BackgroundArthropod-borne viruses (arboviruses) are among the most common agents of human febrile illness worldwide and the most important emerging pathogens, causing multiple notable epidemics of human disease over recent decades. Despite the public health relevance, little is know about the geographic distribution, relative impact, and risk factors for arbovirus infection in many regions of the world. Our objectives were to describe the arboviruses associated with acute undifferentiated febrile illness in participating clinics in four countries in South America and to provide detailed epidemiological analysis of arbovirus infection in Iquitos, Peru, where more extensive monitoring was conducted.Methodology/FindingsA clinic-based syndromic surveillance system was implemented in 13 locations in Ecuador, Peru, Bolivia, and Paraguay. Serum samples and demographic information were collected from febrile participants reporting to local health clinics or hospitals. Acute-phase sera were tested for viral infection by immunofluorescence assay or RT-PCR, while acute- and convalescent-phase sera were tested for pathogen-specific IgM by ELISA. Between May 2000 and December 2007, 20,880 participants were included in the study, with evidence for recent arbovirus infection detected for 6,793 (32.5%). Dengue viruses (Flavivirus) were the most common arbovirus infections, totaling 26.0% of febrile episodes, with DENV-3 as the most common serotype. Alphavirus (Venezuelan equine encephalitis virus [VEEV] and Mayaro virus [MAYV]) and Orthobunyavirus (Oropouche virus [OROV], Group C viruses, and Guaroa virus) infections were both observed in approximately 3% of febrile episodes. In Iquitos, risk factors for VEEV and MAYV infection included being male and reporting to a rural (vs urban) clinic. In contrast, OROV infection was similar between sexes and type of clinic.Conclusions/SignificanceOur data provide a better understanding of the geographic range of arboviruses in South America and highlight the diversity of pathogens in circulation. These arboviruses are currently significant causes of human illness in endemic regions but also have potential for further expansion. Our data provide a basis for analyzing changes in their ecology and epidemiology.
Metric space searching is an emerging technique to address the problem of efficient similarity searching in many applications, including multimedia databases and other repositories handling complex objects. Although promising, the metric space approach is still immature in several aspects that are well established in traditional databases. In particular, most indexing schemes are static, that is, few of them tolerate insertion or deletion of elements at reasonable cost over an existing index. The spatial approximation tree (sa-tree) has been experimentally shown to provide a good tradeoff between construction cost, search cost, and space requirement. However, the sa-tree is static, which renders it unsuitable for many database applications. In this paper, we study different methods to handle insertions and deletions on the sa-tree at low cost. In many cases, the dynamic construction (by successive insertions) is even faster than the previous static construction, and both are similar elsewhere. In addition, the dynamic version significantly improves the search performance of sa-trees in virtually all cases. The result is a much more practical data structure that can be useful in a wide range of database applications.
Similarity search has been proved suitable for searching in large collections of unstructured data objects. A number of practical index data structures for this purpose have been proposed. All of them have been devised to process single queries sequentially. However, in large-scale systems such as Web Search Engines indexing multi-media content, it is critical to deal efficiently with streams of queries rather than with single queries. In this paper we show how to achieve efficient and scalable performance in this context. To this end we transform a sequential index based on clustering into a distributed one and devise algorithms and optimizations specially tailored to support high-performance parallel query processing.
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