Evidence map›Paper›PMID 32511512›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.

Qinxia Wang, Shanghong Xie, Yuanjia Wang, Donglin Zeng

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. 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

5 · Who and what money

Authors and funding

4 authors.

Qinxia WangDepartment of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Shanghong XieDepartment of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Yuanjia WangDepartment of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Donglin ZengDepartment of Biostatistics, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

Statistical Methods for Integrating Mixed-type Biomarkers and Phenotypes in Neurodegenerative Disease ModelingR01NS073671 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WANG, YUANJIA · 2011 to 2025
$3.9M
Statistical and Machine Learning Methods to Improve Dynamic Treatment Regimens Estimation Using Real World Data.R01GM124104 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yuanjia Wang, Donglin Zeng · 2018 to 2026
$3.1M
Integrative Learning to Combine Evidence for Personalized Treatment Strategies.R21MH117458 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WANG, YUANJIA · 2018 to 2019
$446k
NIGMS NIH HHS R01 GM124104NIMH NIH HHS R21 MH117458NINDS NIH HHS R01 NS073671
6 · The paper itself

Abstract

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making. We propose a robust and parsimonious survival-convolution model for predicting key statistics of COVID-19 epidemics (daily new cases). We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (

Indexed as

COVID-19mitigation measurespredictionsurvival-convolution modeltime-varying effective reproduction number

Identifiers

PMID32511512
PMCPMC7273280

What OpenQuestion holds

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LicenceCC BY-ND
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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.