Bayesian Analysis of Tweedie Compound Poisson Partial Linear Mixed Models with Nonignorable Missing Response and Covariates.

Under the Bayesian framework, this study proposes a Tweedie compound Poisson partial linear mixed model on the basis of Bayesian P-spline approximation to nonparametric function for longitudinal semicontinuous data in the presence of nonignorable missing covariates and responses. The logistic regression model is simultaneously used to specify the missing response and covariate mechanisms. A hybrid algorithm combining the Gibbs sampler and the Metropolis-Hastings algorithm is employed to produce the joint Bayesian estimates of unknown parameters and random effects as well as nonparametric function. Several simulation studies and a real example relating to the osteoarthritis initiative data are presented to illustrate the proposed methodologies.

High Diversity and Prevalence of Borrelia burgdorferi sensu lato in Wildlife Hosts, Domestic Animals, and Ticks in Yunnan Province, Southwestern China.

Borrelia burgdorferi sensu lato (BBSL), the causative agent of Lyme disease, is commonly found in wild and domestic mammals and ticks worldwide. In China, human cases of Borrelia burgdorferi infections have been identified across a wide geographic range including Yunnan Province, but few studies have examined BBSL in reservoirs and vectors in southwestern China. Here we conducted a thorough and broad-range investigation of BBSL in small mammals, domestic mammals, and ticks collected from 159 sample sites across 42 counties in Yunnan Province. DNA was extracted from spleen tissue of small mammals, blood from domestic mammals, and homogenized ticks. Nested PCR targeting the 5S-23S rRNA intergenic spacer gene of BBSL was used for screening, with amplicons sequenced directly and analyzed using a BLAST algorithm. A total of 8,478 samples were collected, which were composed of 5,044 mammals belonging to 68 species, 1,927 livestock belonging to five species, and 1, 507 ticks belonging to 14 species. BBSL was detected in 147 mammals (2.9%) from 30 different species, 20 of which represent the first reported detection in that species. A total of 52 (2.7%) livestock samples were positive for BBSL, with dogs having the highest detection rate (6.3%, 43/687), and 103 ticks (6.8%) tested positive with high prevalence in Ixodes granulatus (44.2%, 23/52), Haemaphysalis nepalensi (33.3%, 3/9) and Haemaphysalis kolonini (19.0%, 31/163). Sequence analysis revealed six genospecies of BBSL including B. afzelii, B. burgdorferi sensu stricto, B. japonica, B. garinii, B. sinica, and B. valaisiana. Significant differences in prevalence rates of BBSL were observed by species, landscape types, altitude, and season. Our findings indicate a wide distribution of multiple endemic BBSL genospecies based on a large-scale survey within Yunnan, which underline the need to expand surveillance efforts for human in southwestern China.

Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data.

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis-Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.

ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data.

The World Health Organization (WHO) upgraded the status of the coronavirus disease 2019 (COVID-19) outbreak from epidemic to global pandemic on March 11, 2020. Various mathematical and statistical models have been proposed to predict the spread of COVID-2019 [1]. We collated data on daily new confirmed cases of the COVID-19 outbreaks in Japan and South Korea from January 20, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARIMA) model were introduced to analyze two data sets and predict the daily new confirmed cases for the 7-day period from April 27, 2020 to May 3, 2020. Also, the forecasting results and both data sets are provided.

Investigation on Ehrlichia Infection in Small Mammals and Ticks from Tengchong, Yunnan Province, Southern China.

BACKGROUND: Rare investigation on tick-borne pathogens was carried out in Yunnan, China. In this study, we did a survey on Ehrlichia infection in small mammals and ticks. A total of 40 small mammals and 49 ticks were collected from Tengchong, Yunnan province. PCR targeting 16S ribosomal RNA (rRNA), citrate synthase, GroEL heat-shock protein operon, and major outer membrane protein genes was performed and positive amplicons were sequenced. RESULTS: The 40 small mammals were identified as 10 species, 2 (5.0%) of which were infected with Ehrlichia, 4 (10.0%) were infected with Anaplasma phagocytophilum and another 2 (5.0%) were infected with Candidatus Neoehrlichia mikurensis. Six (12.2%) ticks were positive for Ehrlichia and another two (4.1%) were infected with A. phagocytophilum. Neither small mammals nor ticks had coinfection. The detected Ehrlichia was named as Ehrlichia sp. YN04, which was in the same clade of Ehrlichia sp. 360 by phylogenetic analysis. The sequences of the pathogen recovered from small mammals and ticks were identical with each other. CONCLUSION: The study reports one Ehrlichia species first detected from small mammals and ticks in mainland China. As Yunnan is a famous "Global Biodiversity Hotspot" in the world, we may expect much more tick-borne infectious pathogens existing and declare more public health attention in this region.