Application of modelling to determine the absence of foot-and-mouth disease in the face of a suspected incursion

Dispersal Kernels may be Scalable: Implications from a Plant Pathogen

AIM

Understanding how spatial scale of study affects observed dispersal patterns can provide insights into spatiotemporal population dynamics, particularly in systems with significant long-distance dispersal (LDD). We aimed to investigate the dispersal gradients of two rusts of wheat with spores of similar size, mass, and shape, over multiple spatial scales. We hypothesized that a single dispersal kernel could fit the dispersal from all spatial scales well, and that it would be possible to obtain similar results in spatiotemporal increase of disease when modeling based on differing scales.

LOCATION

Central Oregon and St. Croix Island.

TAXA

Puccinia striiformis f. sp. tritici, Puccinia graminis f. sp. tritici, Triticum aestivum.

METHODS

We compared empirically-derived primary disease gradients of cereal rust across three spatial scales: local (inoculum source and sampling unit = 0.0254 m, spatial extent = 1.52m) field-wide (inoculum source = 1.52 m, sampling unit = 0.305 m, and spatial extent = 91.44 m), and regional (inoculum source and sampling unit = 152 m, spatial extent = 10.7 km). We then examined whether disease spread in spatially explicit simulations depended upon the scale at which data were collected by constructing a compartmental time-step model.

RESULTS

The three data sets could be fit well by a single inverse-power law dispersal kernel. Simulating epidemic spread at different spatial resolutions resulted in similar patterns of spatiotemporal spread. Dispersal kernel data obtained at one spatial scale can be used to represent spatiotemporal disease spread at a larger spatial scale.

MAIN CONCLUSIONS

Organisms spread by aerially dispersed small propagules that exhibit LDD may follow similar dispersal patterns over a several hundred- or thousand-fold expanse of spatial scale. Given that the primary mechanisms driving aerial dispersal remain constant, it may be possible to extrapolate across scales when empirical data are unavailable at a scale of interest.

Estimation of the Infection Window for the 2010/2011 Korean Foot-and-Mouth Disease Outbreak

Objectives

This study aims to develop a method for calculating infection time lines for disease outbreaks on farms was developed using the 2010/2011 foot-and-mouth disease (FMD) epidemic in the Republic of Korea.

Methods

Data on farm demography, the detection date of FMD, the clinical history for the manifestation of lesions, the presence of antibodies against FMD virus (including antibodies against the structural and nonstructural proteins of serotype O), vaccination status (O1 Manisa strain), the number of reactors and information on the slaughter of infected animals were utilized in this method.

Results

Based on estimates of the most likely infection date, a cumulative detection probability that an infected farm would be identified on a specific day was determined. Peak infection was observed between late December and early January, but peak detection occurred in mid-January. The early detection probability was highest for pigs, followed by cattle (dairy, then beef) and small ruminants. Nearly 90% of the infected pig farms were detected by Day 11 post-infection while 13 days were required for detection for both dairy and beef cattle farms, and 21 days were necessary for small ruminant (goat and deer) farms. On average, 8.1 ± 3.1 days passed prior to detecting the presence of FMD virus on a farm. The interval between infection and detection of FMD was inversely associated with the intensity of farming.

Conclusion

The results of our study emphasize the importance of intensive clinical inspection, which is the quickest method of detecting FMD infection and minimizing the damage caused by an epidemic.

Clinical manifestations of foot-and-mouth disease during the 2010/2011 epidemic in the Republic of Korea

We reviewed the clinical signs of the foot-and-mouth disease (FMD) incidences in the Republic of Korea occurring from November 2010 to April 2011. Profuse salivation, vesiculation, lameness or ataxia, and ulceration were the most commonly observed clinical signs of FMD among the infected animals, irrespective of the species. The clinical signs of FMD manifested more clearly in the dairy cattle and pigs compared to the beef cattle, deer and goats on infected farms. About 54% of the infected dairy farms reported vesicles on the teats as the primary clinical sign, while vesiculation on the nose, including the snout and muzzle, was the major lesion observed in infected beef cattle and pig farms. The teat and feet were the second most frequently vesiculated body parts on infected pigs. Although the average age of the first-to-appear clinical lesion in the animals in the beef and dairy cattle farms subjected to vaccination was higher than that observed in the animals in the farms not subjected to vaccination, a reverse pattern was observed in the pig farms. In this study, the clinical signs of FMD were described on the basis of the subjective observations by the farm workers. The present results highlight the clinical signs expected on specific body parts of different types of susceptible animals, and therefore, they may be useful for generating public awareness, particularly among farm workers, as well as for early detection of future FMD outbreaks.

Advances in biosecurity to 2010 and beyond: towards integrated detection, analysis and response to exotic pest invasions

In order to limit the number and impact of exotic pest invasions, leading-edge technologies must be embraced and embedded within integrated national and international biosecurity systems. Outlined here are recent advances in the detection of exotic pests, and prospects for the early recognition of disease. Applications of new tools are described, using our understanding of the genomes of pathogens and vectors. In addition, the role of mathematical and simulation models to aid both biosecurity planning, and decision making in the face of an epidemic, are discussed, and recent attempts to unify epidemiology and evolutionary dynamics are outlined. Given the importance of emerging diseases and zoonoses, the need to align human and veterinary surveillance within fully integrated systems is underlined.