Potential distribution ofPythium insidiosumin Rio Grande do Sul, Brazil, and projections to neighbour countries

Abstract

Pythium insidiosum is a widespread pathogen that causes pythiosis, a disease with severe health consequences in horses and humans worldwide. Latin America hosts one of the largest, but scattered, horse herds, making it critical to identify areas at high risk of pythiosis transmission to help guide surveillance in areas with disease transmission risk. We utilized ecological niche modelling and epidemiological data to reconstruct the ecological conditions for pathogen circulation to identify areas with potential risk of pythiosis in Brazil and Uruguay. We surveyed 338 horse farm locations in southern Brazil and reconstructed the landscape conditions where the disease is endemic, based on the association between high-resolution satellite imagery and P. insidiosum serology. The final model was projected to the entire Brazil-Uruguay border region to predict areas at risk of disease transmission. We detected 66 seropositive farms and risk of infection in areas with low vegetation and low temperatures. There was a significant difference between seropositive and seronegative locations, mainly during the summer, suggesting that P. insidiosum circulates under specific landscape conditions. Areas that were unsuitable for P. insidiosum circulation were clustered in the central, north-eastern, and north-western regions of the study area. Our findings suggest that P. insidiosum is a generalist pathogen that has the potential to circulate across broad areas in Uruguay and Rio Grande do Sul in Brazil. The risk maps generated may help stakeholders with disease prevention, as well as highlight pythiosis as a potential transboundary disease with economic and public health importance.

Spatial distribution and spread potential of sixteenLeptospiraserovars in a subtropical region of Brazil

“…Ecological niche modelling is used to characterize environmental requirements of species and their potential distributions (Peterson, ; Peterson & Vieglais, ; Qiao et al, ). These analyses have been applied for a wide variety of epidemiological purposes such as the prediction of species invasions into novel areas (Benedict, Levine, Hawley, & Lounibos, ; Machado et al, ), anticipation of disease emergence (Peterson, Bauer, & Mills, ; Williams & Peterson, ), and forecast of the impact of climate change on future emerging disease distributions (Baquero & Machado, ; Daszak et al, ; Gálvez, Descalzo, Guerrero, Miró, & Molina, ; González et al, ; De Oliveira et al, ). Our approach represents a novel application of ENM aimed to generate new knowledge about the ecology of Leptospira at serovar level.…”

“…ENM explores geographic and ecological patterns of vectors, host and pathogens to inform about its potential distribution (Peterson, ). This approach has shown effectiveness under diverse applications to fundamental ecological questions such as areas at risk of disease infection (Machado, Weiblen, & Escobar, ), likely pathogen spillover to humans (Peterson, Martínez‐Campos, Nakazawa, & Martínez‐Meyer, ; Samy, Thomas, Wahed, Cohoon, & Peterson, ) and environmental factors linked to infectious diseases (Jia & Joyner, ; Sallam et al, ). Thus, ENM has proven to be a powerful approach to reconstruct the likely factors shaping infectious diseases distributions.…”

Nairobi Sheep Disease Virus: a historical and epidemiological perspective

“…For the global minimum AICc values, 1 model had delta AICc values <=2, and met the full criteria used in the selection step (Figure 4). We emphasize that this selected model met both criteria but did not have lower AICc, which is often utilized as single decision criteria in model selection (96–98) (see the blue triangle which highlights the selected final mode- Figure 4). In order to identify areas of higher risk for NSDv occurrences, the selected model identified the geographic risk areas for small ruminants.…”

Spatial distribution and spread potential of sixteen Leptospira serovars in a subtropical region of Brazil

“…ENM is used to characterize environmental requirements of species and their potential distribution s (Peterson, 2014; Peterson & Vieglais, 2001; Qiao et al, 2018). These analyses have been applied for a wide variety of epidemiological purposes such as the prediction of species invasions into novel areas (Benedict et al, 2007; Machado et al, 2018), anticipation of disease emergence (Peterson, Bauer, & Mills, 2004; Williams & Peterson, 2009), and forecast of the impact of climate change on future emerging disease distributions (González et al, 2010; Gálvez et al, 2011; Daszak et al, 2013; De Oliveira et al, 2017; Baquero and Machado, 2018). Our approach represents a novel application of ENM aimed to generate new knowledge about the ecology of Leptospira at serovar level.…”

“…ENM explores geographic and ecological patterns of vectors, hosts or pathogens distribution, and transmission (Peterson, 2006). This approach has shown effectiveness under diverse applications to fundamental ecological questions such as areas at risk of disease infection (Machado et al, 2018), likely pathogen spillover to humans (Peterson, Martínez-Campos, Nakazawa, & Martínez-Meyer, 2005; Samy, Thomas, Wahed, Cohoon, & Peterson, 2016) and environmental factors linked to infectious diseases (Jia and Joyner, 2015; Sallam et al, 2017). Thus, ENM has proven to be a powerful approach to reconstruct the likely factors shaping infectious diseases distributions.…”