Modelling spatial concordance between Rocky Mountain spotted fever disease incidence and habitat probability of its vector Dermacentor variabilis (American dog tick)

Abstract

Abstract. The spatial distribution of Dermacentor variabilis, the most commonly identified vector of the bacterium Rickettsia rickettsii which causes Rocky Mountain spotted fever (RMSF) in humans, and the spatial distribution of RMSF, have not been previously studied in the south central United States of America, particularly in Texas. From an epidemiological perspective, one would tend to hypothesise that there would be a high degree of spatial concordance between the habitat suitability for the tick and the incidence of the disease. Both maximum-entropy modelling of the tick's habitat suitability and spatially adaptive filters modelling of the human incidence of RMSF disease provide reliable portrayals of the spatial distributions of these phenomenons. Even though rates of human cases of RMSF in Texas and rates of Dermacentor ticks infected with Rickettsia bacteria are both relatively low in Texas, the best data currently available allows a preliminary indication that the assumption of high levels of spatial concordance would not be correct in Texas (Kappa coefficient of agreement = 0.17). It will take substantially more data to provide conclusive findings, and to understand the results reported here, but this study provides an approach to begin understanding the discrepancy.

Human pathogen co-occurrence in Ixodes ricinus ticks: effects of landscape topography, climatic factors and microbiota interactions

“…distribution are poorly understood, as is their range of host species [44, 81]. Yet, it has previously been found that spotted fever incidence in humans, caused by R. ricketsii , is highest in suboptimal tick habitats [82]. This is in line with our findings and suggests that Rickettsia spp.…”

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

“…Geographically weighted regression (GWR) can be used for these two considerations and can often produce improved models that enable better spatial inference and prediction. Recent studies have applied GWR modeling to drug-resistant tuberculosis versus risk factors (Liu et al, 2011); environmental factors versus typhoid fever (Dewan et al, 2013); local climate and population distribution versus hand, foot, and mouth disease (Hu et al, 2012); and environmental factors and tick-borne disease (Atkinson et al, 2012; Atkinson et al, 2014; Wimberly, Baer & Yabsley, 2008; Wimberly et al, 2008), all showing that predictor variables varied spatially across large geographic regions, implying that the results for such studies may be improved using GWR.…”

Peer Review #1 of "A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters (v0.1)"

“…Manuscript to be reviewed tuberculosis versus risk factors (Liu et al 2011); environmental factors versus typhoid fever (Dewan et al 2013); local climate and population distribution versus hand, foot, and mouth disease (Hu et al 2012); and environmental factors and tick-borne disease (Atkinson et al 2012;Atkinson et al 2014;Wimberly et al 2008a;Wimberly et al 2008b), all showing that predictor variables varied spatially across large geographic regions, implying that the results for such studies may be improved using GWR.…”