The Impact of Holding Time on the Likelihood of Moving Internally Contaminated Eggs from a Highly Pathogenic Avian Influenza Infected but Undetected Commercial Table-Egg Layer Flock

Malladi, Sasidhar; Weaver, James H.; Goldsmith, Timothy; Hueston, William D.; Voss, Shauna; Funk, Janel D.; Der, Christina; Bjork, Kathe E.; Clouse, Timothy L.; Hennessey, Morgan; Sampedro, Fernando; Lee, Brendan; Halvorson, David A.

doi:10.1637/10191-041012-reg.1

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…An opportunity to increase the application of dynamical models within the decision-making process is to focus on objective-driven models that provide practical predictions that are directly useful to those managing disease. Indeed, dynamic models have been used mechanistically for avian influenza (Malladi et al, 2012;Weaver et al, 2012;Bonney et al, 2018), brucellosis (Hobbs et al, 2015), and foot-and-mouth disease (Buhnerkempe et al, 2014;Roche et al, 2015) providing practical objectivedriven predictions to support disease management decisions. Recognizing that modeling is a means to improve disease management and outcomes is important and fosters increased use of these tools.…”

Section: Practical Challengesmentioning

confidence: 99%

BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

Miller

Pepin

2019

Journal of Animal Science

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Management and policy decisions are continually made to mitigate disease introductions in animal populations despite often limited surveillance data or knowledge of disease transmission processes. Science-based management is broadly recognized as leading to more effective decisions yet application of models to actively guide disease surveillance and mitigate risks remains limited. Disease-dynamic models are an efficient method of providing information for management decisions because of their ability to integrate and evaluate multiple, complex processes simultaneously while accounting for uncertainty common in animal diseases. Here we review disease introduction pathways and transmission processes crucial for informing disease management and models at the interface of domestic animals and wildlife. We describe how disease transmission models can improve disease management and present a conceptual framework for integrating disease models into the decision process using adaptive management principles. We apply our framework to a case study of African swine fever virus in wild and domestic swine to demonstrate how disease-dynamic models can improve mitigation of introduction risk. We also identify opportunities to improve the application of disease models to support decision-making to manage disease at the interface of domestic and wild animals. First, scientists must focus on objective-driven models providing practical predictions that are useful to those managing disease. In order for practical model predictions to be incorporated into disease management a recognition that modeling is a means to improve management and outcomes is important. This will be most successful when done in a cross-disciplinary environment that includes scientists and decisionmakers representing wildlife and domestic animal health. Lastly, including economic principles of value-of-information and cost-benefit analysis in disease-dynamic models can facilitate more efficient management decisions and improve communication of model forecasts. Integration of disease-dynamic models into management and decision-making processes is expected to improve surveillance systems, risk mitigations, outbreak preparedness, and outbreak response activities.

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Section: Practical Challengesmentioning

confidence: 99%

BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

Miller

Pepin

2019

Journal of Animal Science

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“…In the US, analyses have been performed to simulate the effect of incorporating a holding time before egg movement in conjunction with targeted active surveillance as a novel approach to move eggs from flocks within a control area with a low likelihood of them being contaminated with HPAI. Malladi et al (2012) used a stochastic disease transmission model to estimate the HPAI disease prevalence, mortality and fraction of internally contaminated eggs at various days after the infection of a layer flock. The outcome suggested a significant reduction in the number of internally contaminated eggs moved from an HPAI-infected but undetected flock with each additional day of holding time since the likelihood to detect the outbreak in the flock increases.…”

Section: International Trade and Intra-eu Transportmentioning

confidence: 99%

“…Malladi et al. () used a stochastic disease transmission model to estimate the HPAI disease prevalence, mortality and fraction of internally contaminated eggs at various days after the infection of a layer flock. The outcome suggested a significant reduction in the number of internally contaminated eggs moved from an HPAI‐infected but undetected flock with each additional day of holding time since the likelihood to detect the outbreak in the flock increases.…”

Section: Appendix F – Ai Introduction Non‐wild Bird Pathwaysmentioning

confidence: 99%

Avian influenza

More¹,

Bicout²,

Bøtner³

et al. 2017

EFS2

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“…For an example of the latter, a within-flock model showed that vaccines conferring incomplete protection could counterintuitively increase the infectiousness of a flock, by delaying the time to detection and depopulation (Savill et al, 2006). Within-flock models have also been used to: (1) infer important events – such as the day of introduction – from mortality data (Bos et al, 2007), (2) estimate the risk of HPAIV transmission due to the movement of poultry-industry products from monitored flocks (i.e., flocks not known to be infected with HPAIV) into commerce (Malladi et al, 2012), (3) estimate important epidemiological parameters (Bouma et al, 2009) and (4) estimate mean time to detection for HPAIV strains under different surveillance-detection triggers (Weaver et al, 2012). …”

Section: Transmission Within Poultry Flocksmentioning

confidence: 99%

Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America

Pepin

Spackman

Brown

et al. 2014

Preventive Veterinary Medicine

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Wild birds are the primary source of genetic diversity for influenza A viruses that eventually emerge in poultry and humans. Much progress has been made in the descriptive ecology of avian influenza viruses (AIVs), but contributions are less evident from quantitative studies (e.g., those including disease dynamic models). Transmission between host species, individuals and flocks has not been measured with sufficient accuracy to allow robust quantitative evaluation of alternate control protocols. We focused on the United States of America (USA) as a case study for determining the state of our quantitative knowledge of potential AIV emergence processes from wild hosts to poultry. We identified priorities for quantitative research that would build on existing tools for responding to AIV in poultry and concluded that the following knowledge gaps can be addressed with current empirical data: (1) quantification of the spatio-temporal relationships between AIV prevalence in wild hosts and poultry populations, (2) understanding how the structure of different poultry sectors impacts within-flock transmission, (3) determining mechanisms and rates of between-farm spread, and (4) validating current policy-decision tools with data. The modeling studies we recommend will improve our mechanistic understanding of potential AIV transmission patterns in USA poultry, leading to improved measures of accuracy and reduced uncertainty when evaluating alternative control strategies.

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The Impact of Holding Time on the Likelihood of Moving Internally Contaminated Eggs from a Highly Pathogenic Avian Influenza Infected but Undetected Commercial Table-Egg Layer Flock

Cited by 7 publications

References 23 publications

BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

Avian influenza

Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America

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