Evidence map›Paper›PMID 37529571›Full record

ArticleJournal of applied statistics2023

Identification of factors impacting on the transmission and mortality of COVID-19.

Peiyi Zhang, Tianning Dong, Ninghui Li, Faming Liang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied statistics, 2023. 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

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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Peiyi ZhangDepartment of Statistics, Purdue University, West Lafayette, IN, USA.ORCID https://orcid.org/0000-0003-4010-2944
Tianning DongDepartment of Statistics, Purdue University, West Lafayette, IN, USA.
Ninghui LiDepartment of Computer Science, Purdue University, West Lafayette, IN, USA.
Faming LiangDepartment of Statistics, Purdue University, West Lafayette, IN, USA.

Funding

An Imputation-Consistency Algorithm for Biomedical Complex Data AnalysisR01GM126089 · NIGMS · PURDUE UNIVERSITY · PI LIANG, FAMING · 2018 to 2021
$1.2M
Equivalent Partial Correlation Methods for Integrative Genetic Network Analysis R01GM117597 · NIGMS · UNIVERSITY OF FLORIDA · PI LIANG, FAMING · 2015 to 2017
$1.0M
NIGMS NIH HHS R01 GM117597NIGMS NIH HHS R01 GM126089
6 · The paper itself

Abstract

This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19.

Indexed as

Dynamic infectious disease modelLangevinized ensemble Kalman filterspatio-temporal datastate-space modelsurveillance data

Identifiers

PMID37529571
PMCPMC10388826

What OpenQuestion holds

Textmetadata
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.