Why Did Bluetongue Spread the Way It Did? Environmental Factors Influencing the Velocity of Bluetongue Virus Serotype 8 Epizootic Wave in France

Abstract

Understanding where and how fast an infectious disease will spread during an epidemic is critical for its control. However, the task is a challenging one as numerous factors may interact and drive the spread of a disease, specifically when vector-borne diseases are involved. We advocate the use of simultaneous autoregressive models to identify environmental features that significantly impact the velocity of disease spread. We illustrate this approach by exploring several environmental factors influencing the velocity of bluetongue (BT) spread in France during the 2007–2008 epizootic wave to determine which ones were the most important drivers. We used velocities of BT spread estimated in 4,495 municipalities and tested sixteen covariates defining five thematic groups of related variables: elevation, meteorological-related variables, landscape-related variables, host availability, and vaccination. We found that ecological factors associated with vector abundance and activity (elevation and meteorological-related variables), as well as with host availability, were important drivers of the spread of the disease. Specifically, the disease spread more slowly in areas with high elevation and when heavy rainfall associated with extreme temperature events occurred one or two months prior to the first clinical case. Moreover, the density of dairy cattle was correlated negatively with the velocity of BT spread. These findings add substantially to our understanding of BT spread in a temperate climate. Finally, the approach presented in this paper can be used with other infectious diseases, and provides a powerful tool to identify environmental features driving the velocity of disease spread.

Current mosquito-borne disease emergencies in Italy and climate changes. The neem opportunity

“…Usually the consequence of the genetic upgrade is even more devastating, as reported later, for Xilella [10,11]. The third case concerns pathogens that are strictly linked to a selected host, like the bluetongue story [12][13][14].…”

Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

“…Crase et al (2012) found the RAC approach, which only represents the portion of spatial structure in the dependant variable that is not explained by the explanatory variables, improved the accuracy of parameter estimates and identification of statistically significant variables. An alternative approach is that taken by Pioz et al (2012) when modelling the spread of bluetongue virus in France.…”

“…Beginning with this initial thematic model, a backward stepwise approach based on Akaike's Information Criterion (AIC) was used to select the best fitting thematic model, with the least important variables (based on p values) being removed first (as recommended by Burnham and Anderson 2002). Following the approach taken by Pioz et al (2012), models differing by less than two AIC points were considered to receive identical support from the data. In these instances the more parsimonious model was selected, unless there was good reason a priori for retaining a specific variable.…”

“…Various approaches for handling spatial heterogeneity in a regression model have been developed, including the use of a term that represents either spatial autocorrelation in the dependent variable or in the residuals from the independent variables (Crase et al 2012) and the use of simultaneous autoregressive models (Pioz et al 2012). If relationships are thought to alter spatially within the study area, geographically weighted regression (GWR) can be used to produce localised models for different spatial regions which take neighbouring observations into account Fotheringham et al 1998).…”

Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

“…Beale et al (2010) consider how regression findings for spatial ecology data are affected by the method used (if at all) to reflect spatial dependence. To exemplify hierarchical models for veterinary data, Pioz et al (2012) apply simultaneous autoregressive (SAR) models to investigate bluetongue spread in French municipalities, while Farnsworth and Ward (2009) apply Bayesian conditional autoregressive (CAR) models to avian influenza H5N1 outbreak data. In such applications, identifying elevated risk in particular areas, detecting elevated risk clusters, or assessing significant predictors of risk, are emphasized, in methods recognizing the explicitly spatial structure of the data.…”