RESUMEN
Estimating vulnerability is critical to understand human-induced influenceimpacts on the environmental system. The purpose of the current study was to integrate machine learning algorithm and Twitter data to estimate environmental vulnerability in the Brazilian Cerrado for the years 2011 and 2016. We first selected six exposure indicators and five sensitivity indicators to build an environmental vulnerability model and applied an Autoencoder algorithm to find the representation of exposure and sensitivity, respectively. Then the Displaced Ideal method was used to estimate environmental vulnerability. Finally, related historical Twitter data was mined from these two years to validate the results. The findings showed that the percent of land classified as areas of low, medium and high environmental vulnerability were 6.72%, 34.85%, and 58.44% in 2011 and 3.45%, 33.68% and 62.87% in 2016, respectively and most high environmental vulnerability areas were in the Southern Cerrado. Moreover, the Twitter data results showed that more than 85% of tweets occurred in the areas considered as high environmental vulnerability class. The work revealed that the Autoencoder algorithm can be used for environmental assessment, and the social media data has potential to effectively analyze the relationship between human activity and the environment. Although the study provided a novel perspective to estimate environmental vulnerability at a regional scale, it was necessary to develop a more comprehensive indicator system that can improve model performance in the future.
Asunto(s)
Medios de Comunicación Sociales , Algoritmos , Brasil , Humanos , Aprendizaje AutomáticoRESUMEN
We screened blood samples from 560 wild rodents collected in southeastern Brazil for antibodies to a recombinant nucleoprotein (rN) of Junín virus. Six rodents were antibody positive (1.1%), demonstrating evidence of infection with mammarenaviruses in several species of Brazilian rodents.
Asunto(s)
Infecciones por Arenaviridae/veterinaria , Arenaviridae/clasificación , Roedores/virología , Animales , Animales Salvajes , Infecciones por Arenaviridae/epidemiología , Infecciones por Arenaviridae/virología , Brasil/epidemiología , Estudios SeroepidemiológicosRESUMEN
Time-series of coarse-resolution greenness values derived through remote sensing have been used as a surrogate environmental variable to help monitor and predict occurrences of a number of vector-borne and zoonotic diseases, including malaria. Often, relationships between a remotely-sensed index of greenness, e.g. the normalized difference vegetation index (NDVI), and disease occurrence are established using temporal correlation analysis. However, the strength of these correlations can vary depending on type and change of land cover during the period of record as well as inter-annual variations in the climate drivers (precipitation, temperature) that control the NDVI values. In this paper, the correlation between a long (260 months) time-series of monthly disease case rates and NDVI values derived from the Global Inventory Modeling and Mapping Studies (GIMMS) data set were analysed for two departments (administrative units) located in the Atlantic Forest biome of eastern Paraguay. Each of these departments has undergone extensive deforestation during the period of record and our analysis considers the effect on correlation of active versus quiescent periods of case occurrence against a background of changing land cover. Our results show that timeseries data, smoothed using the Fourier Transform tool, showed the best correlation. A moving window analysis suggests that four years is the optimum time frame for correlating these values, and the strength of correlation depends on whether it is an active or a quiescent period. Finally, a spatial analysis of our data shows that areas where land cover has changed, particularly from forest to non-forest, are well correlated with malaria case rates.
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Conservación de los Recursos Naturales , Ecosistema , Monitoreo del Ambiente/métodos , Malaria/epidemiología , Lluvia , Árboles , Biodiversidad , Brotes de Enfermedades/estadística & datos numéricos , Monitoreo Epidemiológico , Geografía , Humanos , Paraguay/epidemiología , Análisis de Regresión , Factores de Riesgo , Comunicaciones por Satélite , Estaciones del Año , Estadística como Asunto , Temperatura , Factores de Tiempo , Estudios de Tiempo y Movimiento , Clima Tropical , Organización Mundial de la SaludRESUMEN
New habitat-based models for spread of hantavirus are developed which account for interspecies interaction. Existing habitat-based models do not consider interspecies pathogen transmission, a primary route for emergence of new infectious diseases and reservoirs in wildlife and man. The modeling of interspecies transmission has the potential to provide more accurate predictions of disease persistence and emergence dynamics. The new models are motivated by our recent work on hantavirus in rodent communities in Paraguay. Our Paraguayan data illustrate the spatial and temporal overlaps among rodent species, one of which is the reservoir species for Jabora virus and others which are spillover species. Disease transmission occurs when their habitats overlap. Two mathematical models, a system of ordinary differential equations (ODE) and a continuous-time Markov chain (CTMC) model, are developed for spread of hantavirus between a reservoir and a spillover species. Analysis of a special case of the ODE model provides an explicit expression for the basic reproduction number, R(0), such that if R(0)<1, then the pathogen does not persist in either population but if R(0)>1, pathogen outbreaks or persistence may occur. Numerical simulations of the CTMC model display sporadic disease incidence, a new behavior of our habitat-based model, not present in other models, but which is a prominent feature of the seroprevalence data from Paraguay. Environmental changes that result in greater habitat overlap result in more encounters among various species that may lead to pathogen outbreaks and pathogen establishment in a new host.
