Spatial epidemiology of highly pathogenic avian influenza subtype H5N6 in Gyeonggi Province, South Korea, 2016–2017

Lim, Jun‐Sik; Magalhães, Ricardo J. Soares; Chakma, Shovon; You, Dae‐Sung; Lee, Kwang‐Nyeong; Pak, Son‐Il; Kim, Eu-Tteum

doi:10.1111/tbed.14587

Cited by 2 publications

(2 citation statements)

References 55 publications

(62 reference statements)

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“…So far, spatial research aimed at determining the risk factors of highly pathogenic H5N6 virus has been conducted in South Korea [17]. However, the risk factors affecting the occurrence and transmission of HPAI H5N6 in China are still unclear.…”

Section: Discussionmentioning

confidence: 99%

“…The live poultry trade has also played an essential role in spreading and maintaining HPAIV H5N6 in China [16]. Furthermore, poultry density was an important determinant of HPAIV H5N6 infection in Korean farm flocks [17]. The respective roles played by other factors in the presence and spread of HPAIV H5N6 and the HPAI H5N6 outbreak in China are still unclear.…”

Section: Introductionmentioning

confidence: 99%

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Risk Factors Contributing to Highly Pathogenic Avian Influenza H5N6 in China, 2014–2021: Based on a MaxEnt Model

Sun,

Li,

et al. 2023

Transboundary and Emerging Diseases

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Highly pathogenic avian influenza H5N6, an acute infectious poultry disease, has appeared in China since 2014. The serious HPAI H5N6 epidemic has caused substantial economic losses to the poultry industry and seriously threatened public health. This study developed two maximum entropy models based on highly pathogenic avian influenza virus H5N6 surveillance data obtained through China’s national surveillance program and HPAI H5N6 outbreak data in China, identified risk factors for the disease and developed risk prediction maps. Both models had a good predictive performance with area under the receiver operating characteristic curves of 0.931 and 0.892. Our study considered 26 variables from bioclimate, geographical, and socioeconomic factors. After the screening, 14 variables were retained and incorporated into the construction of the two models. HPAIV H5N6 presence was related to population density, road density, poultry market density, mean diurnal range, and chicken density. HPAI H5N6 poultry outbreaks were associated with population density, road density, mean diurnal range, precipitation of the driest month, and chicken density. Overall, Southern China was the high-risk area for HPAIV H5N6 maintenance and H5N6 outbreak. The risk factors and maps associated with the HPAIV H5N6 presence and HPAI H5N6 outbreak identified in our study will help develop appropriate HPAIV H5N6 control measures to reduce the risk of infection and morbidity in poultry and humans.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Factors Contributing to Highly Pathogenic Avian Influenza H5N6 in China, 2014–2021: Based on a MaxEnt Model

Sun,

Li,

et al. 2023

Transboundary and Emerging Diseases

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Mapping African Swine Fever and Highly Pathogenic Avian Influenza Outbreaks along the Demilitarized Zone in the Korean Peninsula

Im,

Curtis,

Song

et al. 2024

Transboundary and Emerging Diseases

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The Korean Demilitarized Zone (DMZ) is one of the world’s most preserved habitats for wild animals and migratory birds. The area also plays a major role in the spread of infectious animal diseases, in particular, African swine fever (ASF) and highly pathogenic avian influenza (HPAI). These outbreaks threaten the livelihood of local livestock farms, not infrequently. In this paper, we explore these relatively under-researched diseases by modeling and mapping ASF and HPAI risks in tandem using MaxEnt, a machine-learning algorithm. The results show robust predictive power with high area under the curve values, of 0.92 and 0.99, respectively. We found that precipitation from spring to early summer and solar radiation in winter were essential in explaining the potential distribution of ASF, but land use contributed little. Thus, understanding only wild boars’ habitat preferences may not be sufficient in preventing ASF epidemics. HPAI risks were shaped by precipitation and mean temperature from winter to spring and land use. Areas with high ASF and HPAI risks were primarily found in forest and agricultural lands, respectively. The DMZ included many high-risk areas, indicating that the DMZ could lead to a broader regional spread of ASF and HPAI in the peninsula. Thus, our results highlight the essential role of cross-border collaboration and the combination of environmental and epidemiological insights in strategies to control ASF and HPAI risks within and surrounding the DMZ.

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Spatial epidemiology of highly pathogenic avian influenza subtype H5N6 in Gyeonggi Province, South Korea, 2016–2017

Cited by 2 publications

References 55 publications

Risk Factors Contributing to Highly Pathogenic Avian Influenza H5N6 in China, 2014–2021: Based on a MaxEnt Model

Risk Factors Contributing to Highly Pathogenic Avian Influenza H5N6 in China, 2014–2021: Based on a MaxEnt Model

Mapping African Swine Fever and Highly Pathogenic Avian Influenza Outbreaks along the Demilitarized Zone in the Korean Peninsula

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