Evidence map›Paper›PMID 39155082›Full record

ArticleThe Journal of veterinary medical science2024

Association between highly pathogenic avian influenza outbreaks and weather conditions in Japan.

Yuri Fujimoto, Takeshi Haga

Abstract read
In one paragraph

Article in The Journal of veterinary medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

2 authors.

Yuri FujimotoLaboratory of OSG Veterinary Science for Global Disease Management, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
Takeshi HagaLaboratory of OSG Veterinary Science for Global Disease Management, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Highly pathogenic avian influenza (HPAI) poses a significant threat to animal and public health, with outbreaks occurring globally. HPAI poses significant challenges due to its high mortality rate and public health concerns, with outbreaks spreading globally since the emergence of the H5N1 virus in 2003. In Japan, HPAI outbreaks have been particularly prevalent during autumn and winter seasons, with the 2022-2023 winter experiencing the most severe outbreak to date. However, limited research has directly examined the association between HPAI outbreaks and weather conditions in Japan. Here we show that specific weather conditions are associated with an increased risk of HPAI outbreaks on poultry farms in Japan. By analyzing databases of HPAI cases and meteorological data from 2020-2023, we found that higher average air temperatures two to three weeks prior, lower average wind speeds four weeks prior, and longer sunlight hours two and four weeks prior to outbreaks were significantly associated with increased risk of HPAI outbreaks in Japan. These results suggest that weather may influence environmental survival and transmission of the virus, as well as patterns of wild bird movement that could seed new outbreaks. These findings enhance our understanding of the factors influencing HPAI transmission dynamics and highlight the importance of integrating weather forecasts into disease surveillance and prevention strategies.

Indexed as

Disease OutbreaksInfluenza in BirdsWeatherAnimalsInfluenza A Virus, H5N1 SubtypeJapanPoultryPoultry DiseasesSeasonscase-control studyhighly pathogenic avian influenzaJapanweather condition

Identifiers

PMID39155082
PMCPMC11442392

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.