Rob Deardon

Optimal reactive vaccination strategies for a foot-and-mouth outbreak in the UK

Foot-and-mouth disease (FMD) in the UK provides an ideal opportunity to explore optimal control measures for an infectious disease. The presence of fine-scale spatio-temporal data for the 2001 epidemic has allowed the development of epidemiological models that are more accurate than those generally created for other epidemics and provide the opportunity to explore a variety of alternative control measures. Vaccination was not used during the 2001 epidemic; however, the recent DEFRA (Department for Environment Food and Rural Affairs) contingency plan details how reactive vaccination would be considered in future. Here, using the data from the 2001 epidemic, we consider the optimal deployment of limited vaccination capacity in a complex heterogeneous environment. We use a model of FMD spread to investigate the optimal deployment of reactive ring vaccination of cattle constrained by logistical resources. The predicted optimal ring size is highly dependent upon logistical constraints but is more robust to epidemiological parameters. Other ways of targeting reactive vaccination can significantly reduce the epidemic size; in particular, ignoring the order in which infections are reported and vaccinating those farms closest to any previously reported case can substantially reduce the epidemic. This strategy has the advantage that it rapidly targets new foci of infection and that determining an optimal ring size is unnecessary.

Accuracy of models for the 2001 foot-and-mouth epidemic

Since 2001 models of the spread of foot-and-mouth disease, supported by the data from the UK epidemic, have been expounded as some of the best examples of problem-driven epidemic models. These claims are generally based on a comparison between model results and epidemic data at fairly coarse spatio-temporal resolution. Here, we focus on a comparison between model and data at the individual farm level, assessing the potential of the model to predict the infectious status of farms in both the short and long terms. Although the accuracy with which the model predicts farms reporting infection is between 5 and 15%, these low levels are attributable to the expected level of variation between epidemics, and are comparable to the agreement between two independent model simulations. By contrast, while the accuracy of predicting culls is higher (20–30%), this is lower than expected from the comparison between model epidemics. These results generally support the contention that the type of the model used in 2001 was a reliable representation of the epidemic process, but highlight the difficulties of predicting the complex human response, in terms of control strategies to the perceived epidemic risk.

Topographic determinants of foot and mouth disease transmission in the UK 2001 epidemic

BackgroundA key challenge for modelling infectious disease dynamics is to understand the spatial spread of infection in real landscapes. This ideally requires a parallel record of spatial epidemic spread and a detailed map of susceptible host density along with relevant transport links and geographical features.ResultsHere we analyse the most detailed such data to date arising from the UK 2001 foot and mouth epidemic. We show that Euclidean distance between infectious and susceptible premises is a better predictor of transmission risk than shortest and quickest routes via road, except where major geographical features intervene.ConclusionThus, a simple spatial transmission kernel based on Euclidean distance suffices in most regions, probably reflecting the multiplicity of transmission routes during the epidemic.

Simulation-based Bayesian inference for epidemic models

A powerful and flexible method for fitting dynamic models to missing and censored data is to use the Bayesian paradigm via data-augmented Markov chain Monte Carlo (DA-MCMC). This samples from the joint posterior for the parameters and missing data, but requires high memory overheads for large-scale systems. In addition, designing efficient proposal distributions for the missing data is typically challenging. Pseudo-marginal methods instead integrate across the missing data using a Monte Carlo estimate for the likelihood, generated from multiple independent simulations from the model. These techniques can avoid the high memory requirements of DA-MCMC, and under certain conditions produce the exact marginal posterior distribution for parameters. A novel method is presented for implementing importance sampling for dynamic epidemic models, by conditioning the simulations on sets of validity criteria (based on the model structure) as well as the observed data. The flexibility of these techniques is illustrated using both removal time and final size data from an outbreak of smallpox. It is shown that these approaches can circumvent the need for reversible-jump MCMC, and can allow inference in situations where DA-MCMC is impossible due to computationally infeasible likelihoods.

Caspase-3-dependent phagocyte death during systemic Salmonella enterica serovar Typhimurium infection of mice

Growth of Salmonella enterica in mammalian tissues results from continuous spread of bacteria to new host cells. Our previous work indicated that infective S. enterica are liberated from host cells via stochastic necrotic burst independently of intracellular bacterial numbers. Here we report that liver phagocytes can undergo apoptotic caspase‐3‐mediated cell death in vivo, with apoptosis being a rare event, more prevalent in heavily infected cells. The density‐dependent apoptotic cell death is likely to constitute an alternative mechanism of bacterial spread as part of a bet‐hedging strategy, ensuring an ongoing protective intracellular environment in which some bacteria can grow and persist.

Effect of data quality on estimates of farm infectiousness trends in the UK 2001 foot-and-mouth disease epidemic

Most of the mathematical models that were developed to study the UK 2001 foot-and-mouth disease epidemic assumed that the infectiousness of infected premises was constant over their infectious periods. However, there is some controversy over whether this assumption is appropriate. Uncertainty about which farm infected which in 2001 means that the only method to determine if there were trends in farm infectiousness is the fitting of mechanistic mathematical models to the epidemic data. The parameter values that are estimated using this technique, however, may be influenced by missing and inaccurate data. In particular to the UK 2001 epidemic, this includes unreported infectives, inaccurate farm infection dates and unknown farm latent periods. Here, we show that such data degradation prevents successful determination of trends in farm infectiousness.

