Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China

Hu, Jianghong; Pei, Xin; Sun, Gui‐Quan; Jin, Zhen

doi:10.3389/fphy.2021.785885

Cited by 7 publications

(8 citation statements)

References 25 publications

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“…Feed and transport vehicles were linked to transmission of porcine epidemic diarrhea virus [83][84][85]. Also, transportation of pigs was considered as a potential transmission route for African swine fever virus [86]. An experimental model showed that contaminated trailers can be a source of transmission of porcine reproductive and respiratory syndrome virus [87].…”

Section: Discussionmentioning

confidence: 99%

Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in Colombia

Karl

Díaz²,

Carlos

et al. 2022

Porc Health Manag

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Biosecurity protocols (BP) and good management practices are key to reduce the risk of introduction and transmission of infectious diseases into the pig farms. In this observational cross-sectional study, survey data were collected from 176 pig farms with inventories over 100 sows in Colombia. We analyzed a complex survey dataset to explore the structure and identify clustering patterns using Multiple Correspondence Analysis (MCA) of swine farms in Colombia, and estimated its association with Influenza A virus detection. Two principal dimensions contributed to 27.6% of the dataset variation. Farms with highest contribution to dimension 1 were larger farrow-to-finish farms, using self-replacement of gilts and implementing most of the measures evaluated. In contrast, farms with highest contribution to dimension 2 were medium to large farrow-to-finish farms, but implemented biosecurity in a lower degree. Additionally, two farm clusters were identified by Hierarchical Cluster Analysis (HCA), and the odds of influenza A virus detection was statistically different between clusters (OR 7.29, CI: 1.7,66, p = < 0.01). Moreover, after logistic regression analysis, three important variables were associated with higher odds of influenza detection: (1) “location in an area with a high density of pigs”, (2) “farm size”, and (3) “after cleaning and disinfecting, the facilities are allowed to dry before use”. Our results revealed two clustering patterns of swine farms. This systematic analysis of complex survey data identified relationships between biosecurity, husbandry practices and influenza status. This approach helped to identify gaps on biosecurity and key elements for designing successful strategies to prevent and control swine respiratory diseases in the swine industry.

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Section: Discussionmentioning

confidence: 99%

Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in Colombia

Karl

Díaz²,

Carlos

et al. 2022

Porc Health Manag

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“…[ 51 ]) 2017 mechanistic spatial structure and transmission, limited data yes cattle Italy hypothetical highly contagious livestock holdings livestock movement, personnel static LITS 8 (Hu et al . [ 52 ]) 2021 mechanistic spatial limited data yes pigs China African swine fever livestock holdings, slaughter point livestock movement static LITS 9 (Wiltshire [ 53 ]; [RUSHPNBM]) (Bucini et al . [ 54 ]; Koliba et al .…”

Section: Resultsmentioning

confidence: 99%

“…Alternatively, nodes or edges were sometimes added or removed by copying empirical records exactly (i.e. without modelling these) [ 44 , 52 ]. Contrastingly, eight ABMs and two statistical models generated dynamically evolving networks.…”

Section: Resultsmentioning

confidence: 99%

“…While distance may be a variable in other types of model, some of the simplest spatial models express the probability of an edge between nodes as a function of distance alone. For example, in Hu et al [52], edges between nodes were simply assigned if the Euclidean distance was lower than an empirically informed threshold. In Rossi et al [51], the probability of contacts between farms via veterinary staff visits was estimated by fitting a logistic regression with distance as the predictor variable.…”

Section: Model Frameworkmentioning

confidence: 99%

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Simulating contact networks for livestock disease epidemiology: a systematic review

Leung

Rudge

Fournié

2023

J. R. Soc. Interface.

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Contact structure among livestock populations influences the transmission of infectious agents among them. Models simulating realistic contact networks therefore have important applications for generating insights relevant to livestock diseases. This systematic review identifies and compares such models, their applications, data sources and how their validity was assessed. From 52 publications, 37 models were identified comprising seven model frameworks. These included mathematical models ( n = 8; including generalized random graphs, scale-free, Watts–Strogatz and spatial models), agent-based models ( n = 8), radiation models ( n = 1) (collectively, considered ‘mechanistic’), gravity models ( n = 4), exponential random graph models ( n = 9), other forms of statistical model ( n = 6) (statistical) and random forests ( n = 1) (machine learning). Overall, nearly half of the models were used as inputs for network-based epidemiological models. In all models, edges represented livestock movements, sometimes alongside other forms of contact. Statistical models were often applied to infer factors associated with network formation ( n = 12). Mechanistic models were commonly applied to assess the interaction between network structure and disease dissemination ( n = 6). Mechanistic, statistical and machine learning models were all applied to generate networks given limited data ( n = 13). There was considerable variation in the approaches used for model validation. Finally, we discuss the relative strengths and weaknesses of model frameworks in different use cases.

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“…Three recognized factors influence ASF occurrence: the host, the landscape, and the factors related to human activities [ 10 ]. Out of Africa, swill-feeding and pig/pork transport are considered the main transmission routes, whereas direct contact between infected and non-infected animals has a limited role in long-distance transmission [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Patterns of ASFV Transmission in Domestic Pigs in Serbia

Glišić¹,

Milićević²,

Veljović³

et al. 2023

Pathogens

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The first case of African swine fever in domestic pigs in Serbia was in 2019. The following year, the disease was confirmed in wild boar. Thenceforth, ASF has been continuously reported in both wild and domestic pigs. The outbreaks in domestic pigs could not be linked directly to wild boars, even though wild boars were endemically infected, and reservoirs for ASF. This study aimed to investigate outbreaks and routes of transmission in domestic pigs in a region of central Serbia where no outbreaks in wild boar were reported. Fourteen outbreaks of ASF on backyard farms with low biosecurity were traced back, and no connection to wild boar was found. The epidemic investigation covered 2094 holdings, with 24,368 pigs, out of which 1882 were tested for ASF. In surrounding hunting grounds, field searches were conducted. Dead wild boars were found, and 138 hunted wild boars were negative for ASFV. It was concluded that outbreaks in 2021 were provoked by the illegal trade of live animals and pig products. Even though infective pressure from wild boars is assumed, no positive cases have been found, while the ASFV spreads within the domestic swine population evidenced in four recent outbreaks in 2022.

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Risk Analysis of the Transmission Route for the African Swine Fever Virus in Mainland China

Cited by 7 publications

References 25 publications

Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in Colombia

Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in Colombia

Simulating contact networks for livestock disease epidemiology: a systematic review

Patterns of ASFV Transmission in Domestic Pigs in Serbia

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