A determination of the spatial concordance between Lyme disease incidence and habitat probability of its primary vector Ixodes scapularis (black-legged tick)

Abstract

The spatial distribution of Ixodes scapularis, the most common tick vector of the bacterium Borrelia burgdorferi, the cause of Lyme disease in humans, has not been studied previously in Texas, United States of America. It has only rarely been reported in this state, so its local, spatial relationship to the distribution of this disease is unknown. From an epidemiological perspective, one would tend to hypothesise that there should be a high degree of spatial concordance between habitat suitability for the tick and incidence of the disease. Both maximum-entropy modelling of the tick's habitat probability and modelling of human incidence of Lyme disease using spatially adaptive filters provide reliable portrayals of the spatial distributions of these phenomena. Even though rates of human cases of Lyme disease as well as rates of Ixodes ticks infected with Borrelia bacteria are both relatively low in Texas, the best data currently available indicate that the assumption of high levels of spatial concordance would not be correct in Texas (Kappa coefficient of agreement = 0.039). 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.

Assessment of the Geographic Distribution of Ornithodoros turicata (Argasidae): Climate Variation and Host Diversity

“…Linkages of biotic and abiotic factors to tick distribution, and risk of exposure to tick-borne pathogens have been most investigated and modeled for surface dwelling ixodid ticks [ 22 , 23 ]. Nidicolous argasid ticks present an additional challenge for modeling, as their life history plays out in niches between subterranean microclimates of burrows or landscape cavities (e.g., caves) and the external environment [ 24 ].…”

Peer Review #1 of "Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus (v0.1)"

“…The pathogen may be contracted during outdoor activities in a higher risk area, and then later their disease is diagnosed by the victim's local physician and reported using a local address. While that idea is a common-sense caveat in many vector-borne research conclusions (see for example : Atkinson, Sarkar et al (2012); (Atkinson, Sarkar et al 2014); M'ikanatha and Iskander (2014); Riddle (2020)), this data mining geovisualization analysis provides some initial evidence to that effect. The low correspondence between WNV habitat risk ( Figure 2) and actual incidence of WNV disease in the population (Figure 9) highlights why a geographically based visualization of the relationships between environmental and socio-economic data may be useful.…”

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.…”