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

Abstract

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.

Keywords: Avian influenza; Between-farm spread; Disease-dynamic model; Poultry; Quantitative data; USA.

Fig. 1

Diagram illustrating a simple dynamic-disease model. The host population is divided into “compartments” that differ by disease status (left); in this case susceptible, infectious or recovered (and presumed to be immune). Disease-dynamic models are a mathematical description of pathogen transmission. Solving or conducting simulations with such a model gives an estimate of how the risk of infection within a population (i.e., flock) changes over time. The example shown here is for a pathogen with a basic reproductive number of 5 and a generation time of 5 days spreading through a population of 10,000 individuals. Here the force of infection is proportional to the number of currently infectious birds expressed as a ratio of the total population size.

Fig. 2

Pathways of emergence of AIVs in commercial poultry operations. Red arrows indicate transitions between the different processes in emergence: AIV spillover from wildlife to AIV spread within poultry flocks on a single operation to AIV spread between poultry operations. (A) Spillover mechanisms from wildlife (adapted from Franklin, 2008). Arrows represent movement of AIVs. Bold arrows indicate transmission links that are strongly supported by empirical studies, thin arrows indicate connections that are supported by limited studies and dotted arrows indicate pathways that remain unexplored. Indirect AIV transmission pathways from wild waterfowl (I and II) to poultry (VI) include: drinking contaminated water (III) or contacting non-waterfowl bird species (V) or wild mammals (IV) that were infected through III. IV may also be infected by scavenging infected waterfowl carcasses by wild mammals. *Note that the importance of direct transmission routes from waterfowl to poultry is well-supported in mixed-species backyard flocks but the importance of this connection in transmission to commercial poultry remains to be determined. It is possible that any of these links involve an intermediate link such as human shoes, etc. (B) AIV spread within poultry operations. Once AIV infects poultry in a farm, it can be transmitted directly to other individuals or indirectly through contaminated water, fomites or air. (C) AIV spread between poultry operations. The mechanisms are variable and currently uncertain, particularly for airborne and local spread. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Distribution of poultry and wild mallards in the USA. (A) Density of poultry farms. (B) Thirty year annual average mallard band encounters between 1980 and 2010 based on hunter harvest data.

Fig. 4

Structure of the poultry industry in the USA. The industry consists of 3 main sectors: commercial (A; multiple colors), backyard (B; brown) and live-bird market system (C; purple). Dotted gray arrows indicate connections between different sectors. Numbers on arrows indicate the percentage of poultry that are typically moved between the indicated locations. (A) Production begins with several rounds of breeding to control host genetics. Elite breeder flocks send eggs to the hatchery (H) where chicks hatch, which takes about 3–5 weeks. Chicks leave hatcheries at 1 day-old. Most chicks will go to a pullet farm (P) for 18 weeks before the next breeder farm for variable amounts of time (determined by the breeder), although some may go directly to the next breeder farm. Some table-egg layers will skip the great-grandparent breeding phase. At the final stage of breeding (i.e., multiplier farms), broiler chickens and 43% of turkeys will go directly from a hatchery to a broiler farm or turkey grower, while table-egg layers typically go to a pullet farm before being transferred to a table-egg production farm. The three different types of commercial poultry (turkey, green; broiler chickens, black; and table-egg layers, orange) are bred and reared on separate farms, often by separate companies. (B) and (C) The supply chain for backyard flocks (BYF) and live-bird markets (LBM) involves multiple sources and poultry within each of these holdings incoming and outgoing poultry within each of these sources may contact each other (i.e., none of these are all-in, all-out operations). Note that “bird, swap, auction, flea market” events are the main mechanisms for interaction between BYF and LBMS and thus do not belong exclusively to either BYF or LBMS. Data in this figure were derived from USDA censuses (USDA, 2005, USDA, 2011, Garber, 2006) and unpublished data from surveys of small-scale poultry traders (K. Pabilonia).

Fig. 5

Conceptual representations of two different approaches to modeling poultry disease outbreaks. (A) Bottom-up approach. Parameters from epidemiological investigations and other sources are input into a detailed simulation of routes of transmission. Within the simulation, a local area is represented by the dotted box. In this example, contact of an infectious premises (open circle) with a focal premises that becomes infected (open star) is via long distance transmission represented by the dotted line. Other types of contact and potential routes of transmission from the focal infected premises to susceptible premises (filled circles) may be by different routes represented by the black and gray solid lines. The detailed simulation output is often a spatial-temporal prediction of outbreak dynamics. (B) Top-down approach. Spatial-temporal outbreak infection data are the input for model fitting of a local spread model. Some features of the detailed simulation model, such as long distance transmission represented by the dotted line may be maintained. Other features such as specific, local transmission routes can be subsumed into a general, local transmission kernel represented by the shaded circle with highest transmission risk near the infectious premises and decreasing transmission risk with distance. This approach may result in a simpler model that can more easily be fit to outbreak data. The output of a local spread model is often estimated outbreak parameters, such as between-premises transmission rates or the time between initial infection and notification.