An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study.

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection.

A wealth of gene expression data generated by high-throughput techniques provides exciting opportunities for studying gene-gene interactions systematically. Gene-gene interactions in a biological system are tightly regulated and are often highly dynamic. The interactions can change flexibly under various internal cellular signals or external stimuli. Previous studies have developed statistical methods to examine these dynamic changes in gene-gene interactions. However, due to the massive number of possible gene combinations that need to be considered in a typical genomic dataset, intensive computation is a common challenge for exploring gene-gene interactions. On the other hand, oftentimes only a small proportion of gene combinations exhibit dynamic co-expression changes. To solve this problem, we propose Bayesian variable selection approaches based on spike-and-slab priors. The proposed algorithms reduce the computational intensity by focusing on identifying subsets of promising gene combinations in the search space. We also adopt a Bayesian multiple hypothesis testing procedure to identify strong dynamic gene co-expression changes. Simulation studies are performed to compare the proposed approaches with existing exhaustive search heuristics. We demonstrate the implementation of our proposed approach to study the association between gene co-expression patterns and overall survival using the RNA-sequencing dataset from The Cancer Genome Atlas breast cancer BRCA-US project.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study.

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Covid-19 vaccine effectiveness against general SARS-CoV-2 infection from the omicron variant: A retrospective cohort study.

We aim to estimate the effectiveness of 2-dose and 3-dose mRNA vaccination (BNT162b2 and mRNA-1273) against general Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (asymptomatic or symptomatic) caused by the omicron BA.1 variant. This propensity-score matched retrospective cohort study takes place in a large public university undergoing weekly Coronavirus Disease 2019 (Covid-19) testing in South Carolina, USA. The population consists of 24,145 university students and employees undergoing weekly Covid-19 testing between January 3rd and January 31st, 2022. The analytic sample was constructed via propensity score matching on vaccination status: unvaccinated, completion of 2-dose mRNA series (BNT162b2 or mRNA-1273) within the previous 5 months, and receipt of mRNA booster dose (BNT162b2 or mRNA-1273) within the previous 5 months. The resulting analytic sample consists of 1,944 university students (mean [SD] age, 19.64 [1.42] years, 66.4% female, 81.3% non-Hispanic White) and 658 university employees (mean [SD] age, 43.05 [12.22] years, 64.7% female, 83.3% non-Hispanic White). Booster protection against any SARS-CoV-2 infection was 66.4% among employees (95% CI: 46.1-79.0%; P<.001) and 45.4% among students (95% CI: 30.0-57.4%; P<.001). Compared to the 2-dose mRNA series, estimated increase in protection from the booster dose was 40.8% among employees (P=.024) and 37.7% among students (P=.001). We did not have enough evidence to conclude a statistically significant protective effect of the 2-dose mRNA vaccination series, nor did we have enough evidence to conclude that protection waned in the 5-month period after receipt of the 2nd or 3rd mRNA dose. Furthermore, we did not find evidence that protection varied by manufacturer. We conclude that in adults 18-65 years of age, Covid-19 mRNA booster doses offer moderate protection against general SARS-CoV-2 infection caused by the omicron variant and provide a substantial increase in protection relative to the 2-dose mRNA vaccination series.

Flexible copula model for integrating correlated multi-omics data from single-cell experiments.

With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation.

Effectiveness and protection duration of Covid-19 vaccines and previous infection against any SARS-CoV-2 infection in young adults.

Data on effectiveness and protection duration of Covid-19 vaccines and previous infection against general SARS-CoV-2 infection in general populations are limited. Here we evaluate protection from Covid-19 vaccination (primary series) and previous infection in 21,261 university students undergoing repeated surveillance testing between 8/8/2021-12/04/2021, during which B.1.617 (delta) was the dominant SARS-CoV-2 variant. Estimated mRNA-1273, BNT162b2, and AD26.COV2.S effectiveness against any SARS-CoV-2 infection is 75.4% (95% CI: 70.5-79.5), 65.7% (95% CI: 61.1-69.8), and 42.8% (95% CI: 26.1-55.8), respectively. Among previously infected individuals, protection is 72.9% when unvaccinated (95% CI: 66.1-78.4) and increased by 22.1% with full vaccination (95% CI: 15.8-28.7). Statistically significant decline in protection is observed for mRNA-1273 (P < .001), BNT162b2 (P < .001), but not Ad26.CoV2.S (P = 0.40) or previous infection (P = 0.12). mRNA vaccine protection dropped 29.7% (95% CI: 17.9-41.6) six months post- vaccination, from 83.2% to 53.5%. We conclude that the 2-dose mRNA vaccine series initially offers strong protection against general SARS-CoV-2 infection caused by the delta variant in young adults, but protection substantially decreases over time. These findings indicate that vaccinated individuals may still contribute to community spread. While previous SARS-CoV-2 infection consistently provides moderately strong protection against repeat infection from delta, vaccination yields a substantial increase in protection.

Flexible bivariate correlated count data regression.

Multivariate count data are common in many disciplines. The variables in such data often exhibit complex positive or negative dependency structures. We propose three Bayesian approaches to modeling bivariate count data by simultaneously considering covariate-dependent means and correlation. A direct approach utilizes a bivariate negative binomial probability mass function developed in Famoye (2010, Journal of Applied Statistics). The second approach fits bivariate count data indirectly using a bivariate Poisson-gamma mixture model. The third approach is a bivariate Gaussian copula model. Based on the results from simulation analyses, the indirect and copula approaches perform better overall than the direct approach in terms of model fitting and identifying covariate-dependent association. The proposed approaches are applied to two RNA-sequencing data sets for studying breast cancer and melanoma (BRCA-US and SKCM-US), respectively, obtained through the International Cancer Genome Consortium.