Evidence map›Paper›PMID 33469604›Full record

ArticlemedRxiv : the preprint server for health sciences2021

BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies.

Shuang Jiang, Quan Zhou, Xiaowei Zhan, Qiwei Li

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

  • Updated by
5 · Who and what money

Authors and funding

4 authors.

Shuang JiangDepartment of Statistical Science, Southern Methodist University, Dallas, TX 75205, USA.
Quan ZhouDepartment of Statistics, Texas A&M University, College Station, TX 77843, USA.
Xiaowei ZhanQuantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Qiwei LiDepartment of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA.

Funding

UT Southwestern Medical Center Simmons Comprehensive Cancer CenterP30CA142543 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Sandi Leigh Pruitt · 2010 to 2026
$53.7M
Method to Enhance the Rare Variant Genetic Architecture Analysis in Meta-AnalysesR01HG008983 · NHGRI · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI LIU, DAJIANG · 2016 to 2019
$1.6M
Tools for integrative genomics and disease association study for the X chromosomeR01GM126479 · NIGMS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI LIU, DAJIANG · 2018 to 2021
$1.2M
NCI NIH HHS P30 CA142543NHGRI NIH HHS R01 HG008983NIGMS NIH HHS R01 GM126479
6 · The paper itself

Abstract

The coronavirus disease of 2019 (COVID-19) is a pandemic. To characterize its disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model builds on a Bayesian Poisson segmented regression model that can 1) capture the epidemiological dynamics under the changing conditions caused by external or internal factors; 2) provide uncertainty estimates of both the number and locations of change points; and 3) adjust any explanatory time-varying covariates. Our model can be used to evaluate public health interventions, identify latent events associated with spreading rates, and yield better short-term forecasts.

Indexed as

Bayesian hierarchical modelingMultiple change-point detectionPoisson segmented regressionStochastic SIR model

Identifiers

PMID33469604
PMCPMC7814850

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.