Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Hantaviruses more specifically cause thousands of human disease cases annually worldwide, while understanding and predicting human hantavirus epidemics pose numerous unsolved challenges. Nephropathia epidemica (NE) is a human infection caused by Puumala virus, which is naturally carried and shed by bank voles (Myodes glareolus). The objective of this study was to develop a method that allows model-based predicting 3 months ahead of the occurrence of NE epidemics. Two data sets were utilized to develop and test the models. These data sets were concerned with NE cases in Finland and Belgium. In this study, we selected the most relevant inputs from all the available data for use in a dynamic linear regression (DLR) model. The number of NE cases in Finland were modelled using data from 1996 to 2008. The NE cases were predicted based on the time series data of average monthly air temperature (°C) and bank voles' trapping index using a DLR model. The bank voles' trapping index data were interpolated using a related dynamic harmonic regression model (DHR). Here, the DLR and DHR models used time-varying parameters. Both the DHR and DLR models were based on a unified state-space estimation framework. For the Belgium case, no time series of the bank voles' population dynamics were available. Several studies, however, have suggested that the population of bank voles is related to the variation in seed production of beech and oak trees in Northern Europe. Therefore, the NE occurrence pattern in Belgium was predicted based on a DLR model by using remotely sensed phenology parameters of broad-leaved forests, together with the oak and beech seed categories and average monthly air temperature (°C) using data from 2001 to 2009. Our results suggest that even without any knowledge about hantavirus dynamics in the host population, the time variation in NE outbreaks in Finland could be predicted 3 months ahead with a 34% mean relative prediction error (MRPE). This took into account solely the population dynamics of the carrier species (bank voles). The time series analysis also revealed that climate change, as represented by the vegetation index, changes in forest phenology derived from satellite images and directly measured air temperature, may affect the mechanics of NE transmission. NE outbreaks in Belgium were predicted 3 months ahead with a 40% MRPE, based only on the climatological and vegetation data, in this case, without any knowledge of the bank vole's population dynamics. In this research, we demonstrated that NE outbreaks can be predicted using climate and vegetation data or the bank vole's population dynamics, by using dynamic data-based models with time-varying parameters. Such a predictive modelling approach might be used as a step towards the development of new tools for the prevention of future NE outbreaks.
The bank vole (Myodes glareolus) is the natural host of Puumala virus (PUUV) in vast areas of Europe. PUUV is one of the hantaviruses which are transmitted to humans by infected rodents. PUUV causes a general mild form of hemorrhagic fever with renal syndrome (HFRS) called nephropathia epidemica (NE). Vector-borne and zoonotic diseases generally display clear spatial patterns due to different space-dependent factors. Land cover influences disease transmission by controlling both the spatial distribution of vectors or hosts, as well as by facilitating the human contact with them. In this study the use of ecological niche modelling (ENM) for predicting the geographical distribution of bank vole population on the basis of spatial climate information is tested. The Genetic Algorithm for Rule-set Prediction (GARP) is used to model the ecological niche of bank voles in Western Europe. The meteorological data, land cover types and geo-referenced points representing the locations of the bank voles (latitude/longitude) in the study area are used as the primary model input value. The predictive accuracy of the bank vole ecologic niche model was significant (training accuracy of 86%). The output of the GARP models based on the 50% subsets of points used for testing the model showed an accuracy of 75%. Compared with random models, the probability of such high predictivity was low (χ2 tests, p < 10−6). As such, the GARP models were predictive and the used ecologic niche model indeed indicates the ecologic requirements of bank voles. This approach successfully identified the areas of infection risk across the study area. The result suggests that the niche modelling approach can be implemented in a next step towards the development of new tools for monitoring the bank vole’s population.
Serial virus specimens rescued from rabbits, experimentally infected with bovine immunodeficiency (BIV) strain R29, were monitored for changes in quasispecies population, using the single-strand conformation polymorphism (SSCP) analysis. The generation of characteristic SSCP patterns enables the rapid differentiation of BIV variants derived from the conserved part on the env region of the BIV genome, reducing the need for expensive and time-consuming direct sequencing analyses. Our results showed genetic polymorphism among a number of sampled BIV population in experimentally infected rabbits. At least three SSCP patterns (BIV quasispecies) were detected. The SSCP analysis allows for an easy, sensitive, and rapid screening of genetic variants of the virus and the assessment of variation at a number of tissue target sites. These variations may relate to cell-type targets and/or disease progression, and could be significant to our understanding of lentiviral pathogenesis.
Abstract. Several species of alphaviruses have been previously described in the Americas, some of which are associated with encephalitis and others are associated with arthralgia. Venezuelan equine encephalitis virus (VEEV) and eastern equine encephalitis virus (EEEV) are endemic to Venezuela, with the former being responsible for major outbreaks of severe and often fatal disease in animals and humans. The aim of this study was to analyze the genetic diversity of Venezuelan alphaviruses isolated during two decades of surveillance in northern Venezuela. Phylogenetic analysis indicated the circulation of a VEEV subtype IAB strain 8 years after the last reported outbreak. Thirteen strains within two subclades of South American lineage III of EEEV were also found in Venezuela. Considerable genetic variability was observed among Venezuelan Una virus strains, which were widely distributed among the clades. The first Venezuelan Mayaro sequence was also characterized.
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