Modeling the impact of vaccination control strategies on a foot and mouth disease outbreak in the Central United States

McReynolds, Sara; Sanderson, Michael W.; Reeves, Aaron; Hill, Ashley E.

doi:10.1016/j.prevetmed.2014.10.005

Cited by 20 publications

(21 citation statements)

References 33 publications

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“…Modelling has been used to better understand disease spread in countries in which an FMD epidemic has occurred (Keeling, Woolhouse, May, Davies, & Grenfell, ; M. J. Keeling et al., ) and for supporting the design of targeted control strategies for preventing or mitigating the impact of potential future outbreaks (Buhnerkempe et al., ; Carpenter, Thurmond, & Bates, ; McReynolds, Sanderson, Reeves, & Hill, ; Pineda‐Krch, O'Brien, Thunes, & Carpenter, ; Ward, Highfield, Vongseng, & Garner, ). Because models are, by definition, simplified versions of reality, a fundamental principle of modelling is to assume certain conditions so that models are initially formulated as simple as possible and become only as complicated as needed (Levins, ).…”

Section: Introductionmentioning

confidence: 99%

Managing complexity: Simplifying assumptions of foot-and-mouth disease models for swine

Kinsley

VanderWaal

Craft

et al. 2018

Transbound Emerg Dis

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Compartmental models have often been used to test the effectiveness and efficiency of alternative control strategies to mitigate the spread of infectious animal diseases. A fundamental principle of epidemiological modelling is that models should start as simple as possible and become as complex as needed. The simplest version of a compartmental model assumes that the population is closed, void of births and deaths and that this closed population mixes homogeneously, meaning that each infected individual has an equal probability of coming into contact with each susceptible individual in the population. However, this assumption may oversimplify field conditions, leading to conclusions about disease mitigation strategies that are suboptimal. Here, we assessed the impact of the homogeneous mixing/closed population assumption, which is commonly assumed for within-farm models of highly contagious diseases of swine, such as foot-and-mouth disease (FMD), on predictions about disease spread. Incorporation of farm structure (different barns or rooms for breeding and gestation, farrowing, nursery and finishing) and demography (piglet births and deaths, and animal movement within and off of the farm) resulted in transmission dynamics that differed in the latter portion of an outbreak. Specifically, farm structure and demography, which were included in the farrow to finish and farrow to wean farms, resulted in FMD virus persistence within the population under certain conditions. Results here demonstrate the impact of incorporating farm structure and demography into models of FMD spread in swine populations and will ultimately contribute to the design and evaluation of effective disease control strategies to mitigate the impact of potential incursions.

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Section: Introductionmentioning

confidence: 99%

Managing complexity: Simplifying assumptions of foot-and-mouth disease models for swine

Kinsley

VanderWaal

Craft

et al. 2018

Transbound Emerg Dis

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“…Such models have two main uses: 1) to identify and uncover mechanistic understanding of the system in question, and 2) to project the outbreak to explore potential outcomes under different conditions. For foot-and-mouth disease (FMD), a highly-contagious, viral disease of several economically-important, cloven-hoofed species (such as cattle, sheep, and pigs), model outputs have been used extensively to inform policy-makers of the likely next steps in an outbreak and to explore the efficacy of various control actions (Keeling et al, 2001; Ferguson et al, 2001; Morris et al, 2001; Carpenter, 2001; Keeling et al, 2003; Bates et al, 2003; Kao, 2003; Tildesley et al, 2006; Thornley and France, 2009; Ward et al, 2009; Backer at al., 2012a; Dürr et al, 2014; McReynolds et al, 2014). Such extensive use of models is due, in part, to the large economic ramifications of trade-bans once FMD infection is detected.…”

Section: Introductionmentioning

confidence: 99%

Decision-making for foot-and-mouth disease control: Objectives matter

et al. 2016

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Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modelers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. The output of our analysis frames subsequent discourse between policy-makers, modelers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

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“…All simulations were run separately for each of the five movement restriction zone sizes, two outbreak types (with eight outbreak scenarios), and three compliance levels with 500 iterations each. Previous studies have demonstrated that this number of iterations was sufficient to generate reliable output estimates (22, 24). …”

Section: Methodsmentioning

confidence: 93%

“…For the spread of viruses via direct or indirect contact, contact rates, probability of infection transfer, and distance distribution of recipient premises were specified. The input parameters for contact rates and probability of disease transfer were adopted from published studies (20, 24, 26). The NAADSM used the geolocation of swine premises in Indiana to compute the between-premises distance matrix.…”

Section: Methodsmentioning

confidence: 99%

“…Vaccination (live attenuated) was implemented in the model for all swine premises in the infected zone (3-km radius) (1, 9, 29). The depopulation capacity was modeled to gradually increase from 0 to 10 premises/day by day 7 and 15 premises/day by 15 days onward (24). Additional details on the model parameters and model descriptions can be found elsewhere (21, 22, 28).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Movement Restriction Zone Sizes in Controlling Classical Swine Fever Outbreaks

Yadav¹,

Widmar

Lay

et al. 2017

Front. Vet. Sci.

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The objective of this study was to compare the impacts of movement restriction zone sizes of 3, 5, 9, and 11 km with that of 7 km (the recommended zone size in the United States) in controlling a classical swine fever (CSF) outbreak. In addition to zone size, different compliance assumptions and outbreak types (single site and multiple site) were incorporated in the study. Three assumptions of compliance level were simulated: baseline, baseline ± 10%, and baseline ± 15%. The compliance level was held constant across all zone sizes in the baseline simulation. In the baseline ± 10% and baseline ± 15% simulations, the compliance level was increased for 3 and 5 km and decreased for 9 and 11 km from the baseline by the indicated percentages. The compliance level remained constant in all simulations for the 7-km zone size. Four single-site (i.e., with one index premises at the onset of outbreak) and four multiple-site (i.e., with more than one index premises at the onset of outbreak) CSF outbreak scenarios in Indiana were simulated incorporating various zone sizes and compliance assumptions using a stochastic between-premises disease spread model to estimate epidemic duration, percentage of infected, and preemptively culled swine premises. Furthermore, a risk assessment model that incorporated the results from the disease spread model was developed to estimate the number of swine premises under movement restrictions that would experience animal welfare outcomes of overcrowding or feed interruption during a CSF outbreak in Indiana. Compared with the 7-km zone size, the 3-km zone size resulted in a longer median epidemic duration, larger percentages of infected premises, and preemptively culled premises (P’s < 0.001) across all compliance assumptions and outbreak types. With the assumption of a higher compliance level, the 5-km zone size significantly (P < 0.001) reduced the epidemic duration and percentage of swine premises that would experience animal welfare outcomes in both outbreak types, whereas assumption of a lower compliance level for 9- and 11-km zone sizes significantly (P < 0.001) increased the epidemic duration and percentage of swine premises with animal welfare outcomes compared with the 7-km zone size. The magnitude of impact due to a zone size varied across the outbreak types (single site and multiple site). Overall, the 7-km zone size was found to be most effective in controlling CSF outbreaks, whereas the 5-km zone size was comparable to the 7-km zone size in some circumstances.

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Modeling the impact of vaccination control strategies on a foot and mouth disease outbreak in the Central United States

Cited by 20 publications

References 33 publications

Managing complexity: Simplifying assumptions of foot-and-mouth disease models for swine

Managing complexity: Simplifying assumptions of foot-and-mouth disease models for swine

Decision-making for foot-and-mouth disease control: Objectives matter

Evaluation of Movement Restriction Zone Sizes in Controlling Classical Swine Fever Outbreaks

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