Integrating land cover and terrain characteristics to explain plague risks in Western Usambara Mountains, Tanzania: a geospatial approach

Abstract

Literature suggests that higher resolution remote sensing data integrated in Geographic Information System (GIS) can provide greater possibility to refine the analysis of land cover and terrain characteristics for explanation of abundance and distribution of plague hosts and vectors and hence of health risk hazards to humans. These technologies are not widely used in East Africa for studies on diseases including plague. The objective of this study was to refine the analysis of single and combined land cover and terrain characteristics in order to gain an insight into localized plague infection risks in the West Usambara Mountains in north-eastern Tanzania. The study used a geospatial approach to assess the influence of land cover and terrain factors on the abundance and spatial distribution of plague hosts (small mammals) and plague vectors (fleas). It considered different levels of scale and resolution. Boosted Regression Tree (BRT) statistical method was used to clarify the relationships between land cover and terrain variables with small mammals and fleas. Results indicate that elevation positively influenced the presence of small mammals. The presence of fleas was clearly influenced by land management features such as miraba. Medium to high resolution remotely sensed data integrated in a GIS have been found to be quite useful in this type of analysis. These findings contribute to efforts on plague surveillance and awareness creation among communities on the probable risks associated with various landscape factors during epidemics. ________________________________________________________________________________________

Modelling the current distribution and predicted spread of the flea species Ctenocephalides felis infesting outdoor dogs in Spain

“…The Inframacaronesian bioclimatic belt emerged as the most likely zone of C. felis infestation out of the six bioclimatic levels analysed. Bioclimatics is an important determinant of habitat suitability for the cat flea and its hosts because it encompasses the effects of both climate and vegetation factors [20,33]. However, as a limitation for a more in-depth knowledge of a whole bioclimatic area, more sample points are required.…”

“…Statistical models help to determine the relative contribution of drivers to map vector occurrence or predict future vector distributions based on expected climate change [18]. In East Africa, scientists have had ample experience in surveying the bacterium Yersinia pestis, the causative agent of the plague, through GIS and Remote Sensing (RS) procedures [19][20][21][22][23]. However, so far only Beugnet et al [14] have modelled the distribution of the cat flea affecting pets based on climate forecasts for a large geographical area [14].…”

Plague risk in the western United States over seven decades of environmental change

“…Dozens of statistical methods have been applied to species distribution modeling in the past few decades, with a wide range of performance (Norberg et al, 2019 ). Over the past few years, classification and regression tree (CART) methods—including random forests and boosted regression trees—have become especially popular for mapping the geographic distribution of infectious diseases (Bhatt et al, 2013 ; Carlson et al, 2019 ; Hieronimo et al, 2014 ; Pigott et al, 2014 ; Richards et al, 2020 ; Shearer et al, 2018 ). Here, we use a fairly new method, Bayesian additive regression trees (BARTs), implemented with the R package embarcadero as a species distribution modeling wrapper for the dbarts package (Carlson, 2020 ; Dorie, 2020 ).…”

Plague risk in the western United States over seven decades of environmental change

“…Dozens of statistical methods have been applied to species distribution modeling in the past few decades, with a wide range of performance. 102 Over the past few years, classification and regression tree methods (CART) – including random forests and boosted regression trees – have become especially popular for mapping the geographic distribution of infectious diseases 103,38,104,105,106,107 . Here, we use a fairly new method, Bayesian additive regression trees (BARTs), implemented with the package as a species distribution modeling wrapper for the package.…”