Modeling Potential Distribution of Oligoryzomys longicaudatus, the Andes Virus (Genus: Hantavirus) Reservoir, in Argentina

Abstract

We constructed a model to predict the potential distribution of Oligoryzomys longicaudatus, the reservoir of Andes virus (Genus: Hantavirus), in Argentina. We developed an extensive database of occurrence records from published studies and our own surveys and compared two methods to model the probability of O. longicaudatus presence; logistic regression and MaxEnt algorithm. The environmental variables used were tree, grass and bare soil cover from MODIS imagery and, altitude and 19 bioclimatic variables from WorldClim database. The models performances were evaluated and compared both by threshold dependent and independent measures. The best models included tree and grass cover, mean diurnal temperature range, and precipitation of the warmest and coldest seasons. The potential distribution maps for O. longicaudatus predicted the highest occurrence probabilities along the Andes range, from 32°S and narrowing southwards. They also predicted high probabilities for the south-central area of Argentina, reaching the Atlantic coast. The Hantavirus Pulmonary Syndrome cases coincided with mean occurrence probabilities of 95 and 77% for logistic and MaxEnt models, respectively. HPS transmission zones in Argentine Patagonia matched the areas with the highest probability of presence. Therefore, colilargos presence probability may provide an approximate risk of transmission and act as an early tool to guide control and prevention plans.

Estimating Hantavirus Risk in Southern Argentina: A GIS-Based Approach Combining Human Cases and Host Distribution

“…, minimum temperature, precipitation of the warmest season) [38] and, consequently, their spatial distribution, which will then influence the occurrence and distribution of the disease. These same variables were found to be significant for host distribution at different spatial scales [19,34,45]. We may infer, therefore, that these variables reflect factors that influence host dynamics and distribution in areas of high transmission and, consequently, determine the number of human cases, as stated in the trophic cascade model.…”

“…In that context, Geographical Information Systems (GIS) and remote sensing (RS) may represent proper tools to describe the spatial distribution of infectious diseases and predict disease risk. These tools have already been used to explain or predict tick-borne [16] and rodent-borne diseases [17,18,19], relating spatial data on land cover and climate to the ecology of the vector or hosts. Species distribution models (SDM) help to delineate the specific habitat requirements of a species [20,21]; coupled with GIS and RS tools, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitats [22].…”

“…The mouse, Oligoryzomys longicaudatus (Bennet, 1832), commonly known as colilargo, is the reservoir of Andes virus (ANDV), the hantavirus responsible for HPS in southern Argentina and Chile [27,28]. The colilargo is a widespread rodent highly abundant in the woods and shrublands in Chile and southwestern Argentina [19,29,30,31,32,33,34]. However, it has also been captured in the steppe and in disturbed habitats, such as the borders of cultivated fields, peridomestic settings and pastures [32,33].…”

“…Therefore, given the lethality of the disease and the possibility of human-to-human transmission in southern Argentina, it is of great interest to identify and explain the environmental variables associated with HPS case occurrence in order to recognize and predict those places where the risk is higher, allowing public health authorities to focus surveillance efforts and concentrate resources where the need is greatest. Previous works have already assessed the distribution of O. longicaudatus in Argentina [19,44,45]. Here, we model and map the distribution of HPS cases caused by ANDV in southern Argentina, to determine the relationship between case occurrence and environmental variables and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the rodent host [19].…”

“…Previous works have already assessed the distribution of O. longicaudatus in Argentina [19,44,45]. Here, we model and map the distribution of HPS cases caused by ANDV in southern Argentina, to determine the relationship between case occurrence and environmental variables and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the rodent host [19]. …”