arXiv subjects

Le Bao

Publications and source records attributed to Le Bao.

26 records · Page 2Linked to original sources

arXiv2020

A Joint Spatial Conditional Auto-Regressive Model for Estimating HIV Prevalence Rates Among Key Populations

Ending the HIV/AIDS pandemic is among the Sustainable Development Goals for the next decade. In order to overcome the gap between the need for care and the available resources, better understanding of HIV epidemics is needed to guide policy decisions, especially for key populations that are at higher risk for HIV infection. Accurate HIV epidemic estimates for key populations have been difficult to obtain because their HIV surveillance data is very limited. In this paper, we propose a so-called joint spatial conditional auto-regressive model for estimating HIV prevalence rates among key populations. Our model borrows information from both neighboring locations and dependent populations. As illustrated in the real data analysis, it provides more accurate estimates than independently fitting the sub-epidemic for each key population. In addition, we provide a study to reveal the conditions that our proposal gives a better prediction. The study combines both theoretical investigation and numerical study, revealing strength and limitations of our proposal.

stat.AP↗

arXiv2020

What Can We Learn from the Travelers Data in Detecting Disease Outbreaks -- A Case Study of the COVID-19 Epidemic

Background: Travel is a potent force in the emergence of disease. We discussed how the traveler case reports could aid in a timely detection of a disease outbreak. Methods: Using the traveler data, we estimated a few indicators of the epidemic that affected decision making and policy, including the exponential growth rate, the doubling time, and the probability of severe cases exceeding the hospital capacity, in the initial phase of the COVID-19 epidemic in multiple countries. We imputed the arrival dates when they were missing. We compared the estimates from the traveler data to the ones from domestic data. We quantitatively evaluated the influence of each case report and knowing the arrival date on the estimation. Findings: We estimated the travel origin's daily exponential growth rate and examined the date from which the growth rate was consistently above 0.1 (equivalent to doubling time < 7 days). We found those dates were very close to the dates that critical decisions were made such as city lock-downs and national emergency announcement. Using only the traveler data, if the assumed epidemic start date was relatively accurate and the traveler sample was representative of the general population, the growth rate estimated from the traveler data was consistent with the domestic data. We also discussed situations that the traveler data could lead to biased estimates. From the data influence study, we found more recent travel cases had a larger influence on each day's estimate, and the influence of each case report got smaller as more cases became available. We provided the minimum number of exported cases needed to determine whether the local epidemic growth rate was above a certain level, and developed a user-friendly Shiny App to accommodate various scenarios.

stat.AP↗

arXiv2017

Assignment of endogenous retrovirus integration sites using a mixture model

Structural variation occurs in the genomes of individuals because of the different positions occupied by repetitive genome elements like endogenous retroviruses, or ERVs. The presence or absence of ERVs can be determined by identifying the junction with the host genome using high-throughput sequence technology and a clustering algorithm. The resulting data give the number of sequence reads assigned to each ERV-host junction sequence for each sampled individual. Variability in the number of reads from an individual integration site makes it difficult to determine whether a site is present for low read counts. We present a novel two-component mixture of negative binomial distributions to model these counts and assign a probability that a given ERV is present in a given individual. We explain how our approach is superior to existing alternatives, including another form of two-component mixture model and the much more common approach of selecting a threshold count for declaring the presence of an ERV. We apply our method to a data set of ERV integrations in mule deer [Odocoileus hemionus], a species for which no genomic resources are available, and demonstrate that the discovered patterns of shared integration sites contain information about animal relatedness.

stat.AP↗

arXiv2016

Incorporating Hierarchical Structure Into Dynamic Systems: An Application Of Estimating HIV Epidemics At Sub-National And Sub-Population Level

