METHODS AND FINDINGS: As a key step to estimate the economic and disease burden of dengue in Southeast Asia (SEA), we projected dengue cases from 2001 through 2010 using EFs. We conducted a systematic literature review (1995-2011) and identified 11 published articles reporting original, empirically derived EFs or the necessary data, and 11 additional relevant studies. To estimate EFs for total cases in countries where no empirical studies were available, we extrapolated data based on the statistically significant inverse relationship between an index of a country's health system quality and its observed reporting rate. We compiled an average 386,000 dengue episodes reported annually to surveillance systems in the region, and projected about 2.92 million dengue episodes. We conducted a probabilistic sensitivity analysis, simultaneously varying the most important parameters in 20,000 Monte Carlo simulations, and derived 95% certainty level of 2.73-3.38 million dengue episodes. We estimated an overall EF in SEA of 7.6 (95% certainty level: 7.0-8.8) dengue cases for every case reported, with an EF range of 3.8 for Malaysia to 19.0 in East Timor.
CONCLUSION: Studies that make no adjustment for underreporting would seriously understate the burden and cost of dengue in SEA and elsewhere. As the sites of the empirical studies we identified were not randomly chosen, the exact extent of underreporting remains uncertain. Nevertheless, the results reported here, based on a systematic analysis of the available literature, show general consistency and provide a reasonable empirical basis to adjust for underreporting.
OBJECTIVE: This study is aimed at determining the impact of sociological and environmental factors contributing to dengue cases.
METHODS: The study surveyed 379 respondents with dengue history. The socio-environmental factors were evaluated by chi-square and binary regression.
RESULT: The chi-square results revealed sociological factors associated between family with dengue experience such as older age (p =0.012), fewer than four people in the household (p= 0.008), working people (p= 0.004) and apartment/terrace houses (p=0.023). Similarly, there is a significant association between respondent's dengue history and houses that are shaded with vegetation (p= 0.012) and the present of public playground areas near the residential (p = 0.011).
CONCLUSION: The study identified socio-environmental factors that play an important role in the abundance of Aedes mosquitoes and also for the local dengue control measures.
METHODOLOGY/PRINCIPAL FINDINGS: The Shewhart method and Endemic Channel were used to identify alarm variables that may predict dengue outbreaks. Five country datasets were compiled by epidemiological week over the years 2007-2013. These data were split between the years 2007-2011 (historic period) and 2012-2013 (evaluation period). Associations between alarm/ outbreak variables were analysed using logistic regression during the historic period while alarm and outbreak signals were captured during the evaluation period. These signals were combined to form alarm/ outbreak periods, where 2 signals were equal to 1 period. Alarm periods were quantified and used to predict subsequent outbreak periods. Across Mexico and Dominican Republic, an increase in probable cases predicted outbreaks of hospitalised cases with sensitivities and positive predictive values (PPV) of 93%/ 83% and 97%/ 86% respectively, at a lag of 1-12 weeks. An increase in mean temperature ably predicted outbreaks of hospitalised cases in Mexico and Brazil, with sensitivities and PPVs of 79%/ 73% and 81%/ 46% respectively, also at a lag of 1-12 weeks. Mean age was predictive of hospitalised cases at sensitivities and PPVs of 72%/ 74% and 96%/ 45% in Mexico and Malaysia respectively, at a lag of 4-16 weeks.
CONCLUSIONS/SIGNIFICANCE: An increase in probable cases was predictive of outbreaks, while meteorological variables, particularly mean temperature, demonstrated predictive potential in some countries, but not all. While it is difficult to define uniform variables applicable in every country context, the use of probable cases and meteorological variables in tailored early warning systems could be used to highlight the occurrence of dengue outbreaks or indicate increased risk of dengue transmission.