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Reservorios de Enfermedades/virología , Infecciones por Hantavirus/transmisión , Infecciones por Hantavirus/veterinaria , Modelos Biológicos , Animales , Ecosistema , Sistemas de Información Geográfica , Infecciones por Hantavirus/epidemiología , Masculino , Cadenas de Markov , Paraguay/epidemiología , Enfermedades de los Roedores/epidemiología , Enfermedades de los Roedores/virología , Especificidad de la EspecieRESUMEN
Hantaviruses may cause serious disease when transmitted to humans by their rodent hosts. Since their emergence in the Americas in 1993, there have been extensive efforts to understand the role of environmental factors on the presence of these viruses in their host rodent populations. HPS outbreaks have been linked to precipitation, but climatic factors alone have not been sufficient to predict the spatial-temporal dynamics of the environment-reservoir-virus system. Using a series of mark-recapture sampling sites located at the Mbaracayú Biosphere Reserve, an Atlantic Forest site in eastern Paraguay, we investigated the hypothesis that microhabitat might also influence the prevalence of Jaborá hantavirus within populations of its reservoir species, Akodon montensis. Seven trapping sessions were conducted during 2005-2006 at four sites chosen to capture variable microhabitat conditions within the study site. Analysis of microhabitat preferences showed that A. montensis preferred areas with little forest overstory and denser vegetation cover on and near the ground. Moreover, there was a significant difference in the microhabitat occupied by antibody-positive vs antibody-negative rodents, indicating that microhabitats with greater overstory cover may promote transmission and maintenance of hantavirus in A. montensis.
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Arvicolinae/virología , Reservorios de Enfermedades/virología , Ecosistema , Infecciones por Hantavirus/veterinaria , Orthohantavirus/inmunología , Animales , Anticuerpos Antivirales/sangre , Arvicolinae/fisiología , Infecciones por Hantavirus/virología , Paraguay , Factores de Riesgo , Estudios Seroepidemiológicos , ÁrbolesRESUMEN
Landscape epidemiology has made significant strides recently, driven in part by increasing availability of land cover data derived from remotely-sensed imagery. Using an example from a study of land cover effects on hantavirus dynamics at an Atlantic Forest site in eastern Paraguay, we demonstrate how automated classification methods can be used to stratify remotely-sensed land cover for studies of infectious disease dynamics. For this application, it was necessary to develop a scheme that could yield both land cover and land use data from the same classification. Hypothesizing that automated discrimination between classes would be more accurate using an object-based method compared to a per-pixel method, we used a single Landsat Enhanced Thematic Mapper+ (ETM+) image to classify land cover into eight classes using both per-pixel and object-based classification algorithms. Our results show that the object-based method achieves 84% overall accuracy, compared to only 43% using the per-pixel method. Producer's and user's accuracies for the object-based map were higher for every class compared to the per-pixel classification. The Kappa statistic was also significantly higher for the object-based classification. These results show the importance of using image information from domains beyond the spectral domain, and also illustrate the importance of object-based techniques for remote sensing applications in epidemiological studies.
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Agricultura/clasificación , Ecología , Infecciones por Hantavirus/parasitología , Fotograbar , Nave Espacial , Animales , Enfermedades Transmisibles , Ecosistema , Orthohantavirus , Infecciones por Hantavirus/epidemiología , Paraguay/epidemiología , Medición de RiesgoRESUMEN
Recently, we reported the discovery of several potential rodent reservoirs of hantaviruses in western (Holochilus chacarius) and eastern Paraguay (Akodon montensis, Oligoryzomys chacoensis, and O. nigripes). Comparisons of the hantavirus S- and M-segments amplified from these four rodents revealed significant differences from each another and from other South American hantaviruses. The ALP strain from the semiarid Chaco ecoregion clustered with Leguna Negra and Rio Mamore (LN/RM), whereas the BMJ-NEB strain from the more humid lower Chaco ecoregion formed a clade with Oran and Bermejo. The other two strains, AAI and IP37/38, were distinct from known hantaviruses. With respect to the S-segment sequence, AAI from eastern Paraguay formed a clade with ALP/LN/RM, but its M-segment clustered with Pergamino and Maciel, suggesting a possible reassortment. AAI was found in areas experiencing rapid land cover fragmentation and change within the Interior Atlantic Forest. IP37/38 did not show any strong association with any of the known hantavirus strains.