Spatial epidemiology of African swine fever: Host, landscape and anthropogenic drivers of disease occurrence in wild boar

Podgórski, Tomasz; Borowik, Tomasz; Łyjak, Magdalena; Woźniakowski, Grzegorz

doi:10.1016/j.prevetmed.2019.104691

Cited by 55 publications

(81 citation statements)

References 38 publications

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“…Moreover, more ASFpositive carcasses were reported in the affected hexagons with low HLI. This is likely because ASF-infected wild boars prefer high-moisture and cool-temperature environments because of the clinical symptoms of high fever and dehydration (Gabriel et al, 2011), leading to an increased abundance of infected wild boars and therefore ASFpositive carcasses, which is consistent with previous studies (Podgórski et al, 2019;Cukor et al, 2020). Furthermore, this observation might be due to the fact that ASFV is more resistant to colder environments, which means that the virus remains active for a longer time, generating more opportunities for transmission (Bellini et al, 2016).…”

Section: Discussionsupporting

confidence: 90%

“…). Thus, the covariates were selected based on these characteristics and previous studies (Morelle et al, 2019; Podgórski et al, 2019; Cukor et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…). Thus, the covariates were selected based on these characteristics and previous studies (Morelle et al, 2019;Podgórski et al, 2019;Cukor et al, 2020). The habitat suitability for wild boars was retrieved from the species distribution model to identify the distribution of wild boars (Eu-Tteum and Son-Il, 2020).…”

Section: Covariatesmentioning

confidence: 99%

“…EVI representing the level of vegetation activity was obtained from the Terra MODIS MOD13Q1 Version 6 product with a geographic resolution of 250 m (Didan, 2015). Human density could be associated with the distribution of wild boars or the report of ASF-infected carcasses (Podgórski et al, 2019). These data were retrieved from the Gridded Population of the World Version 4, Center for International Earth Science Information Network, Columbia University (Center for International Earth Science Information Network -CIESIN -Columbia University, 2018) with a geographic resolution of 30 arcsecond (approximately 1 km).…”

Section: Covariatesmentioning

confidence: 99%

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Modelling the deathbed of ASF-infected wild boars in South Korea using 2019-2020 national surveillance data

Lim¹,

Vergne

Pak³

et al. 2021

Preprint

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In September 2019, African swine fever (ASF) was reported in South Korea for the first time. Since then, more than 651 ASF cases in wild boars and 14 farm outbreaks have been notified in the country. The purpose of this study was to characterize the spatial distribution of ASF-positive wild boar carcasses to identify the risk factors associated with the presence of ASF and number of ASF-positive wild boar carcasses in the affected areas. To achieve this objective, we divided the study into two periods (October 2, 2019, to January 19, 2020, and January 19 to April 28, 2020) and aggregated the number of reported ASF-positive carcasses into a regular grid of hexagons. To account for imperfect detection, we adjusted spatial zero-inflated Poisson regression models to the number of ASF-positive wild boar carcasses per hexagons. During the first study period, only proximity to North Korea was identified as a risk factor for the presence of African swine fever virus (ASFV). In addition, there were more reports in the affected hexagons with a high habitat suitability for wild boar, low heat load index (HLI), and high human density. During the second study period, proximity to an ASF-positive carcass reported during the first period was the only significant risk factor for the presence of ASF-positive carcasses. Additionally, high HLI and low elevation were associated with an increased number of ASF-positive carcasses reported in the affected hexagons. Although the proportion of ASF-affected hexagons increased from 0.06 (95% credible interval [CrI]: 0.05-0.07) to 0.09 (95% CrI: 0.08-0.10), the probability of reporting ASF-affected hexagons increased substantially from 0.49 (95% CrI: 0.41-0.57) to 0.73 (95% CrI: 0.66-0.81) between the two study periods. These results can be used to further advance risk-based surveillance.

