The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing pandemic affecting over 200 countries and regions. Inference about the transmission dynamics of COVID-19 can provide important insights into the speed of disease spread and the effects of mitigation policies. We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model using data of daily confirmed COVID-19 cases. In particular, we consider a probabilistic extension of the classical susceptible-infectious-recovered model, which takes into account undocumented infections and allows the epidemiological parameters to vary over time. We estimate the disease transmission rate via a Gaussian process prior, which captures nonlinear changes over time without the need of specific parametric assumptions. We utilize a parallel-tempering Markov chain Monte Carlo algorithm to efficiently sample from the highly correlated posterior space. Predictions for future observations are done by sampling from their posterior predictive distributions. Performance of the proposed approach is assessed using simulated datasets. Finally, our approach is applied to COVID-19 data from six states of the United States: Washington, New York, California, Florida, Texas, and Illinois. An R package BaySIR is made available at https://github.com/tianjianzhou/BaySIR for the public to conduct independent analysis or reproduce the results in this paper.

Summary A basket trial in oncology encompasses multiple “baskets” that simultaneously assess one treatment in multiple cancer types or subtypes. It is well-recognized that hierarchical modeling methods, which adaptively borrow strength across baskets, can improve over simple pooling and stratification. We propose a novel Bayesian method, RoBoT (Robust Bayesian Hypothesis Testing), for the data analysis and decision-making in phase II basket trials. In contrast to most existing methods that use posterior credible intervals to determine the efficacy of the new treatment, RoBoT builds upon a formal Bayesian hypothesis testing framework that leads to interpretable and robust inference. Specifically, we assume that the baskets belong to several latent subgroups, and within each subgroup, the treatment has similar probabilities of being more efficacious than controls, historical, or concurrent. The number of latent subgroups and subgroup memberships are inferred by the data through a Dirichlet process mixture model. Such model specification helps avoid type I error inflation caused by excessive shrinkage under typical hierarchical models. The operating characteristics of RoBoT are assessed through computer simulations and are compared with existing methods. Finally, we apply RoBoT to data from two recent phase II basket trials of imatinib and vemurafenib, respectively.

Background and ObjectivesDeproteinization is an indispensable process for the elimination of antigenicity in xenograft bones. However, the hydrogen peroxide (H2O2) deproteinized xenograft, which is commonly used to repair bone defect, exhibits limited osteoinduction activity. The present study was designed to develop a new method for deproteinization and compare the osteogenic capacities of new pepsin deproteinized xenograft bones with those of conventional H2O2 deproteinized ones.MethodsBones were deproteinized in H2O2 or pepsin for 8 hours. The morphologies were compared by HE staining. The content of protein and collagen I were measured by the Kjeldahl method and HPLC-MS, respectively. The physical properties were evaluated by SEM and mechanical tests. For in vivo study, X-ray, micro-CT and HE staining were employed to monitor the healing processes of radius defects in rabbit models transplanted with different graft materials.ResultsCompared with H2O2 deproteinized bones, no distinct morphological and physical changes were observed. However, pepsin deproteinized bones showed a lower protein content, and a higher collagen content were preserved. In vivo studies showed that pepsin deproteinized bones exhibited better osteogenic performance than H2O2 deproteinized bones, moreover, the quantity and quality of the newly formed bones were improved as indicated by micro-CT analysis. From the results of histological examination, the newly formed bones in the pepsin group were mature bones.ConclusionsPepsin deproteinized xenograft bones show advantages over conventional H2O2 deproteinized bones with respect to osteogenic capacity; this new method may hold potential clinical value in the development of new biomaterials for bone grafting.

No abstract