Anomaly Detection Models for SARS-CoV-2 Surveillance Based on Genome

Open access · goldAbstract read

In one paragraph

Article in Microorganisms, 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

0.9field-weighted citation impact, top 20% of its field

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, 5 citations in OpenAlex.

Enhancing epidemic preparedness: a data-driven system for managing respiratory infections.BMC infectious diseases · 2025
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

3 authors at 2 institutions in 1 country.

Haotian RenBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.ORCID 0000-0002-5261-5651

Yixue LiBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Tao HuangBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.ORCID 0000-0003-1975-9693

Shanghai Institute of Nutrition and Health · CNShanghai Jiao Tong University · CN

Funding

National Key R&D Program of China 2018YFC2000205National Key R&D Program of China 2022YFF1203202Self-supporting Program of Guangzhou Laboratory SRPG22-007Strategic Priority Research Program of Chinese Academy of Sciences XDA26040304Strategic Priority Research Program of Chinese Academy of Sciences XDB38050200

6 · The paper itself

Abstract

Since COVID-19 has brought great challenges to global public health governance, developing methods that track the evolution of the virus over the course of an epidemic or pandemic is useful for public health. This paper uses anomaly detection models to analyze SARS-CoV-2 virus genome

Indexed as

anomaly detectionk-mermachine learningSARS-CoV-2virus surveillance

Identifiers

PMID38004784

DOI10.3390/microorganisms11112773

PMCPMC10673111

OpenAlexW4388704322

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.