Studies indicate that asymptomatic infections do indeed occur frequently for both seasonal and pandemic influenza, accounting for about one-third of influenza infections. Studies carried out during the 2009 pH1N1 pandemic have found significant antibody response against seasonal H1N1 and H3N2 vaccine strains in schoolchildren receiving only pandemic H1N1 monovalent vaccine, yet reported either no symptoms or only mild symptoms.

Although it has been suggested that schoolchildren vaccination reduces influenza morbidity and mortality in the community, it is unknown whether geographical heterogeneity would affect vaccine effectiveness.

Despite the success of genome-wide association studies (GWASs) in detecting common variants (minor allele frequency ≥0.05) many suggested that rare variants also contribute to the genetic architecture of diseases. Recently, researchers demonstrated that rare variants can show a strong stratification which may not be corrected by using existing methods. In this paper, we focus on a case-parents study and consider methods for testing group-wise association between multiple rare (and common) variants in a gene region and a disease. All tests depend on the numbers of transmitted mutant alleles from parents to their diseased children across variants and hence they are robust to the effect of population stratification. We use extensive simulation studies to compare the performance of four competing tests: the largest single-variant transmission disequilibrium test (TDT), multivariable test, combined TDT, and a likelihood ratio test based on a random-effects model. We find that the likelihood ratio test is most powerful in a wide range of settings and there is no negative impact to its power performance when common variants are also included in the analysis. If deleterious and protective variants are simultaneously analyzed, the likelihood ratio test was generally insensitive to the effect directionality, unless the effects are extremely inconsistent in one direction.

Accurately estimating the morbidity of individuals with specific conditions is crucial for healthcare systems to enhance healthcare delivery. In this study, we introduce DKABio-clusters and elucidate their characteristics, demonstrating their significant implications for healthcare management. The key applications of DKABio-clusters include the development of a health score (DKABio-HS) and risk prediction. The health score enables the calculation of a comprehensive "disease-related" score that summarizes an individual's health status into a single value. Our findings reveal that the health score exhibits high consistency, as indicated by minimal differences in health scores between the training and validation samples (mean absolute percentage errors within 0 to 10 years were less than 0.1%, with all mean absolute percentage errors ranging between 1.2-1.6%). A higher health score signifies better health or lower disease risk, with the health score decreasing with age or the presence of multiple diseases. By utilizing the health score, we can establish a classification rule known as the "disease map," enabling quantitative differentiation of individuals at various non-diseased or diseased levels. Consequently, non-disease individuals can be categorized as healthy or sub-healthy, allowing tailored health management approaches for preventive measures. Our results indicate that individuals classified as sub-healthy exhibit significantly higher disease risks compared to healthy individuals (Female (male) 5-year risks of developing at least one disease are 29% vs. 15% (29% vs. 16.5%)). Additionally, by employing a selected set of health variables, we can estimate the distribution of DKABio-clusters and simultaneously predict the 10-year risks of 15 diseases/conditions. We validate the predictive power of our model by comparing the predicted risks with true risks derived from the training and validation samples. In the majority of cases, the differences between the true and predicted risks were not statistically significant. All study findings were based on data from the National Health Insurance Research Database released by the National Health Research Institute, Taiwan, and the Mei Jau Health Management Institution database, collected between 2000 and 2016 in Taiwan.