Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data

The COVID-19 pandemic has highlighted the necessity of advanced modeling inference using the limited data of daily cases. Tracking a long-term epidemic trajectory requires explanatory modeling with more complexities than the one with short-time forecasts, especially for the highly vaccinated scenario in the latest phase. With this work, we propose a novel modeling framework that combines an epidemiological model with Bayesian inference to perform an explanatory analysis on the spreading of COVID-19 in Israel. The Bayesian inference is implemented on a modified SEIR compartmental model supplemented by real-time vaccination data and piecewise transmission and infectious rates determined by change points. We illustrate the fitted multi-wave trajectory in Israel with the checkpoints of major changes in publicly announced interventions or critical social events. The result of our modeling framework partly reflects the impact of different stages of mitigation strategies as well as the vaccination effectiveness, and provides forecasts of near future scenarios

An integrated epidemic modelling framework for the real-time forecast of COVID-19 outbreaks in current epicentres

Various studies have provided a wide variety of mathematical and statistical models for early epidemic prediction of the COVID-19 outbreaks in Mainland China and other epicentres worldwide. In this paper, we present an integrated modelling framework, which incorporates typical exponential growth models, dynamic systems of compartmental models and statistical approaches, to depict the trends of COVID-19 spreading in 33 most heavily suffering countries. The dynamic system of SIR-X plays the main role for estimation and prediction of the epidemic trajectories showing the effectiveness of containment measures, while the other modelling approaches help determine the infectious period and the basic reproduction number. The modelling framework has reproduced the subexponential scaling law in the growth of confirmed cases and adequate fitting of empirical time-series data has facilitated the efficient forecast of the peak in the case counts of asymptomatic or unidentified infected individuals, the plateau that indicates the saturation at the end of the epidemic growth, as well as the number of daily positive cases for an extended period

Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data

The COVID-19 pandemic has highlighted the necessity of advanced modeling inference using the limited data of daily cases. Tracking a long-term epidemic trajectory requires explanatory modeling with more complexities than the one with short-time forecasts, especially for the highly vaccinated scenario in the latest phase. With this work, we propose a novel modeling framework that combines an epidemiological model with Bayesian inference to perform an explanatory analysis on the spreading of COVID-19 in Israel. The Bayesian inference is implemented on a modified SEIR compartmental model supplemented by real-time vaccination data and piecewise transmission and infectious rates determined by change points. We illustrate the fitted multi-wave trajectory in Israel with the checkpoints of major changes in publicly announced interventions or critical social events. The result of our modeling framework partly reflects the impact of different stages of mitigation strategies as well as the vaccination effectiveness, and provides forecasts of near future scenarios

Reference analysis for Birnbaum-Saunders distribution

In this paper we consider the Bayesian estimators for the unknown parameters of the Birnbaum-Saunders distribution under the reference prior. The Bayesian estimators cannot be obtained in closed forms. An approximate Bayesian approach is proposed using the idea of Lindley and Gibbs sampling procedure is also used to obtain the Bayesian estimators. These results are compared using Monte Carlo simulations with the maximum likelihood method and another approximate Bayesian approach Laplace's approximation. Two real data sets are analyzed for illustrative purposes.

Statistical inference for zero-and-one-inflated poisson models

In this paper, a zero-and-one-inflated Poisson (ZOIP) model is studied. The maximum likelihood estimation and the Bayesian estimation of the model parameters are obtained based on data augmentation method. A simulation study based on proposed sampling algorithm is conducted to assess the performance of the proposed estimation for various sample sizes. Finally, two real data-sets are analysed to illustrate the practicability of the proposed method

Constructing Narrowband Thermally Activated Delayed Fluorescence Materials with Emission Maxima Beyond 560 nm Based on Frontier Molecular Orbital Engineering

The development of purely organic materials with narrowband emission in long wavelength region beyond 560 nm still remains a great challenge. Herein, we present a modification approach of multiple resonance (MR) skeleton with electron donor based on frontier molecular orbital engineering (FMOE), resulting in significant red-shift emission of target molecules. Subsequently, the parent MR skeleton is functionalized by boron esterification reaction and changed into a universal building block, namely, the key intermediate BN-Bpin, for molecular structure optimizations. BN-Bpin has been employed to construct a series of highly efficient thermally activated delayed fluorescence (TADF) materials with high color purity through one-step Suzuki coupling reaction. The target molecule perfectly integrates the inherent advantages of MR skeleton and spatial separation typical donor–acceptor (D–A) structure. The results demonstrate that the ingenious modulation of the acceptor is an effective approach to achieve bathochromic emission and narrowband emission simultaneously.</p

Constructing Narrowband Heavy Metal Platinum (II) Complex by Integrating Multiple Resonance Molecular System

The narrowband emission required by wide color gamut display is an extremely important research topic for any luminescence mechanism, which has made significant progress in traditional fluorescence and thermally activated delayed fluorescence (TADF) based on purely organic compounds, but is far from mature in phosphorescence based on metal organic complexes. Herein, we propose a feasible molecular design paradigm for constructing the desirable narrowband-emission organic electroluminescence (EL) emitter by integrating an original multi-resonance thermally activated delayed fluorescent (MR-TADF) fragment into the classical heavy metal platinum (II) complex. The target model platinum (II) complex BNCPPt shows green emission with a single peak at 497 nm and the quite narrow full-width at half-maximum (FWHM) of 27 nm in toluene