Ecological approaches in veterinary epidemiology: mapping the risk of bat-borne rabies using vegetation indices and night-time light satellite imagery

Abstract

Rabies remains a disease of significant public health concern. In the Americas, bats are an important source of rabies for pets, livestock, and humans. For effective rabies control and prevention, identifying potential areas for disease occurrence is critical to guide future research, inform public health policies, and design interventions. To anticipate zoonotic infectious diseases distribution at coarse scale, veterinary epidemiology needs to advance via exploring current geographic ecology tools and data using a biological approach. We analyzed bat-borne rabies reports in Chile from 2002 to 2012 to establish associations between rabies occurrence and environmental factors to generate an ecological niche model (ENM). The main rabies reservoir in Chile is the bat species Tadarida brasiliensis; we mapped 726 occurrences of rabies virus variant AgV4 in this bat species and integrated them with contemporary Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The correct prediction of areas with rabies in bats and the reliable anticipation of human rabies in our study illustrate the usefulness of ENM for mapping rabies and other zoonotic pathogens. Additionally, we highlight critical issues with selection of environmental variables, methods for model validation, and consideration of sampling bias. Indeed, models with weak or incorrect validation approaches should be interpreted with caution. In conclusion, ecological niche modeling applications for mapping disease risk at coarse geographic scales have a promising future, especially with refinement and enrichment of models with additional information, such as night-time light data, which increased substantially the model’s ability to anticipate human rabies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13567-015-0235-7) contains supplementary material, which is available to authorized users.

Ecological niche modeling of rabies in the changing Arctic of Alaska

“…To overcome such problems, predictive modeling emerged as a powerful method, based on empirical data and best-available science ([34] for rabies; for other examples see [35–38]). Organisms, including pathogens and their hosts, are bound by a certain ecological niche [32, 33, 39].…”

“…Following best practice and state-of-the art methods [33, 34, 38, 41, 42], this investigation tried to define the quantitative envelope of the ecological niche for rabies in the Arctic using Alaska as a test case. We carried out such an analysis with an ecological niche model using machine learning algorithms, based on geographical information systems (GIS) and publicly available environmental data, applied to presence only locations of compiled rabies detections.…”

Detection of potentially novel paramyxovirus and coronavirus viral RNA in bats and rats in the Mekong Delta region of southern Viet Nam

“…Bats are a known reservoir for rabies virus (Escobar et al., ; Jakava‐Viljanen et al., ; Moratelli & Calisher, ; Rocha, de Oliveira, Heinemann, & Gonçalves, ), and have recently been associated with other viral pathogens with a severe infection phenotype in humans. Notoriously, Nipah and Menangle viruses, both paramyxoviruses, have caused outbreaks in humans, horses and pigs in Australia, Cambodia, Malaysia, Bangladesh and India (Escaffre et al., ; Kulkarni, Tosh, Venkatesh, & Senthil, ).…”

Collinearity in ecological niche modeling: Confusions and challenges

“…Maxent has been applied to a wide range of studies, including those related to discovering rare species (Fois, Fenu, Lombrana, Cogoni, & Bacchetta, 2015;Jackson & Robertson, 2011;Menon, Choudhury, Khan, & Peterson, 2010), conservation and invasive species management (Feng, Lin, Qiao, & Ji, 2015;Ficetola, Thuiller, & Miaud, 2007;Park & Potter, 2015a, 2015bRoura-Pascual, Brotons, Peterson, & Thuiller, 2009), and disease transmission (Escobar et al, 2015;Gonzalez et al, 2011). Concurrently, many methodological studies have aimed to optimize model performance.…”