Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation

Guinat, Claire; Porphyre, Thibaud; Gogin, Andrey; Dixon, Linda K.; Pfeiffer, Dirk U.; Gubbins, Simon

doi:10.1111/tbed.12748

Cited by 61 publications

(119 citation statements)

References 42 publications

(76 reference statements)

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“…Overall,

Q = 728 / Δ t

static multilayer networks were constructed for each value of

Δ t

considered. In this study,

Δ t = 7

days and

Δ t = 28

days were considered for constructing networks, since these represent a range of likely farm‐level infection period that would precede the detection of important swine diseases (such as ASF, CSF and FMD), thus preceding the implementation of disease control activities (Guinat et al, ; Porphyre et al, ). As such, for each scenario considered, 104 and 26 multilayer networks were constructed when

Δ t = 7

days and

Δ t = 28

days, respectively.…”

Section: Methodsmentioning

confidence: 99%

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

Porphyre

Bronsvoort

Gunn

et al. 2020

Transbound Emerg Dis

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When assessing the role of live animal trade networks in the spread of infectious diseases in livestock, attention has focused mainly on direct movements of animals between premises, whereas the role of haulage vehicles used during transport, an indirect route for disease transmission, has largely been ignored. Here, we have assessed the impact of sharing haulage vehicles from livestock transport service providers on the connectivity between farms as well as on the spread of swine infectious diseases in Great Britain (GB). Using all pig movement records between April 2012 and March 2014 in GB, we built a series of directed and weighted static multiplex networks consisting of two layers of identical nodes, where nodes (farms) are linked either by (a) the direct movement of pigs and (b) the shared use of haulage vehicles. The haulage contact definition integrates the date of the move and the duration Δs that lorries are left contaminated by pathogens, hence accounting for the temporal aspect of contact events. For increasing Δs, descriptive network analyses were performed to assess the role of haulage on network connectivity. We then explored how viruses may spread throughout the GB pig sector by computing the reproduction number R. Our results showed that sharing haulage vehicles increases the number of contacts between farms by >50% and represents an important driver of disease transmission. In particular, sharing haulage vehicles, even if Δs < 1 day, will limit the benefit of the standstill regulation, increase the number of premises that could be infected in an outbreak, and more easily raise R above 1. This work confirms that sharing haulage vehicles has significant potential for spreading infectious diseases within the pig sector. The cleansing and disinfection process of haulage vehicles is therefore a critical control point for disease transmission risk mitigation.

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“…Overall,

Q = 728 / Δ t

static multilayer networks were constructed for each value of

Δ t

considered. In this study,

Δ t = 7

days and

Δ t = 28

Δ t = 7

days and

Δ t = 28

days, respectively.…”

Section: Methodsmentioning

confidence: 99%

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

Porphyre

Bronsvoort

Gunn

et al. 2020

Transbound Emerg Dis

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show abstract

“…To calculate R 0 the number of infected animals for each time step (i.e., day) is needed, but these data are virtually impossible to obtain for a wild population. However, for diseases with a high case-lethality ratio, mortality cases can be used as a proxy for the number of newly infected individuals (e.g., [11]). Once appropriate data on carcasses are collected, several mathematical methods can be used to estimate R 0 value [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

R0 Estimation for the African Swine Fever Epidemics in Wild Boar of Czech Republic and Belgium

Marcon

Lindén

Satran

et al. 2019

Veterinary Sciences

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African swine fever (ASF) is a contagious haemorrhagic fever that affects both domesticated and wild pigs. Since ASF reached Europe wild boar populations have been a reservoir for the virus. Collecting reliable data on infected individuals in wild populations is challenging, and this makes it difficult to deploy an effective eradication strategy. However, for diseases with high lethality rate, infected carcasses can be used as a proxy for the number of infected individuals at a certain time. Then R0 parameter can be used to estimate the time distribution of the number of newly infected individuals for the outbreak. We estimated R0 for two ASF outbreaks in wild boar, in Czech Republic and Belgium, using the exponential growth method. This allowed us to estimate both R0 and the doubling time (Td) for those infections. The results are R0 = 1.95, Td = 4.39 for Czech Republic and R0 = 1.65, Td = 6.43 for Belgium. We suggest that, if estimated as early as possible, R0 and Td can provide an expected course for the infection against which to compare the actual data collected in the field. This would help to assess if passive surveillance is properly implemented and hence to verify the efficacy of the applied control measures.

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“…Importantly, during calendar year 2017, the Foreign Animal Disease Diagnostic Laboratory (FADDL) at Plum Island Animal Disease Center performed only two cases requesting ASF testing and both were negative (Personal communication). Of note, however, a stochastic model used to evaluate transmission of ASFV within a population found that the virus may be circulating in a herd for several weeks before a marked increase in mortality is observed, which limits the usefulness of mortality data as a means of early detection in an outbreak scenario ( 105 ). It may also be useful to compare the conditions in the United States to those in Europe to determine whether the buffer zones necessary to quell an ASF outbreak in Europe ( 90 ) would be similar to those required for an outbreak in the United States.…”

Section: Discussionmentioning

confidence: 99%

A Review of African Swine Fever and the Potential for Introduction into the United States and the Possibility of Subsequent Establishment in Feral Swine and Native Ticks

Brown

Bevins

2018

Front. Vet. Sci.

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African swine fever (ASF) is caused by African swine fever virus (ASFV), which can cause substantial morbidity and mortality events in swine. The virus can be transmitted via direct and indirect contacts with infected swine, their products, or competent vector species, especially Ornithodoros ticks. Africa and much of Eastern Europe are endemic for ASF; a viral introduction to countries that are currently ASF free could have severe economic consequences due to the loss of production from infected animals and the trade restrictions that would likely be imposed as a result of an outbreak. We identified vulnerabilities that could lead to ASFV introduction or persistence in the United States or other ASF-free regions. Both legal and illegal movements of live animals, as well as the importation of animal products, byproducts, and animal feed, pose a risk of virus introduction. Each route is described, and current regulations designed to prevent ASFV and other pathogens from entering the United States are outlined. Furthermore, existing ASFV research gaps are highlighted. Laboratory experiments to evaluate multiple species of Ornithodoros ticks that have yet to be characterized would be useful to understand vector competence, host preferences, and distribution of competent soft tick vectors in relation to high pig production areas as well as regions with high feral swine (wild boar or similar) densities. Knowledge relative to antigenic viral proteins that contribute to host response and determination of immune mechanisms that lead to protection are foundational in the quest for a vaccine. Finally, sampling of illegally imported and confiscated wild suid products for ASFV could shed light on the types of products being imported and provide a more informed perspective relative to the risk of ASFV importation.

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Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation

Cited by 61 publications

References 42 publications

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

R0 Estimation for the African Swine Fever Epidemics in Wild Boar of Czech Republic and Belgium

A Review of African Swine Fever and the Potential for Introduction into the United States and the Possibility of Subsequent Establishment in Feral Swine and Native Ticks

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