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Frontiers in veterinary science2024; 11; 1395327; doi: 10.3389/fvets.2024.1395327

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

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.
Copyright © 2024 Ding, Wang, Liang, He, Zhai, He, Xu, Lei, Mu, Zheng, Liu and Shi.
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Publication Date: 2024-06-03 PubMed ID: 38887536PubMed Central: PMC11182002DOI: 10.3389/fvets.2024.1395327Google Scholar: Lookup
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Summary

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Overview

  • This study analyzes the global spatiotemporal patterns of equine influenza (EI) outbreaks from 2005 to 2022 and predicts environmentally suitable areas for EI occurrence using advanced spatial analysis and ecological niche modeling.
  • The research identifies key environmental factors influencing EI distribution and forecasts how climate change scenarios may alter the risk regions in the future.

Introduction to Equine Influenza and Study Purpose

  • Equine influenza (EI) is a highly contagious viral disease affecting horses, leading to significant economic losses in the equine industry worldwide.
  • Understanding the spatiotemporal distribution of EI can help in formulating effective prevention and control measures.
  • Spatial epidemiology tools allow researchers to map disease patterns over time and space and to identify environmental factors linked to disease occurrence.
  • This study represents the first systematic global analysis of the spatiotemporal dynamics and environmental suitability of EI.

Data and Methods

  • Data: 517 recorded EI outbreak occurrences from 2005 to 2022 were compiled globally.
  • Spatiotemporal Clustering: SaTScan software was used to detect and analyze statistically significant clusters of EI outbreaks in both space and time.
  • Environmental Suitability Modeling: The Maxent model, a machine-learning based species distribution model, was employed to identify environmental variables associated with EI presence and to predict suitable areas for EI occurrence.
  • Model Performance: The Maxent model achieved high predictive accuracy, evidenced by an Area Under the Curve (AUC) score of 0.920 ± 0.008, indicating reliable identification of risk areas.

Key Findings

  • Spatiotemporal Clusters: 14 significant clusters of EI outbreaks were identified, indicating non-random patterns in the occurrence of the disease worldwide.
  • Important Environmental Variables:
    • Annual average ultraviolet (UV) radiation
    • Horse population density
    • Precipitation during the coldest quarter of the year
  • Predictive Distribution:
    • Suitable EI occurrence zones are broadly spread across all continents with hotspots notably in:
      • Asia: India, Mongolia, and China
      • Americas: Brazil, Uruguay, USA, and Mexico
    • Model projections suggest future expansion and eastward movement of these suitable areas.

Future Predictions under Climate Scenarios

  • Climate Scenario SSP126 (low greenhouse gas emissions):
    • Predicted largest expansion of suitable EI areas indicating increased risk zones.
  • Climate Scenario SSP585 (high greenhouse gas emissions):
    • Opposite trend with contraction or shift in suitable EI areas observed.
  • The differences in future risk patterns highlight the influence of climate change pathways on EI distribution.

Implications and Applications

  • This research provides the first comprehensive spatial epidemiological characterization of EI globally.
  • The findings offer critical insights into environmental drivers and spatial-temporal dynamics which can support:
    • Targeted disease surveillance
    • Improved risk assessment
    • Formulation of proactive prevention and control strategies in high-risk and emerging areas worldwide.
  • Policies can be better informed by these predictive models to mitigate economic losses and enhance animal health security.

Cite This Article

APA
Ding J, Wang Y, Liang J, He Z, Zhai C, He Y, Xu J, Lei L, Mu J, Zheng M, Liu B, Shi M. (2024). Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022). Front Vet Sci, 11, 1395327. https://doi.org/10.3389/fvets.2024.1395327

Publication

Frontiers in veterinary science
ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 11
Pages: 1395327
PII: 1395327

Researcher Affiliations

Ding, Jiafeng
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
  • Nanning New Technology Entrepreneur Center, Nanning, China.
Wang, Yu
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
Liang, Jinjiao
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
He, Zhenhuan
  • Shenyang Zhengda Animal Husbandry Co., Ltd., Shenyang, China.
Zhai, Changhong
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
He, Yinghao
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
Xu, Jiayin
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
Lei, Lei
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
Mu, Jing
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.
Zheng, Min
  • Guangxi Center for Animal Disease Control and Prevention, Nanning, China.
Liu, Boyang
  • College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China.
Shi, Mingxian
  • College of Animal Science and Technology, Guangxi University, Nanning, China.
  • Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, Guangxi University, Nanning, China.

Conflict of Interest Statement

JD was employed by Nanning New Technology Entrepreneur Center. ZH was employed by Shenyang Zhengda Animal Husbandry Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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