Risk Maps for the Spread of Highly Pathogenic Avian Influenza in Poultry

Abstract

Devastating epidemics of highly contagious animal diseases such as avian influenza, classical swine fever, and foot-and-mouth disease underline the need for improved understanding of the factors promoting the spread of these pathogens. Here the authors present a spatial analysis of the between-farm transmission of a highly pathogenic H7N7 avian influenza virus that caused a large epidemic in The Netherlands in 2003. The authors developed a method to estimate key parameters determining the spread of highly transmissible animal diseases between farms based on outbreak data. The method allows for the identification of high-risk areas for propagating spread in an epidemiologically underpinned manner. A central concept is the transmission kernel, which determines the probability of pathogen transmission from infected to uninfected farms as a function of interfarm distance. The authors show how an estimate of the transmission kernel naturally provides estimates of the critical farm density and local reproduction numbers, which allows one to evaluate the effectiveness of control strategies. For avian influenza, the analyses show that there are two poultry-dense areas in The Netherlands where epidemic spread is possible, and in which local control measures are unlikely to be able to halt an unfolding epidemic. In these regions an epidemic can only be brought to an end by the depletion of susceptible farms by infection or massive culling. The analyses provide an estimate of the spatial range over which highly pathogenic avian influenza viruses spread between farms, and emphasize that control measures aimed at controlling such outbreaks need to take into account the local density of farms. Modern approaches in the epidemiology of infectious diseases include the use of mechanistic mathematical models to analyze and predict the dynamics of disease transmission. Modelling work during the massive epidemic of foot-and-mouth-disease in 2001 in Great Britain has provided an important example of how such analyses can be performed whilst an epidemic unfolds, predicting the effectiveness of intervention efforts and thus helping to inform policymaking. In this article, we use the example of highly pathogenic avian influenza in poultry to set out a computational approach yielding risk maps for the spread of highly transmissible livestock diseases. In these risk maps, geographic areas are identified in which a given intervention strategy fails to control the spread of the disease between farms. Using the epidemiological data of a large avian influenza epidemic in The Netherlands in 2003, this approach yields an estimate of the distance-dependent transmission probability. From this transmission probability, the transmission potential is calculated for all farms in The Netherlands, leading to the identification of two high-risk areas, defined as clusters of farms with transmission potential exceeding unity. The risk map concept is an instrument suitable for analyses of epidemic control options both during crisis and in peacetime.