No abstract

Foot-and-mouth disease (FMD) virus causes an acute vesicular disease of domesticated and wild ruminants and pigs. Identifying sources of FMD outbreaks is often confounded by incomplete epidemiological evidence and the numerous routes by which virus can spread (movements of infected animals or their products, contaminated persons, objects, and aerosols). Here, we show that the outbreaks of FMD in the United Kingdom in August 2007 were caused by a derivative of FMDV O1 BFS 1860, a virus strain handled at two FMD laboratories located on a single site at Pirbright in Surrey. Genetic analysis of complete viral genomes generated in real-time reveals a probable chain of transmission events, predicting undisclosed infected premises, and connecting the second cluster of outbreaks in September to those in August. Complete genome sequence analysis of FMD viruses conducted in real-time have identified the initial and intermediate sources of these outbreaks and demonstrate the value of such techniques in providing information useful to contemporary disease control programmes.

Estimating detailed transmission trees that reflect the relationships between infected individuals or populations during a disease outbreak often provides valuable insights into both the nature of disease transmission and the overall dynamics of the underlying epidemiological process. These trees may be based on epidemiological data that relate to the timing of infection and infectiousness, or genetic data that show the genetic relatedness of pathogens isolated from infected individuals. Genetic data are becoming increasingly important in the estimation of transmission trees of viral pathogens due to their inherently high mutation rate. Here, we propose a maximum-likelihood approach that allows epidemiological and genetic data to be combined within the same analysis to infer probable transmission trees. We apply this approach to data from 20 farms infected during the 2001 UK foot-and-mouth disease outbreak, using complete viral genome sequences from each infected farm and information on when farms were first estimated to have developed clinical disease and when livestock on these farms were culled. Incorporating known infection links due to animal movement prior to imposition of the national movement ban results in the reduction of the number of trees from 41 472 that are consistent with the genetic data to 1728, of which just 4 represent more than 95% of the total likelihood calculated using a model that accounts for the epidemiological data. These trees differ in several ways from those constructed prior to the availability of genetic data.

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

Speed is paramount in the diagnosis of foot-and-mouth disease (FMD) and simplicity is required if a test is to be deployed in the field. The development of a one-step, reverse transcription loop-mediated amplification (RT-LAMP) assay enables FMD virus (FMDV) to be detected in under an hour in a single tube without thermal cycling. A fragment of the 3D RNA polymerase gene of the virus is amplified at 65 degrees C in the presence of a primer mixture and both reverse transcriptase and Bst DNA polymerase. Compared with real-time RT-PCR, RT-LAMP was consistently faster, and ten copies of FMDV transcript were detected in twenty-two minutes. Amplification products were detected by visual inspection, agarose gel electrophoresis, or in real-time by the addition of a fluorescent dye. The specificity of the reaction was demonstrated by the absence of amplification of RNA from other viruses that cause vesicular diseases and from that of genetically related picornaviruses. Diagnostic sensitivity was validated by the amplification of reference FMDV strains and archival material from field cases of FMD. In comparison with the performance of the established diagnostic TaqMan assay, RT-LAMP appears to be sensitive, rapid, specific, and cost-effective, with the potential for field deployment and use by developing countries for FMDV surveillance.