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Section: Discussionsupporting

confidence: 90%

“…). Thus, the covariates were selected based on these characteristics and previous studies (Morelle et al, 2019; Podgórski et al, 2019; Cukor et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Section: Covariatesmentioning

confidence: 99%

Section: Covariatesmentioning

confidence: 99%

See 2 more Smart Citations

Modelling the deathbed of ASF-infected wild boars in South Korea using 2019-2020 national surveillance data

Lim¹,

Vergne

Pak³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…ASF was subsequently reported in Ukraine and Belarus in July 2012 and June 2013, respectively. In January 2014, ASF reached Eastern Europe, where it spread throughout Estonia, Latvia, Lithuania, Poland, Belgium, Bulgaria, Moldova, Czech Republic, Hungary, Romania, Slovakia, Serbia, Greece, and Germany, affecting wild boars and, in some countries, domestic pigs (3)(4)(5)(6)(7)(8)(9)(10). In addition, since 2017, ASF has rapidly spread eastward, with the Russian Federation registering new cases in Eastern Siberia followed by China in 2018; in 2019 Mongolia, Vietnam, Cambodia, Democratic People's Republic of Korea (North Korea), Republic of Korea (South Korea), Lao People's Democratic Republic, Myanmar, Philippines, Timor-Leste, and Indonesia, and in 2020, India and Papua New Guinea also registered cases of ASF.…”

Section: Introductionmentioning

confidence: 99%

Risk for African Swine Fever Introduction Into Kazakhstan

et al. 2021

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African swine fever (ASF) is a disease of swine that is endemic to some African countries and that has rapidly spread since 2007 through many regions of Asia and Europe, becoming endemic in some areas of those continents. Since there is neither vaccine nor treatment for ASF, prevention is an important action to avoid the economic losses that this disease can impose on a country. Although the Republic of Kazakhstan has remained free from the disease, some of its neighbors have become ASF-infected, raising concerns about the potential introduction of the disease into the country. Here, we have identified clusters of districts in Kazakhstan at highest risk for ASF introduction. Questionnaires were administered, and districts were visited to collect and document, for the first time, at the district level, the distribution of swine operations and population in Kazakhstan. A snowball sampling approach was used to identify ASF experts worldwide, and a conjoint analysis model was used to elicit their opinion in relation to the extent at which relevant epidemiological factors influence the risk for ASF introduction into disease-free regions. The resulting model was validated using data from the Russian Federation and Mongolia. Finally, the validated model was used to rank and categorize Kazakhstani districts in terms of the risk for serving as the point of entry for ASF into the country, and clusters of districts at highest risk of introduction were identified using the normal model of the spatial scan statistic. Results here will help to allocate resources for surveillance and prevention activities aimed at early detecting a hypothetical ASF introduction into Kazakhstan, ultimately helping to protect the sanitary status of the country.

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Survey of wild boar hunter interactions with pig farming in central Europe

Jánoska

Nagy

Tari

et al. 2022

Veterinary Medicine & Sci

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African swine fever (ASF) is a fatal animal disease without zoonotic potential but greatly impacts human well‐being, especially in the most vulnerable human communities. In Europe, ASF concerns mostly the wildlife domain of health. The main vector of the disease is confirmed to be the wild boar, though long‐distance jumps of the infection are due to anthropogenic effects. This study aimed to evaluate the potential role of hunting assistant personnel (beaters and carcass handlers) in ASF spread in Hungary. Based on a personal interview survey, we attempted to identify the epidemiological risk caused by hunting activities and the hunting personnel. The interviews with 58 hunting workers confirmed that an extent backyard pig sector (13 pig farmers) and pork production system (31 pork producers) existed within the study region out of the authorities’ sight. Two pig farmers did not wear special working clothes for pig caring, seven pork producers disposed of slaughter offal in the settlements periphery, and six persons regularly contacted distant pig farms. The revealed knowledge, attitude, and practice of the questioned pig farmers suggested that this sector would be very vulnerable in an epidemic situation; moreover, backyard farms would cause a great risk for wildboar populations. Considering that the study region is the third poorest region of Hungary, these findings called attention to the high epidemiologic risk of socioeconomic inequality between different regions within the European Union.

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Spatial epidemiology of African swine fever: Host, landscape and anthropogenic drivers of disease occurrence in wild boar

Cited by 55 publications

References 38 publications

Modelling the deathbed of ASF-infected wild boars in South Korea using 2019-2020 national surveillance data

Modelling the deathbed of ASF-infected wild boars in South Korea using 2019-2020 national surveillance data

Risk for African Swine Fever Introduction Into Kazakhstan

Survey of wild boar hunter interactions with pig farming in central Europe

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