Modelling High-Risk Areas for African Horse Sickness Occurrence in Mainland China Along Southeast Asia

Gao, Hongyan; Jia, Bingrui; Wang, Haoran; Zheng, Jiahao; Gao, Xiang; Xiao, Jianhua; Wang, Hongbin

doi:10.22541/au.160570106.63915759/v1

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…In light of these diverse environmental influences (17, 33, 41), our study aims to integrate these varied environmental factors into a cohesive model. By employing a global-scale approach and focusing on variable identification, we endeavor to elucidate the general laws governing the spread of AHS and address the geographical variability in prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

Kim,

Xu,

Villalan

et al. 2024

Preprint

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This study marks a pioneering effort in analyzing the global epidemiological patterns of African Horse Sickness (AHS) across different regions. By employing predictive modeling with a comprehensive set of environmental variables, we uncovered overarching global patterns in AHS dynamics, a first in this field. Our analysis revealed significant regional differences influenced by specific climatic conditions, highlighting the disease’s complexity. The study also identifies new high-risk areas for AHS, underscoring the necessity for regionally tailored disease management strategies. Despite some limitations, such as the exclusion of wild equine data, this research offers critical insights for global AHS intervention and prevention, setting a path for future research incorporating broader datasets and socio-economic factors.Author SummaryAHS presents a significant challenge to the global equine industry, impacting both health and economic aspects. Our study highlights the profound effect of climate change, particularly the frequency of extreme climatic events including temperature and humidity variations, on the transmission dynamics of diseases like AHS. In our research, we focused on overcoming the challenges associated with identifying key environmental factors and determining the appropriate geographic scale for a comprehensive global understanding of AHS. Our aim was to bridge existing knowledge gaps and elucidate the fundamental principles governing AHS transmission. This study establishes a solid foundation for understanding the intricate dynamics of AHS and offers practical pathways for conservation efforts. It emphasizes the urgent need for environmentally conscious strategies to protect horse populations and the industries dependent on them. By highlighting the relationship between environmental factors, vector presence, and AHS transmission, our research underscores the importance of a holistic approach to disease mitigation. In conclusion, the findings of our study not only contribute to the scientific understanding of AHS but also serve as a guide for policymakers and practitioners in developing effective strategies for disease management and prevention, tailored to the specific environmental conditions and challenges faced in different regions around the world.

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Section: Introductionmentioning

confidence: 99%

Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

Kim,

Xu,

Villalan

et al. 2024

Preprint

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Thermal bioclimatic indicators over Southeast Asia: present status and future projection using CMIP6

Hamed

Nashwan

Shahid

et al. 2022

Environ Sci Pollut Res

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Mapping potential changes in bioclimatic characteristics are critical for planning climate change adaptation and mitigation goals. Assessment of such changes is particularly important for Southeast Asia, which has one of the world's highest ecological diversity. Twenty-three CMIP6 GCMs are used in this study to evaluate the change in 11 thermal bioclimatic indicators of Southeast Asia for two shared socioeconomic pathways (SSPs), 2-4.5 and 5-8.5. The spatial changes in the ensemble mean, 5th, and 95th percentile of each indicator for the near (2020-2059) and far (2060-2099) futures for the two SSPs to understand the changes with time and associated uncertainty. The results indicate large spatial heterogeneity and temporal variability in projected changes in bioclimatic indicators. A higher change was projected in the mainland SEA for the far future and less in maritime SEA for the near future. At the same time,uncertainty in the projected bioclimatic indices was higher for mainland SEA than maritime SEA. The multimodel ensemble mean (MME) revealed a change in mean temperature in the range of -0.71 to 3.23 °C for the near and 0.00 to 4.07 °C for the far future. The diurnal temperature range was projected to reduce over most of SEA in the range of -1.1 to -2.0 °C, while isothermality to decrease by -1.1 to -4.6%. The decrease in isothermality along with a decrease in seasonality indicates a possible shift in climate, particularly in the north of mainland-SEA in the future. Maximum temperature in the warmest month/quarter was projected to increase a little more than the coldest month/quarter and the mean temperature in the driest month to increase more than the wettest month. This would cause an increase in the annual temperature range in the future.

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Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

Kim,

Xu,

Kannan Villalan

et al. 2024

Transboundary and Emerging Diseases

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African horse sickness (AHS) is a viral disease transmitted by arthropods that impacts Equidae, specifically horses and related species. Recognized as a notifiable disease by the World Organisation for Animal Health (WOAH), AHS is associated with a high mortality rate of 80%–90% in susceptible hosts and exhibits rapid transmission dynamics. Historical records document numerous instances of mass horse deaths attributed to AHS, with recent occurrences in Thailand and Malaysia in 2020 causing heightened concerns within the local horse industry. The lack of a comprehensive global perspective on the distribution and transmission of AHS poses challenges in comprehending and implementing effective prevention and control strategies. This study marks a pioneering effort in analyzing the global epidemiological patterns of AHS across different regions. By employing predictive modeling with a comprehensive set of environmental variables, we uncovered overarching global patterns in AHS dynamics, a first in this field. Our analysis revealed significant regional differences influenced by specific climatic conditions, highlighting the disease’s complexity. The study also identifies new high‐risk areas for AHS, underscoring the necessity for regionally tailored disease management strategies. Despite some limitations, such as the exclusion of wild equine data, this research offers critical insights for global AHS intervention and prevention, setting a path for future research incorporating broader datasets and socioeconomic factors.

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Modelling High-Risk Areas for African Horse Sickness Occurrence in Mainland China Along Southeast Asia

Cited by 5 publications

References 42 publications

Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

Thermal bioclimatic indicators over Southeast Asia: present status and future projection using CMIP6

Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

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