arXiv subjects

Zeny Feng

Publications and source records attributed to Zeny Feng.

4 recordsLinked to original sources

arXiv2025

DESA: An R Package for Detecting Epidemics using a School-Absenteeism Surveillance Framework

Absenteeism of elementary school children has been shown to be effective in the early detection of an incoming influenza epidemic within a given population. This paper introduces DESA, an R package designed to: 1) model an epidemic using school absenteeism data, 2) raise an alert for an incoming epidemic using school absenteeism data, 3) evaluate the timeliness of the raised alert using different metrics, and 4) simulate community-level household populations, epidemics, and school absenteeism to facilitate research in related fields. This paper provides an overview of the functions in the package and demonstrates its complete workflow using simulated data generated within the package. DESA offers researchers and public health officials a tool for improving early detection of seasonal influenza epidemics or epidemics of other diseases. The package is available on CRAN, making it readily accessible to the R user community.

stat.AP↗

arXiv2025

Dominating Hyperplane Regularization for Variable Selection in Multivariate Count Regression

Identifying relevant factors that influence the multinomial counts in compositional data is difficult in high dimensional settings due to the complex associations and overdispersion. Multivariate count models such as the Dirichlet-multinomial (DM), negative multinomial, and generalized DM accommodate overdispersion but are difficult to optimize due to their non-concave likelihood functions. Further, for the class of regression models that associate covariates to the multivariate count outcomes, variable selection becomes necessary as the number of potentially relevant factors becomes large. The sparse group lasso (SGL) is a natural choice for regularizing these models. Motivated by understanding the associations between water quality and benthic macroinvertebrate compositions in Canada's Athabasca oil sands region, we develop dominating hyperplane regularization (DHR), a novel method for optimizing regularized regression models with the SGL penalty. Under the majorization-minimization framework, we show that applying DHR to a SGL penalty gives rise to a surrogate function that can be expressed as a weighted ridge penalty. Consequently, we prove that for multivariate count regression models with the SGL penalty, the optimization leads to an iteratively reweighted Poisson ridge regression. We demonstrate stable optimization and high performance of our algorithm through simulation and real world application to benthic macroinvertebrate compositions.

stat.ME↗

arXiv2021

Pathogen.jl: Infectious Disease Transmission Network Modelling with Julia

We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs can be used to jointly infer transmission networks, event times, and model parameters within a Bayesian framework via Markov chain Monte Carlo (MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and our implementation of these strategies in the Julia package, Pathogen.jl, which leverages key features of the Julia language. We provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, and then perform inference using observations that were generated from that epidemic. We also demonstrate the functionality of Pathogen.jl with an application of TN-ILMs to data from a measles outbreak that occurred in Hagelloch, Germany in 1861(Pfeilsticker 1863; Oesterle 1992).

stat.AP↗

arXiv2011

Generalized genetic association study with samples of related individuals

Genetic association study is an essential step to discover genetic factors that are associated with a complex trait of interest. In this paper we present a novel generalized quasi-likelihood score (GQLS) test that is suitable for a study with either a quantitative trait or a binary trait. We use a logistic regression model to link the phenotypic value of the trait to the distribution of allelic frequencies. In our model, the allele frequencies are treated as a response and the trait is treated as a covariate that allows us to leave the distribution of the trait values unspecified. Simulation studies indicate that our method is generally more powerful in comparison with the family-based association test (FBAT) and controls the type I error at the desired levels. We apply our method to analyze data on Holstein cattle for an estimated breeding value phenotype, and to analyze data from the Collaborative Study of the Genetics of Alcoholism for alcohol dependence. The results show a good portion of significant SNPs and regions consistent with previous reports in the literature, and also reveal new significant SNPs and regions that are associated with the complex trait of interest.

stat.AP↗