On the influenza vaccination policy through mathematical modeling.

OBJECTIVES: Aimed at mitigating influenza transmission, this study assessed the timing of the vaccination program and took vaccine capacity, strain mismatch and priority group into consideration. METHODS: An age-structured dynamic transmission model was fitted to the laboratory data of the national influenza surveillance system to reconstruct a baseline scenario with which the vaccination scenarios of interest could be compared. Outcome measures were defined as the impacts on the seasonal epidemic: decompression of the epidemic peak, reduction of the epidemic burden and change of the epidemic peak time. RESULTS: It was found that vaccine capacity building, although indispensable, could not guarantee substantial impact on the seasonal influenza epidemic. Vaccine mismatch might greatly offset vaccine capacity building. Notably, advance vaccine distribution could compensate for some vaccine underperformance. In the case of a well-matched vaccine, advance vaccine distribution could even exploit its utility. CONCLUSIONS: This study indicated that timely vaccine distribution should be put high on the agenda of seasonal influenza control policies. It provided a tangible platform for the policymakers to evaluate health policy impacts and to enhance risk communication with the public through mathematical modeling.

Data-driven interdisciplinary mathematical modelling quantitatively unveils competition dynamics of co-circulating influenza strains.

BACKGROUND: Co-circulation of influenza strains is common to seasonal epidemics and pandemic emergence. Competition was considered involved in the vicissitudes of co-circulating influenza strains but never quantitatively studied at the human population level. The main purpose of the study was to explore the competition dynamics of co-circulating influenza strains in a quantitative way. METHODS: We constructed a heterogeneous dynamic transmission model and ran the model to fit the weekly A/H1N1 influenza virus isolation rate through an influenza season. The construction process started on the 2007-2008 single-clade influenza season and, with the contribution from the clade-based A/H1N1 epidemiological curves, advanced to the 2008-2009 two-clade influenza season. Pearson method was used to estimate the correlation coefficient between the simulated epidemic curve and the observed weekly A/H1N1 influenza virus isolation rate curve. RESULTS: The model found the potentially best-fit simulation with correlation coefficient up to 96% and all the successful simulations converging to the best-fit. The annual effective reproductive number of each co-circulating influenza strain was estimated. We found that, during the 2008-2009 influenza season, the annual effective reproductive number of the succeeding A/H1N1 clade 2B-2, carrying H275Y mutation in the neuraminidase, was estimated around 1.65. As to the preceding A/H1N1 clade 2C-2, the annual effective reproductive number would originally be equivalent to 1.65 but finally took on around 0.75 after the emergence of clade 2B-2. The model reported that clade 2B-2 outcompeted for the 2008-2009 influenza season mainly because clade 2C-2 suffered from a reduction of transmission fitness of around 71% on encountering the former. CONCLUSIONS: We conclude that interdisciplinary data-driven mathematical modelling could bring to light the transmission dynamics of the A/H1N1 H275Y strains during the 2007-2009 influenza seasons worldwide and may inspire us to tackle the continually emerging drug-resistant A/H1N1pdm09 strains. Furthermore, we provide a prospective approach through mathematical modelling to solving a seemingly unintelligible problem at the human population level and look forward to its application at molecular level through bridging the resolution capacities of related disciplines.

Application of classification tree and logistic regression for the management and health intervention plans in a community-based study.

BACKGROUND: A community-based aboriginal study was conducted and analysed to explore the application of classification tree and logistic regression. METHODS: A total of 1066 aboriginal residents in Yilan County were screened during 2003-2004. The independent variables include demographic characteristics, physical examinations, geographic location, health behaviours, dietary habits and family hereditary diseases history. Risk factors of cardiovascular diseases were selected as the dependent variables in further analysis. RESULTS: The completion rate for heath interview is 88.9%. The classification tree results find that if body mass index is higher than 25.72 kg m(-2) and the age is above 51 years, the predicted probability for number of cardiovascular risk factors > or =3 is 73.6% and the population is 322. If body mass index is higher than 26.35 kg m(-2) and geographical latitude of the village is lower than 24 degrees 22.8', the predicted probability for number of cardiovascular risk factors > or =4 is 60.8% and the population is 74. As the logistic regression results indicate that body mass index, drinking habit and menopause are the top three significant independent variables. CONCLUSIONS: The classification tree model specifically shows the discrimination paths and interactions between the risk groups. The logistic regression model presents and analyses the statistical independent factors of cardiovascular risks. Applying both models to specific situations will provide a different angle for the design and management of future health intervention plans after community-based study.