Evidence map›Paper›PMID 38076564›Full record

ArticleFrontiers in veterinary science2023

Estimating the time of infection for African swine fever in pig farms in Korea.

Hachung Yoon, Youngmin Son, Kyung-Sook Kim, Ilseob Lee, Yeon-Hee Kim, Eunesub Lee

Open access · goldAbstract read
In one paragraph

Article in Frontiers in veterinary science, 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
1.4field-weighted citation impact, top 18% 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, 4 citations in OpenAlex.

  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

6 authors at 1 institution in 1 country.

Hachung YoonVeterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, Republic of Korea.
Youngmin SonVeterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, Republic of Korea.
Kyung-Sook KimVeterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, Republic of Korea.
Ilseob LeeVeterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, Republic of Korea.
Yeon-Hee KimVeterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, Republic of Korea.
Eunesub LeeVeterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, Republic of Korea.
Animal and Plant Quarantine Agency · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

African swine fever (ASF) is a highly contagious and lethal disease with characteristics of hemorrhagic fever. ASF outbreaks in pig farms significantly damage the entire pork industry. Understanding the transmission dynamics of ASF is crucial to effectively respond. Notably, it is important to know when the infection started on the outbreak farm. This study aimed at establishing a procedure for estimating the time of infection on pig farms affected by the ASF outbreak in Korea. The protocol for sampling to detect ASF virus infection, the estimation of the time interval between infection and detection, and the estimation of the infection stage parameters for the simulation model were described. After infection, fattening sheds (9.8 days in median) had the longest detection time compared with pregnant (8.6 days) or farrowing sheds (8.0 days). The intervals were 8.8 days for farrow-to-finisher farms, 7.0 days for farrow-to-weaning farms, and 9.5 days for fattening farms. The findings of this study provide valuable insights into ASF outbreaks in pig farms thus, improving the disease control ability.

Indexed as

African swine feverKoreapig farmsimulationtime of infection

Identifiers

PMID38076564
PMCPMC10701385
OpenAlexW4388931366

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.