Dynamic models have been successfully used in producing estimates of HIV epidemics at national level, due to their epidemiological nature and their ability to simultaneously estimate prevalence, incidence, and mortality rates. Recently, HIV interventions and policies have required more information at sub-national and sub-population levels to support local planning, decision making and resource allocation. Unfortunately, many areas and high-risk groups lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas and groups within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time consuming. In this paper, we propose a simple and innovative way to incorporate the hierarchical information into the dynamic systems by using auxiliary data. The proposed method efficiently uses information from multiple areas and risk groups within each country without increasing the computational burden. As a result, the new model improves predictive ability in general with especially significant improvement in areas and risk groups with sparse data.

stat.ME↗

arXiv2016

Clustering pipeline for determining consensus sequences in targeted next-generation sequencing

Analyses of targeted genomic sequencing data from next-generation-sequencing (NGS) technologies typically involves mapping reads to a reference sequence or clustering reads. For a number of species a reference genome is not available so the analyses of targeted sequencing data, for example polymorphic structural variation caused by mobile elements is difficult; clustering methods are preferred for such data analysis. Clustering of reads requires a clustering threshold parameter, which is used to compare and group reads. However, determining the optimal clustering threshold for a read dataset is challenging because of different sequence composition, the number of sequences present, and also the amount of sequencing errors in the dataset. High values of the clustering threshold parameter can falsely inflate the number of recovered genomic regions, while low values of clustering threshold can merge reads from distinct regions into a single cluster. Thus, an algorithm that can empirically determine clustering threshold is needed. We propose a pipeline for clustering genomic sequences wherein the clustering threshold is empirically determined from the NGS data. The optimal threshold is decided based on two internal clustering measures which assess clusters for small intra-cluster diameters and large inter-cluster distances. We evaluate the pipeline on two simulated datasets derived from human genome sequence simulating different genomic regions and sequencing depth. The total number of clusters obtained from our pipeline is closer to the actual number of reference sequences when compared to single round of clustering. Also, the number of clusters whose consensus sequence matches a corresponding reference sequence is higher in our pipeline. We observe that the presence of repeat regions affects clustering accuracy.

q-bio.GN↗

arXiv2015

Estimating HIV Epidemics for Sub-National Areas

As the global HIV pandemic enters its fourth decade, increasing numbers of surveillance sites have been established which allows countries to look into the epidemics at a finer scale, e.g. at sub-national levels. Currently, the epidemic models have been applied independently to the sub-national areas within countries. However, the availability and quality of the data vary widely, which leads to biased and unreliable estimates for areas with very few data. We propose to overcome this issue by introducing the dependence of the parameters across areas in a mixture model. The joint distribution of the parameters in multiple areas can be approximated directly from the results of independent fits without needing to refit the data or unpack the software. As a result, the mixture model has better predictive ability than the independent model as shown in examples of multiple countries in Sub-Saharan Africa.

stat.AP↗

arXiv2014

A Hierarchical Model for Estimating HIV Epidemics

As the global HIV pandemic enters its fourth decade, increasing numbers of surveillance sites have been established which allows countries to look into the epidemics at a finer scale, e.g. at sub-national level. However, the epidemic models have been applied independently to the sub-national areas within countries. An important technical barrier is that the availability and quality of the data vary widely from area to area, and many areas lack data for deriving stable and reliable results. To improve the accuracy of the results in areas with little data, we propose a hierarchical model that utilizes information efficiently by assuming similar characteristics of the epidemics across areas within one country. The joint distribution of the parameters in the hierarchical model can be approximated directly from the results of independent fits without needing to the refit the data. As a result, the hierarchical model has better predictive ability than the independent model as shown in examples of multiple countries in Sub-Saharan Africa.

stat.AP↗

arXiv2012

Estimation and Clustering with Infinite Rankings

This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its properties and give procedures to estimate it from data. For estimation of multimodal distributions we introduce the Exponential-Blurring-Mean-Shift nonparametric clustering algorithm. The experiments highlight the properties of the new model and demonstrate that infinite models can be simple, elegant and practical.

cs.LG↗