Impact of spatial clustering on disease transmission and optimal control

Abstract

Spatial heterogeneities and spatial separation of hosts are often seen as key factors when developing accurate predictive models of the spread of pathogens. The question we address in this paper is how coarse the resolution of the spatial data can be for a model to be a useful tool for informing control policies. We examine this problem using the specific case of foot-and-mouth disease spreading between farms using the formulation developed during the 2001 epidemic in the United Kingdom. We show that, if our model is carefully parameterized to match epidemic behavior, then using aggregate county-scale data from the United States is sufficient to closely determine optimal control measures (specifically ring culling). This result also holds when the approach is extended to theoretical distributions of farms where the spatial clustering can be manipulated to extremes. We have therefore shown that, although spatial structure can be critically important in allowing us to predict the emergent population-scale behavior from a knowledge of the individual-level dynamics, for this specific applied question, such structure is mostly subsumed in the parameterization allowing us to make policy predictions in the absence of high-quality spatial information. We believe that this approach will be of considerable benefit across a range of disciplines where data are only available at intermediate spatial scales.foot-and-mouth | modeling T he spatial distribution of organisms is viewed as critically important for determining population dynamics. Numerous examples from the epidemiological and ecological literature have shown that spatial structure has a profound impact on how population-level dynamics emerge from individual-level behavior (123-4). For infectious diseases in particular, where transmission generally occurs over relatively short distances, spatial structure (and in particular the spatial distribution of sessile hosts) plays three roles: hosts that are far from sources of infection are at very little risk, local transmission and depletion of susceptible hosts can dramatically reduce the speed of epidemic growth, and local control measures can be applied using spatial proximity as a method of targeting at-risk hosts. These three elements are present for any spatial distribution of hosts, but are generally amplified by clustering. The impact of spatial structure on the spread of infectious disease has been examined for humans (5), wildlife (6, 7), and livestock (8, 9), but the ability to make useful quantitative predictions relies on the availability of good quality spatial and epidemic data. In recent years, considerable research has focused on the spread of livestock infections due to the extreme vulnerability of the livestock industry, the potential economic costs, the variety of strategies that can be used as control measures, and the costs associated with such measures.The UK 2001 epidemic of foot-and-mouth disease (FMD) provides a prime example of what can be achieved when comprehensive spatial models, detailed hos...

Cattle movement as a risk factor for epidemics

“…It has been pointed out in previous studies (Miller, 2009;Tildesley et al, 2010), that cattle markets specifically, represent a high risk for the spread of infectious cattle diseases. These places serve as points of contact between infected herds and the displacement of the cattle leads to a rapid propagation over long distances (Matthews et Trasporto di bestiame come fattore di rischio per le epidemie Il trasporto degli animali è uno dei principali fattori di rischio per la comparsa di un'epidemia.…”

Using network properties to predict disease dynamics on human contact networks

“…In a spatial context, there have been several cases where disease dynamics for an empirical contact network have been successfully modelled without needing to incorporate fine-scale network structure (e.g. fast-spreading farm animal disease such as foot-and-mouth [33]; or plague [34]), probably because of the mean degree of these empirical networks.…”

Dynamics of infectious diseases

“…However, if the policy decision is to minimise the epidemic duration (and hence costs on the economic) rather than minimise the number of farms affected, it might be preferable to increase the vaccination radius. Whether it is possible to gain analytic insight into the complex dependency of the optimal vaccination radius upon factors such as the maximum vaccination capacity [236], the spatial clustering of potential host farms [233] or underlying epidemiological parameters remains an open question. At the moment we are restricted to multiple stochastic simulations if we wish to include a degree of realism within our models.…”