Posterior Estimates of Dynamic Constants in HIV Transmission Modeling.

Yingqing Chen, Renee Dale, Hongyu He, Quoc-Anh T Le

Abstract read

In one paragraph

Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Estimating epidemiological parameters of a stochastic differential model of HIV dynamics using hierarchical Bayesian statistics.PloS one · 2018
Article

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yingqing ChenFred Hutchinson Cancer Research Center, Seattle, WA, USA.

Renee DaleDepartment of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.

Hongyu HeDepartment of Mathematics, Louisiana State University, Baton Rouge, LA 70803, USA.ORCID 0000-0002-4034-3812

Quoc-Anh T LeDepartment of Mathematics, Louisiana State University, Baton Rouge, LA 70803, USA.

Funding

Statistical Methods for Adherence Issues in HIV Prevention ResearchR01AI121259 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2015 to 2018

$2.5M

NIAID NIH HHS R01 AI121259

6 · The paper itself

Abstract

In this paper, we construct a linear differential system in both continuous time and discrete time to model HIV transmission on the population level. The main question is the determination of parameters based on the posterior information obtained from statistical analysis of the HIV population. We call these parameters dynamic constants in the sense that these constants determine the behavior of the system in various models. There is a long history of using linear or nonlinear dynamic systems to study the HIV population dynamics or other infectious diseases. Nevertheless, the question of determining the dynamic constants in the system has not received much attention. In this paper, we take some initial steps to bridge such a gap. We study the dynamic constants that appear in the linear differential system model in both continuous and discrete time. Our computations are mostly carried out in Matlab.

Indexed as

AlgorithmsComputer SimulationGlobal HealthHIV InfectionsHumansLeast-Squares AnalysisLinear ModelsNonlinear DynamicsPopulation DynamicsRiskSoftwareUnited States

Identifiers

PMID29250132

DOI10.1155/2017/1093045

PMCPMC5698610

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.