Estimation of COVID-19 spread curves integrating global data and borrowing information

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19

Investigating international new product diffusion speed: A semiparametric approach

Global marketing managers are interested in understanding the speed of the new product diffusion process and how the speed has changed in our ever more technologically advanced and global marketplace. Understanding the process allows firms to forecast the expected rate of return on their new products and develop effective marketing strategies. The most recent major study on this topic [Marketing Science 21 (2002) 97--114] investigated new product diffusions in the United States. We expand upon that study in three important ways. (1) Van den Bulte notes that a similar study is needed in the international context, especially in developing countries. Our study covers four new product diffusions across 31 developed and developing nations from 1980--2004. Our sample accounts for about 80% of the global economic output and 60% of the global population, allowing us to examine more general phenomena. (2) His model contains the implicit assumption that the diffusion speed parameter is constant throughout the diffusion life cycle of a product. Recognizing the likely effects on the speed parameter of recent changes in the marketplace, we model the parameter as a semiparametric function, allowing it the flexibility to change over time. (3) We perform a variable selection to determine that the number of internet users and the consumer price index are strongly associated with the speed of diffusion.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS519 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian Semiparametric Multivariate Density Deconvolution

We consider the problem of multivariate density deconvolution when the interest lies in estimating the distribution of a vector-valued random variable but precise measurements of the variable of interest are not available, observations being contaminated with additive measurement errors. The existing sparse literature on the problem assumes the density of the measurement errors to be completely known. We propose robust Bayesian semiparametric multivariate deconvolution approaches when the measurement error density is not known but replicated proxies are available for each unobserved value of the random vector. Additionally, we allow the variability of the measurement errors to depend on the associated unobserved value of the vector of interest through unknown relationships which also automatically includes the case of multivariate multiplicative measurement errors. Basic properties of finite mixture models, multivariate normal kernels and exchangeable priors are exploited in many novel ways to meet the modeling and computational challenges. Theoretical results that show the flexibility of the proposed methods are provided. We illustrate the efficiency of the proposed methods in recovering the true density of interest through simulation experiments. The methodology is applied to estimate the joint consumption pattern of different dietary components from contaminated 24 hour recalls