Human‐mediated Foot‐and‐mouth Disease Epidemic Dispersal: Disease and Vector Clusters

Abstract

Disease clusters were retrospectively explored at national level using a geo-referenced dataset from the 2001 Uruguayan Foot-and-Mouth Disease (FMD) epidemic. Disease location and time (first 11 epidemic weeks) were analysed across 250 counties (of which 160 were infected), without and with control for human mobility related factors (human population and road densities). The null hypothesis of random disease distribution over space and/or time was assessed with: (i) purely temporal; (ii) purely spatial; and (iii) space/time tests. At least within epidemic weeks 2 and 6, a principal disease cluster was observed in 33 contiguous counties (P < 0.01). Two secondary clusters, located at >100 km from each other, were also observed (P < 0.01). The purely spatial test that controlled for human population density identified two non-contiguous clusters (P < 0.01). Space and time analysis also revealed the same 33 counties as members of the principal cluster, of which 31 were also clustered when human population was controlled (P < 0.01). No clusters were reported by the spatial test when road density was assessed. The hypothesis that human mobility related factors autocorrelate with disease was empirically supported by two pieces of information: (i) removal of human population/road densities eliminated >93.9% of the counties included in the principal disease cluster; and (ii) statistically significant correlations (P < 0.05) were observed in the first three epidemic weeks between road density and the number of cases. Clusters where human population density was associated with 47% greater number of cases/sq. km than that of the principal cluster indicated possible roles as disease vectors (vector clusters). Selective control policy in vector clusters is recommended. Periodic (i.e. weekly) cluster and correlation analyses of both disease and other covariates may facilitate disease surveillance and help design space-specific control policy.

Spatial clustering analysis of the foot-and-mouth disease outbreaks in Mato Grosso do Sul state, Brazil - 2005

“…Dividing these figures, we obtain a rough estimate of 1.25km per day for the speed of propagation, which is similar to the speed estimated by FERREIRA (2000). RIVAS et al (2006) observed that the data from the 2001 Uruguayan FMD epidemic were compatible with two hypotheses, namely: (i) disease clusters may be observed even in rapid disseminating epidemics; (ii) human mobility-related factors (demographic and road densities) may facilitate epidemic dispersal. It was also observed disease clusters in the FMD epidemic in the Mato Grosso do Sul state, confirming the importance of local spread for the transmission of FMD.…”

“…The K-function is defined as the expected number of further points within a distance s of an arbitrary point, divided by the overall intensity of the points (ROWLINGSON & DIGGLE, 1993). Spatial clustering analysis is a useful tool to study the spread of acute infectious diseases in animal populations.…”

“…The identification of disease cluster was suggested to be important in diseases of rapid dissemination, such as FMD (RIVAS et al, 2006). In particular, RIVAS et al (2006) andPEREZ et al (2004), when analyzing data from the 2001 FMD epidemic in Uruguay and Argentina, observed disease cluster.…”

“…Spatial analysis studies of FMD outbreaks were carried out by several authors (e.g., GERBIER et al, 2002;WILESMITH et al, 2003;PEREZ et al, 2004;RIVAS et al, 2006). The identification of disease cluster was suggested to be important in diseases of rapid dissemination, such as FMD (RIVAS et al, 2006).…”

A Flexible Spatial Framework for Modeling Spread of Pathogens in Animals with Biosurveillance and Disease Control Applications

“…Incorporating the spatial structure of the susceptible host contact network has been shown to be important for predicting emergent population-scale epidemiological parameters like the basic reproductive number and attack rates [21][22][23]. The highly detailed models of the 2001 FMD in the UK by Keeling et al [23] and Ferguson et al [14] were possible due to the availability of precise geo-location data on livestock holdings.…”