Metapopulation Dynamics Enable Persistence of Influenza A, Including A/H5N1, in Poultry

Thanks to K. Sturm-Ramirez, C. Jessup, J. Rosenthal and the staff of EcoHealth Alliance for feedback. Disclaimer: The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.Conceived and designed the experiments: PRH TF RH DZ CSA AG MJM XX TB PD. Performed the experiments: PRH. Analyzed the data: PRH. Contributed reagents/materials/analysis tools: PRH TF RH DZ CSA AG MJM XX TB JHJ PD. Wrote the paper: PRH TF RH DZ CSA AG MJM XX TB JHJ PD.Highly pathogenic influenza A/H5N1 has persistently but sporadically caused human illness and death since 1997. Yet it is still unclear how this pathogen is able to persist globally. While wild birds seem to be a genetic reservoir for influenza A, they do not seem to be the main source of human illness. Here, we highlight the role that domestic poultry may play in maintaining A/H5N1 globally, using theoretical models of spatial population structure in poultry populations. We find that a metapopulation of moderately sized poultry flocks can sustain the pathogen in a finite poultry population for over two years. Our results suggest that it is possible that moderately intensive backyard farms could sustain the pathogen indefinitely in real systems. This fits a pattern that has been observed from many empirical systems. Rather than just employing standard culling procedures to control the disease, our model suggests ways that poultry production systems may be modified.Yeshttp://www.plosone.org/static/editorial#pee

For a fixed total single species total population size 32,000, without non-influenza mortality (μ = 0), the effect of changing local patch size and patch number on (A) frequency of epidemic failure, (B) median length of epidemic in days, and (C) median total number of animals infected over 100 simulations.

<p>Dotted lines represent the empirical 97.5% and 2.5% percentiles, creating a 95% bootstrap confidence interval. Other parameters are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080091#pone-0080091-t001" target="_blank">Table 1</a>, including environmental transmission, except α = 0.1111, and ω = 0.03, without any infection control program. Gray area represents parameter region where 10<6 within a patch.</p

Parameters of the model.

<p>Parameters of the model.</p

Two example random networks.

<p>(A) A Watt-Strogatz random small world network with 64 vertices, with  = 2.33 and ρ = 0.0596 (as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080091#pone-0080091-t001" target="_blank">Table 1</a>), (B) A Barabasi random network with 64 vertices, with the power of the preferential attachment set to 0.5 and the zero appeal to 1.0, both represent a network of 64 farms and markets, each with 500 animals.</p

Dynamics over time of for three different simulation runs, solid grey line represents global prevalence of infection across all patches (farms and markets), colored dashed lines represent abundance of infected individuals within each patch (farm and market).

<p>(A) 1280 patches of size 25, longest simulation run, note only three patches infected, (B) 128 patches of size 250, random simulation run, note global prevalence always less than 2.5%, (C) 16 patches of size 2000, random simulation run, note near deterministic similarity of epidemic in each patch.</p

For a fixed total single species total population size (32,000), without non-influenza mortality (μ = 0), the effect of changing local patch size and patch number on (A) frequency of epidemic failure, (B) median length of epidemic in days, and (C) median total number of animals infected over 100 simulations.

<p>Dotted lines represent the empirical 97.5% and 2.5% percentiles, creating a 95% bootstrap confidence interval. Here with control measures implemented, (π_Report = 0.9, π_Detect = 0.9, τ_Crit = 1, I_Crit = 5). Gray area represents parameter region where 10<6 within a patch.</p