Dotted grey arrows suggest connections between different sectors. chicken, PU 02 leading to improved steps of accuracy and reduced uncertainty PU 02 when evaluating option control strategies. Keywords:Avian influenza, USA, Between-farm spread, Disease-dynamic model, Quantitative data, Poultry == 1. Introduction == Emergence of avian influenza viruses (AIVs) in poultry remains a global problem that can cost hundreds of millions of US dollars (Halvorson, 2009,Lupiani and Reddy, 2009). In the USA, even low-pathogenic avian influenza viruses (LPAIVs) can cost millions of dollars to control once detected in commercial poultry (Davison et al., 1999,Halvorson, 2009). Major goals of the USA national plan (Foreign Animal Diseases Preparedness and Response Plan) for minimizing losses due to AIVs are: (1) to prevent the introduction of AIVs into poultry, (2) to identify infected flocks as quickly as possible, and (3) to eliminate the computer virus as quickly as possible once it is detected (USDA, 2012). These goals are achieved through biosecurity (management procedures that minimize introduction or dissemination of infectious diseases), diagnostics and surveillance (detection of AIVs), depopulation and controlled slaughter, education of flock owners/workers and public outreach, all of which occur in a planned, coordinated manner (USDA, 2012). In developing and implementing specific prevention and response activities, multiple biological, political and economic factors are considered, such as computer virus pathotype (either highly pathogenic avian influenza computer virus: HPAIV, or LPAIV), the poultry commodity or commodities affected, the type of operation (i.e., commercial, backyard or live-bird market), the density of poultry in a geographic area, the demands of export markets, federal versus state regulatory authority, availability of financial compensation, public belief and potential for zoonotic transmission of the computer virus. Thus, the numerous response activities that occur depend on scenario-specific circumstances. The success of any strategy is dependent on trust, co-operation and conversation between industry and government (Swayne PU 02 and Akey, 2005). Consequently, it can be challenging to assimilate all of the necessary information during an emergency. Sound quantitative tools are essential for preparedness and response planning. Preparedness and response modeling are two complementary quantitative approaches for informing policy-based decisions made during an AIV event. During preparedness modeling, there is more time for model formulation, evaluation and situational analysis, but appropriate data from previous outbreaks may be unavailable or irrelevant. In response modeling, appropriate quantitative data are likely being collected and analyzed as the outbreak unfolds, but time for detailed evaluation of quantitative methods is very limited. Because preparedness and response modeling involve comparable methods and data, preparedness modeling can and does facilitate response modeling. The development, detailed investigation, and validation of several sound quantitative approaches prior to an event are important for performing response analyses with high confidence in a short period of time. Disease-dynamic models are useful for informing control guidelines (Anderson and May, 1992) because they incorporate a quantitative description of how transmission changes during the course of an epidemic (Fig. 1). Adding additional components, such as age-structure or life-history stage, to simple disease-stage models (i.e., models with different disease says such as susceptible, infectious or recovered;Fig. 1) allows determination of how alternative control strategies, implemented at different stages of the transmission process, will impact epidemic dynamics. Disease-dynamic models are characterized by the presence of a pressure of contamination (rate at which a susceptible individual acquires disease) term that defines precisely how the infection hazard experienced by a susceptible individual (or farm) depends on the current number of infectious individuals (or farms), their proximity and their type. A key parameter that can usually Rabbit polyclonal to ANXA13 be derived using the pressure if contamination term is the basic reproductive number,R0, defined as the expected number of secondary infections generated by one infectious individual (or farm) in an otherwise susceptible population.R0is used to assess the required proportion of a population that must be rendered non-transmissible for an outbreak to be controlled (Heesterbeek and Roberts, 2007) and is predictive of the impact of interventions in reducing the.