ABSTRACT. Foot-and-mouth disease (FMD) occurred recently for the first time in a decade in Japan. The index case was detected on a beef-breeding farm in Miyazaki Prefecture, Southern Japan, on April 20, 2010. After confirmation of this first case, control measures such as stamping out, movement restriction and disinfection were implemented. However, these strategies proved insufficient to prevent the spread of FMD and emergency vaccination was adopted. Up until the last outbreak on July 4, 2010, a total of 292 outbreaks had been confirmed, with about 290,000 animals having been culled. The epidemic occurred in an area with a high density of cattle and pigs, making disease control difficult. Invasion of the disease into a high-density area aided its rapid spread and led to difficulties in locating suitable burial sites. Epidemiological investigations indicated that the disease was introduced into Japan approximately one month before detection. This delay in initial detection is considered to have allowed an increased number of outbreaks in the early stage of the epidemic. Nevertheless, the epidemic was contained within a localized area in Miyazaki Prefecture and was eradicated within three months because of intensive control efforts including emergency vaccination. Although this epidemic devastated the livestock industry in Japan, many lessons can be learnt for the future prevention and control of infectious diseases in animals.

BackgroundAlthough several attempts have been made to control enzootic bovine leukosis (EBL) at the local level, a nationwide control program has not been implemented in Japan, except for passive surveillance. Effective control of EBL requires that the transmission routes of bovine leukemia virus (BLV) infection should be identified and intercepted based on scientific evidence. In this cross-sectional study, we examined the risk factors associated with within-herd transmission of BLV on infected dairy farms in Japan. Blood samples taken from 30 randomly selected adult cows at each of 139 dairy farms were tested by enzyme-linked immunosorbent assay (ELISA). Information on herd management was collected using a structured questionnaire.ResultsInfected farms were defined as those with more than one ELISA-positive animal and accounted for 110 (79.1%) of the 139 farms in the study. Completed questionnaires obtained from 90 of these 110 farms were used for statistical analysis. Seroprevalence, which was defined as the proportions of animals that tested positive out of all animals tested on the farm, was 17.1%, 48.1%, and 68.5% for the 25th, 50th, and 75th percentiles, respectively. A mixed logistic regression analysis implicated a loose housing system, dehorning, and a large number of horseflies in summer as risk factors (coefficient = 0.71, 1.11, and 0.82; p = 0.03, < 0.01, and 0.01, respectively) and feeding of colostrum to newborn calves from their dams as a protective factor (coefficient = -1.11, p = 0.03) against within-farm transmission of BLV on infected farms.ConclusionControl of EBL in infected dairy farms in Japan will be improved by focusing particularly on these risk and protective factors.

No abstract

A number of transmission network models are available that combine genomic and epidemiological data to reconstruct networks of who infected whom during infectious disease outbreaks. For such models to reliably inform decision-making they must be transparently validated, robust, and capable of producing accurate predictions within the short data collection and inference timeframes typical of outbreak responses. A lack of transparent multi-model comparisons reduces confidence in the accuracy of transmission network model outputs, negatively impacting on their more widespread use as decision-support tools. We undertook a formal comparison of the performance of nine published transmission network models based on a set of foot-and-mouth disease outbreaks simulated in a previously free country, with corresponding simulated phylogenies and genomic samples from animals on infected premises. Of the transmission network models tested, Lau’s systematic Bayesian integration framework was found to be the most accurate for inferring the transmission network and timing of exposures, correctly identifying the source of 73% of the infected premises (with 91% accuracy for sources with model support >0.80). The Structured COalescent Transmission Tree Inference provided the most accurate inference of molecular clock rates. This validation study points to which models might be reliably used to reconstruct similar future outbreaks and how to interpret the outputs to inform control. Further research could involve extending the best-performing models to explicitly represent within-host diversity so they can handle next-generation sequencing data, incorporating additional animal and farm-level covariates and combining predictions using Ensemble methods and other approaches.