Risk based culling for highly infectious diseases of livestock

Abstract

The control of highly infectious diseases of livestock such as classical swine fever, foot-and-mouth disease, and avian influenza is fraught with ethical, economic, and public health dilemmas. Attempts to control outbreaks of these pathogens rely on massive culling of infected farms, and farms deemed to be at risk of infection. Conventional approaches usually involve the preventive culling of all farms within a certain radius of an infected farm. Here we propose a novel culling strategy that is based on the idea that farms that have the highest expected number of secondary infections should be culled first. We show that, in comparison with conventional approaches (ring culling), our new method of risk based culling can reduce the total number of farms that need to be culled, the number of culled infected farms (and thus the expected number of human infections in case of a zoonosis), and the duration of the epidemic. Our novel risk based culling strategy requires three pieces of information, viz. the location of all farms in the area at risk, the moments when infected farms are detected, and an estimate of the distance-dependent probability of transmission.

Figure 1

Classification of the farms in the model. Farms are either susceptible to infection, infected but not yet infectious (exposed), infected and infectious, detected and not infectious anymore, or removed from the system by culling.

Figure 2

Overview of model parameters. (a) Distribution of the days to detection of an infected farm, (b) Culling capacity as a function of the time since detection of the outbreak, (c) Hazard kernels with an increased and decreased tail, and the misspecified kernel, (d) Hazard kernels with increased and decreased capacity.

Figure 3

Overview of how to calculate the approximate risk that farms were infected in the past based on knowledge of the detected infections (equation 8). In the example below, at day 7 an infected farm is identified as being infected. On day 7 a neighboring farm has on average been exposed to the infected farm for the last 7 days. If the susceptible farm was not detected as being infected then on day 8, it has been on average exposed for 6 days. And so on, until day 14 when on average there is no further exposure. In equation 5 at, for example, day 9, t = 9, t_jd = 7, and T = 7 which results in 5 days exposure.

Figure 4

Examples of outbreaks (a) homogeneous Poisson clustered, (b) moderately clustered (base scenario), and (c) clustered map. Left graph shows a starting position with infected farms in red, the right graph shows an end situation with culled farms in blue.