Evidence map›Paper›PMID 38887536›Full record

ArticleFrontiers in veterinary science2024

Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022).

Jiafeng Ding, Yu Wang, Jinjiao Liang, Zhenhuan He, Changhong Zhai, Yinghao He, Jiayin Xu, Lei Lei, Jing Mu, Min Zheng and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in veterinary science, 2024. 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

12 authors.

Jiafeng DingCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Yu WangCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Jinjiao LiangCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Zhenhuan HeShenyang Zhengda Animal Husbandry Co., Ltd., Shenyang, China.
Changhong ZhaiCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Yinghao HeCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Jiayin XuCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Lei LeiCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Jing MuCollege of Animal Science and Technology, Guangxi University, Nanning, China.
Min ZhengGuangxi Center for Animal Disease Control and Prevention, Nanning, China.
Boyang LiuCollege of Wildlife and Protected Area, Northeast Forestry University, Harbin, China.
Mingxian ShiCollege of Animal Science and Technology, Guangxi University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Equine influenza (EI) is a severe infectious disease that causes huge economic losses to the horse industry. Spatial epidemiology technology can explore the spatiotemporal distribution characteristics and occurrence risks of infectious diseases, it has played an important role in the prevention and control of major infectious diseases in humans and animals. For the first time, this study conducted a systematic analysis of the spatiotemporal distribution of EI using SaTScan software and investigated the important environmental variables and suitable areas for EI occurrence using the Maxent model. A total of 517 occurrences of EI from 2005 to 2022 were evaluated, and 14 significant spatiotemporal clusters were identified. Furthermore, a Maxent model was successfully established with high prediction accuracy (AUC = 0.920 ± 0.008). The results indicated that annual average ultraviolet radiation, horse density, and precipitation of the coldest quarter were the three most important environmental variables affecting EI occurrence. The suitable areas for EI occurrence are widely distributed across all continents, especially in Asia (India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico). In the future, these suitable areas will expand and move eastward. The largest expansion is predicted under SSP126 scenarios, while the opposite trend will be observed under SSP585 scenarios. This study presents the spatial epidemiological characteristics of EI for the first time. The results could provide valuable scientific insights that can effectively inform prevention and control strategies in regions at risk of EI worldwide.

Indexed as

equine influenzaMaxent modelspatial epidemiologyspatiotemporal patternsuitable area

Identifiers

PMID38887536
PMCPMC11182002

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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.