Background
Dengue is the most common vector-borne viral infection. In recent times, an increase in the age of cases with clinical dengue has been reported in the national surveillance system and published literature of Vietnam. This change not only alter the risk of transmission and disease burden in different populations but also will impact for prevention and control strategies. A retrospective study was conducted from 2000 to 2015 in 19 provinces of southern Vietnam to describe the changes in age distribution of dengue cases and circulating serotypes.
Methodology/Principal findings
The study is a time trend analysis of the data aggregated from the database of dengue surveillance system. The database consisted of clinically diagnosed and laboratory-confirmed cases of dengue in southern Vietnam from 2000 to 2015. In the study period, the mean age of dengue cases increased from 12.2 ± 8.8 years old (y/o) to 16.8 ± 13.3 y/o between 2000 and 2015. Majority of severe cases were observed in the age group of 5–9 y/o and 10–14 y/o. Overall, the mortality and case fatality rates (CFR) were lowest during 2010 to 2015, and all four serotypes of dengue were observed.
Conclusions/Significance
With the exception of severe form, the age distribution of clinical cases of dengue appears to be shifting towards older age groups. An increase in the mean age of clinical cases of dengue has been observed in southern Vietnam over the past decade, and the highest incidence was observed in age group of 5–14 y/o. All serotypes of dengue were in circulation.
In disease information retrieval, usually, users only know a top of basic information about the disease such as headache and high fever symptoms. Most search engines rely on keyword search and do not return exact results because they only literally find the records matching with the keywords of the queries. In this work, we propose a novel interactive search for disease information by using data mining-based ontology. In particular, a human disease ontology is used as a knowledge base to semantically recognize the input for the search engine. The association rule is applied to generate associated relations among keywords (including symptoms, derives from, located in …) for interactive query refinement. A Bayesian-based ranking algorithm is also proposed to arrange the search result. Prospectively, our approach is valuable not only to increase the accuracy of searching human disease, but also provide a significant approach in data mining ontology.
ARTICLE HISTORY
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.