Bayesian analysis of risk factors for infection with a genotype of porcine reproductive and respiratory syndrome virus in Ontario swine herds using monitoring data.

Porcine reproductive and respiratory syndrome (PRRS) has a worldwide distribution. This economically important endemic disease causes reproductive failure in breeding stock and respiratory tract illness in young pigs. In Ontario restricted fragment length polymorphism (RFLP) 1-18-4 has been determined as one of the most common virus genotypes. Individual-level models (ILMs) for infectious diseases, fitted in a Bayesian MCMC framework, have been used to describe both the spatial and temporal spread of diseases. They are an intuitive and flexible class of models that can take into account population heterogeneity via various individual-level covariates. The objective of this study is to identify relative importance of risk factors for the spread of the genotype 1-18-4 from monitoring data in southern Ontario using ILMs. Specifically, we explore networks through which resources are obtained or delivered, as well as the ownership structure of herds, and identify factors that may be contributing to high risk of infection. A population of 316 herds which experienced their PRRS outbreaks between September 2004 and August 2007 are included in the analyses, in which 194 (61%) are sow herds. During the study period, 45 herds (27 sow herds) experienced their first outbreak due to RFLP 1-18-4. Our results show that the three relatively most important factors for the spread of 1-18-4 genotype in Ontario swine herds were sharing the same herd ownership, gilt source and market trucks. All other networks had relatively smaller impact on spread of this PRRSV genotype. Spatial proximity could not be identified as important contributor to spread. Our findings also suggest that gilt acclimation should be practiced whenever possible and appropriate to reduce the risk for the herd and for others as it is already widely implemented and recommended in the North American swine industry.

Evaluation of external biosecurity practices on southern Ontario sow farms

External biosecurity protocols, aimed at preventing the introduction of new pathogens to the farm environment, are becoming increasingly important in the swine industry. Although assessments at the individual farm level occur regularly, efforts to cluster swine herds into meaningful biosecurity groups and to summarize this information at the regional level are relatively infrequent. The objectives of this study were: (i) to summarize external biosecurity practices on sow farms in southern Ontario; (ii) to cluster these farms into discrete biosecurity groups and to describe their characteristics, the variables of importance in differentiating between these groups, and their geographic distribution; and (iii) to identify significant predictors of biosecurity group membership. Data were collected using the Production Animal Disease Risk Assessment Programs Survey for the Breeding Herd. A subset of variables pertaining to external biosecurity practices was selected for two-step cluster analysis, which resulted in 3 discrete biosecurity groups. These groups were named by the authors as: (i) high biosecurity herds that were open with respect to replacement animals, (ii) high biosecurity herds that were closed with respect to replacement animals, and (iii) low biosecurity herds. Variables pertaining to trucking practices and the source of replacement animals were the most important in differentiating between these groups. Multinomial logistic regression provided insight into which demographic and neighborhood variables serve as significant predictors of biosecurity group membership (p<0.05). Variables in the final regression model include: herd density within a 4.8 km radius, number of sows on the premises, and site production type. The odds of belonging to the high biosecurity group that was open with respect to replacement animals, relative to the low biosecurity group, increased 1.001 times for each additional sow (p=0.001). The odds of belonging to the high biosecurity group that was open with respect to replacement animals, relative to the low biosecurity group, were 6.5 times greater for farms that produced genetic animals than for farms that produced commercial animals (p=0.003). The information obtained through this work allows a better understanding of biosecurity in sow herds at the regional level, and the implementation of biosecurity protocols in North American swine herds in general.

Linearized Forms of Individual-Level Models for Large-Scale Spatial Infectious Disease Systems

Individual-level models (ILMs) for infectious diseases, fitted in a Bayesian MCMC framework, are an intuitive and flexible class of models that can take into account population heterogeneity via various individual-level covariates. ILMs containing a geometric distance kernel to account for geographic heterogeneity provide a natural way to model the spatial spread of many diseases. However, in even only moderately large populations, the likelihood calculations required can be prohibitively time consuming. It is possible to speed up the computation via a technique which makes use a linearized distance kernel. Here we examine some methods of carrying out this linearization and compare the performances of these methods.

Supervised learning and prediction of spatial epidemics

Parameter estimation for mechanistic models of infectious disease can be computationally intensive. Nsoesie et al. (2011) introduced an approach for inference on infectious disease data based on the idea of supervised learning. Their method involves simulating epidemics from various infectious disease models, and using classifiers built from the epidemic curve data to predict which model were most likely to have generated observed epidemic curves. They showed that the classification approach could fairly identify underlying characteristics of the disease system, without fitting various transmission models via, say, Bayesian Markov chain Monte Carlo. We extend this work to the case where the underlying infectious disease model is inherently spatial. Our goal is to compare the use of global epidemic curves for building the classifier, with the use of spatially stratified epidemic curves. We demonstrate these methods on simulated data and apply the method to analyze a tomato spotted wilt virus epidemic dataset.