Comparing the Powers of the Wald-Wolfowitz and Kolmogorov-Smirnov Tests

The Wald-Wolfowitz test and the Kolmogorov-Smirnov test are two well-known tests which can be used to test for differences between two population distributions, where these distributions could differ in means, variances or shapes. This paper compares the simulated power of the Wald-Wolfowitz test to that of the Kolmogorov-Smirnov test in several situations. Differences in location only, differences in variance only, and differences in both location and variance are considered. In most cases, equal sample sizes of 10 and 20 are used.     

Approaches to Refining Estimates of Global Burden and Economics of Dengue

Dengue presents a formidable and growing global economic and disease burden, with around half the world''s population estimated to be at risk of infection. There is wide variation and substantial uncertainty in current estimates of dengue disease burden and, consequently, on economic burden estimates. Dengue disease varies across time, geography and persons affected. Variations in the transmission of four different viruses and interactions among vector density and host''s immune status, age, pre-existing medical conditions, all contribute to the disease''s complexity. This systematic review aims to identify and examine estimates of dengue disease burden and costs, discuss major sources of uncertainty, and suggest next steps to improve estimates. Economic analysis of dengue is mainly concerned with costs of illness, particularly in estimating total episodes of symptomatic dengue. However, national dengue disease reporting systems show a great diversity in design and implementation, hindering accurate global estimates of dengue episodes and country comparisons. A combination of immediate, short-, and long-term strategies could substantially improve estimates of disease and, consequently, of economic burden of dengue. Suggestions for immediate implementation include refining analysis of currently available data to adjust reported episodes and expanding data collection in empirical studies, such as documenting the number of ambulatory visits before and after hospitalization and including breakdowns by age. Short-term recommendations include merging multiple data sources, such as cohort and surveillance data to evaluate the accuracy of reporting rates (by health sector, treatment, severity, etc.), and using covariates to extrapolate dengue incidence to locations with no or limited reporting. Long-term efforts aim at strengthening capacity to document dengue transmission using serological methods to systematically analyze and relate to epidemiologic data. As promising tools for diagnosis, vaccination, vector control, and treatment are being developed, these recommended steps should improve objective, systematic measures of dengue burden to strengthen health policy decisions.