BackgroundDengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults.Methods and FindingsA total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm3 or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever.ConclusionThis study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.
Data from longitudinal analyses can be useful in the design and implementation of control strategies.
Chikungunya fever swept across many South and South-east Asian countries, following extensive outbreaks in the Indian Ocean Islands in 2005. However, molecular epidemiological data to explain the recent spread and evolution of Chikungunya virus (CHIKV) in the Asian region are still limited. This study describes the genetic Characteristics and evolutionary relationships of CHIKV strains that emerged in Sri Lanka and Singapore during 2006-2008. The viruses isolated in Singapore also included those imported from the Maldives (n51), India (n52) and Malaysia (n531). All analysed strains belonged to the East, Central and South African (ECSA) lineage and were evolutionarily more related to Indian than to Indian Ocean Islands strains. Unique genetic characteristics revealed five genetically distinct subpopulations of CHIKV in Sri Lanka and Singapore, which were likely to have emerged through multiple, independent introductions. The evolutionary network based on E1 gene sequences indicated the acquisition of an alanine to valine 226 substitution (E1-A226V) by virus strains of the Indian sublineage as a key evolutionary event that contributed to the transmission and spatial distribution of CHIKV in the region. The E1-A226V substitution was found in 95.7 % (133/139) of analysed isolates in 2008, highlighting the widespread establishment of mutated CHIKV strains in Sri Lanka, Singapore and Malaysia. As the E1-A226V substitution is known to enhance the transmissibility of CHIKV by Aedes albopictus mosquitoes, this observation has important implications for the design of vector control strategies to fight the virus in regions at risk of chikungunya fever.
BackgroundDengue resurged in Singapore during 2013-14, causing an outbreak with unprecedented number of cases in the country. In the present study, we summarise the epidemiological, virological and entomological findings gathered through the dengue surveillance programme and highlight the drivers of the epidemic. We also describe how the surveillance system facilitated the preparedness to moderate epidemic transmission of dengue in the country.MethodsThe case surveillance was based on a mandatory notification system that requires all medical practitioners to report clinically-suspected and laboratory-confirmed cases within 24 hours. The circulating Dengue virus (DENV) populations were monitored through an island wide virus surveillance programme aimed at determining the serotypes and genotypes of circulating virus strains. Entomological surveillance included adult Aedes surveillance as well as premise checks for larval breeding.ResultsA switch in the dominant serotype from DENV-2 to DENV-1 in March 2013 signalled a potential spike in cases, and the alert was corroborated by an increase in average Aedes house index. The alert triggered preparedness and early response to moderate the impending outbreak. The two-year outbreak led to 22,170 cases in 2013 and 18,338 in 2014, corresponding to an incidence rate of 410.6 and 335.0 per 100,000 population, respectively. DENV-1 was the dominant serotype in 2013 (61.7 %, n = 5,071) and 2014 (79.2 %, n = 5,226), contributed largely by a newly-introduced DENV-1 genotype III strain. The percentage of houses with Ae. aegypti breeding increased significantly (p < 0.001) from 2012 (annual average of 0.07 %) to 2013 (annual average of 0.14 %), followed by a drop in 2014 (annual average of 0.10 %). Aedes breeding data further showed a wide spread distribution of Ae. aegypti in the country that corresponded with the dengue case distribution pattern in 2013 and 2014. The adult Aedes data from 34 gravitrap sentinel sites revealed that approximately 1/3 of the monitored sites remained at high risk of DENV transmission in 2013.ConclusionsThe culmination of the latest epidemic is likely to be due to a number of demographic, social, virological, entomological, immunological, climatic and ecological factors that contribute to DENV transmission. A multi-pronged approach backed by the epidemiological, virological and entomological understanding paved way to moderate the case burden through an integrated vector management approach.
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