Ka Chun Chong

Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus

BackgroundDuring the early stages of a new influenza pandemic, travel restriction is an immediate and non-pharmaceutical means of retarding incidence growth. It extends the time frame of effective mitigation, especially when the characteristics of the emerging virus are unknown. In the present study, we used the 2009 influenza A pandemic as a case study to evaluate the impact of regulating air, sea, and land transport. Other government strategies, namely, antivirals and hospitalizations, were also evaluated.MethodsHong Kong arrivals from 44 countries via air, sea, and land transports were imported into a discrete stochastic Susceptible, Exposed, Infectious and Recovered (SEIR) host-flow model. The model allowed a number of latent and infectious cases to pass the border, which constitutes a source of local disease transmission. We also modeled antiviral and hospitalization prevention strategies to compare the effectiveness of these control measures. Baseline reproduction rate was estimated from routine surveillance data.ResultsRegarding air travel, the main route connected to the influenza source area should be targeted for travel restrictions; imposing a 99% air travel restriction delayed the epidemic peak by up to two weeks. Once the pandemic was established in China, the strong land connection between Hong Kong and China rendered Hong Kong vulnerable. Antivirals and hospitalization were found to be more effective on attack rate reductions than travel restrictions. Combined strategies (with 99% restriction on all transport modes) deferred the peak for long enough to establish a vaccination program.ConclusionThe findings will assist policy-makers with decisions on handling similar future pandemics. We also suggest regulating the extent of restriction and the transport mode, once restriction has been deemed necessary for pandemic control. Although travel restrictions have yet to gain social acceptance, they allow time for mitigation response when a new and highly intrusive virus emerges.

Identifying Meteorological Drivers for the Seasonal Variations of Influenza Infections in a Subtropical City — Hong Kong

Compared with temperate areas, the understanding of seasonal variations of influenza infections is lacking in subtropical and tropical regions. Insufficient information about viral activity increases the difficulty of forecasting the disease burden and thus hampers official preparation efforts. Here we identified potential meteorological factors that drove the seasonal variations in influenza infections in a subtropical city, Hong Kong. We fitted the meteorological data and influenza mortality data from 2002 to 2009 in a Susceptible-Infected-Recovered model. From the results, air temperature was a common significant driver of seasonal patterns and cold temperature was associated with an increase in transmission intensity for most of the influenza epidemics. Except 2004, the fitted models with significant meteorological factors could account for more than 10% of the variance in additional to the null model. Rainfall was also found to be a significant driver of seasonal influenza, although results were less robust. The identified meteorological indicators could alert officials to take appropriate control measures for influenza epidemics, such as enhancing vaccination activities before cold seasons. Further studies are required to fully justify the associations.

Interpreting the transmissibility of the avian influenza A(H7N9) infection from 2013 to 2015 in Zhejiang Province, China

SUMMARY Three epidemic waves of human influenza A(H7N9) were documented in several different provinces in China between 2013 and 2015. With limited understanding of the potential for human-to-human transmission, it was difficult to implement control measures efficiently or to inform the public adequately about the application of interventions. In this study, the human-to-human transmission rate for the epidemics that occurred between 2013 and 2015 in Zhejiang Province, China, was analysed. The reproduction number (R), a key indicator of transmission intensity, was estimated by fitting the number of infections from poultry to humans and from humans to humans into a mathematical model. The posterior mean R for human-to-human transmission was estimated to be 0·27, with a 95% credible interval of 0·14–0·44 for the first wave, whereas the posterior mean Rs decreased to 0·15 in the second and third waves. Overall, these estimates indicate that a human H7N9 pandemic is unlikely to occur in Zhejiang. The reductions in the viral transmissibility and the number of poultry-transmitted infections after the first epidemic may be attributable to the various intervention measures taken, including changes in the extent of closures of live poultry markets.

Trajectories of frailty among Chinese older people in Hong Kong between 2001 and 2012: an age-period-cohort analysis

Background there is little evidence to suggest that older people today are living in better health than their predecessors did at the same age. Only a few studies have evaluated whether there are birth cohort effects on frailty, an indicator of health in older people, encompassing physical, functional and mental health dimensions. Objectives this study examined longitudinal trajectories of frailty among Chinese older people in Hong Kong. Methods this study utilised data from the 18 Elderly Health Centres of the Department of Health comprising a total of 417,949 observations from 94,550 community-dwelling Chinese people aged ≥65 years in one early birth cohort (1901-23) and four later birth cohorts (1924-29, 1930-35, 1936-41, 1942-47) collected between 2001 and 2012, to examine trajectories of the frailty index and how birth cohorts may have contributed to the trends using an age-period-cohort analysis. Results more recent cohorts had higher levels of frailty than did earlier cohorts at the same age, controlling for period, gender, marital status, educational levels, socioeconomic status, lifestyle and social factors. Older age, being female, widowhood, lower education and smoking were associated with higher levels of frailty. Conclusion more recent cohorts had higher levels of frailty than did earlier cohorts. Frailty interventions, coupled with early detection, should be developed to combat the increasing rates of frailty in Hong Kong Chinese.

Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data

Genetic data consists of a wide range of marker types, including common, low‐frequency, and rare variants. Multiple genetic markers and their interactions play central roles in the heritability of complex disease. In this study, we propose an algorithm that uses a stratified variable selection design by genetic architectures and interaction effects, achieved by a dataset‐adaptive W‐test. The polygenic sets in all strata were integrated to form a classification rule. The algorithm was applied to the Critical Assessment of Genome Interpretation 4 bipolar challenge sequencing data. The prediction accuracy was 60% using genetic markers on an independent test set. We found that epistasis among common genetic variants contributed most substantially to prediction precision. However, the sample size was not large enough to draw conclusions for the lack of predictability of low‐frequency variants and their epistasis.

An increasing trend of rural infections of human influenza A (H7N9) from 2013 to 2017: A retrospective analysis of patient exposure histories in Zhejiang province, China

Background Although investigations have shown that closing live poultry markets (LPMs) is highly effective in controlling human influenza A (H7N9) infections, many of the urban LPMs were shut down, but rural LPMs remained open. This study aimed to compare the proportional changes between urban and rural infections in the Zhejiang province from 2013 to 2017 by analyzing the exposure histories of human cases. Methods All laboratory-confirmed cases of H7N9 from 2013 (the first wave) to 2017 (the fifth wave) in the Zhejiang province of China were analyzed. Urban and rural infections were defined based on the locations of poultry exposure (direct and indirect) in urban areas (central towns) and rural areas (towns and villages on the outskirts of cities). A Chi-square trend test was used to compare the proportional trend between urban and rural infections over time and logistic regression was used to obtain the odds ratio by years. Results From 2013 to 2017, a statistically significant trend in rural infections was observed (p <0.01). The incremental odds ratio by years of rural infections was 1.59 with 95% confidence intervals of 1.34 to 1.86. Each year, significant increases in the proportion of live poultry transactions in LPMS and poultry processing plants were detected in conjunction with an increased proportion of urban and rural infections. Conclusion The empirical evidence indicated a need for heightened infection control measures in rural areas, such as serving rural farms and backyards as active surveillance points for the H7N9 virus. Other potential interventions such as the vaccination of poultry and extending the closure of LPMs to the provincial level require further careful investigations.

Gene-methylation epistatic analyses via the W-test identifies enriched signals of neuronal genes in patients undergoing lipid-control treatment

An increasing number of studies are focused on the epigenetic regulation of DNA to affect gene expression without modifications to the DNA sequence. Methylation plays an important role in shaping disease traits; however, previous studies were mainly experiment, based, resulting in few reports that measured gene–methylation interaction effects via statistical means. In this study, we applied the data set adaptive W-test to measure gene–methylation interactions. Performance was evaluated by the ability to detect a given set of causal markers in the data set obtained from the GAW20. Results from simulation data analyses showed that the W-test was able to detect most markers. The method was also applied to chromosome 11 of the experimental data set and identified clusters of genes with neuronal and retinal functions, including MPPED2I, GUCY2E, NAV2, and ZBTB16. Genes from the TRIM family were also identified; these genes are potentially related to the regulation of triglyceride levels. Our results suggest that the W-test could be an efficient and effective method to detect gene–methylation interactions. Furthermore, the identified genes suggest an interesting relationship between lipid levels and the etiology of neurological disorders.

Incorporating methylation genome information improves prediction accuracy for drug treatment responses

BackgroundAn accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data into models for disease prediction. However, it is still a challenge to integrate different types of omics data into prediction models, and the contribution of methylation information to prediction remains to be fully clarified.ResultsA stratified drug-response prediction model was built based on an artificial neural network to predict the change in the circulating triglyceride level after fenofibrate intervention. Associated single-nucleotide polymorphisms (SNPs), methylation of selected cytosine-phosphate-guanine (CpG) sites, age, sex, and smoking status, were included as predictors. The model with selected SNPs achieved a mean 5-fold cross-validation prediction error rate of 43.65%. After adding methylation information into the model, the error rate dropped to 41.92%. The combination of significant SNPs, CpG sites, age, sex, and smoking status, achieved the lowest prediction error rate of 41.54%.ConclusionsCompared to using SNP data only, adding methylation data in prediction models slightly improved the error rate; further prediction error reduction is achieved by a combination of genome, methylation genome, and environmental factors.

Estimation of clinical parameters of chronic kidney disease by exhaled breath full-scan mass spectrometry data and iterative PCA with intensity screening algorithm

Breath mass spectrometry is a useful tool for identifying important compounds associated with health. However, there have been few studies that have explored human exhaled breath by full-scan mass spectrometry as a non-invasive method for medical diagnosis, which may be attributed to the difficulties resulting from multicollinearity and small sample sizes relative to a large number of product ions. In this study, breath samples from 54 chronic kidney disease patients were analyzed by selected ion flow tube mass spectrometry in the full-scan mode. With the signal intensities of product ions, we developed a novel and robust algorithm, iterative PCA with intensity screening (IPS), to build linear models for estimating important clinical parameters of chronic kidney disease. It has been shown that IPS provided good estimations in cross-validated samples, and furthermore the identified product ions could have direct medical relevance to the disease. The study demonstrated the potential of quantitative breath analysis using mass spectrometry for medical diagnosis, and the importance of applying appropriate statistical tools to unveil the rich information in this type of data.

A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests

Motivation: Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non‐functional variants in an arbitrary genomic region could impair the testing power. Results: We propose a Zoom‐Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants’ tests, including the SKAT, SKAT‐O, burden test and the W‐test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene‐based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data. Availability and Implementation: The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html Contact: maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk Supplementary information: Supplementary data are available at Bioinformatics online.