional data Microarray gene-expression data Supervised classification