Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

A new battery modelling method based on simulation error minimization

A new battery modelling method is presented based on the simulation error minimization criterion rather than the conventional prediction error criterion. A new integrated optimization method to optimize the model parameters is proposed. This new method is validated on a set of Li ion battery test data, and the results confirm the advantages of the proposed method in terms of the model generalization performance and long-term prediction accuracy.

Distribution and visual impact of postoperative refractive error after cataract surgery in rural China: study of cataract outcomes and up-take of services report 4.

PURPOSE: To model the possible impact of using average-power intraocular lenses (IOLs) and evaluate the postoperative refractive error in patients having cataract surgery in rural China.SETTING: Rural Guangdong, China.METHODS: Patients having cataract surgery by local surgeons were examined and visual function was assessed 10 to 14 months after surgery. Subjective refraction at near and distance was performed bilaterally by an ophthalmologist. Patients had a target refraction of -0.50 diopter (D) based on ocular biometry.RESULTS: Of the 313 eligible patients, 242 (77%) could be contacted and 176 (74% of contacted patients, 56% overall) were examined. Examined patients had a mean age of 69.4 +/- 10.5 years. Of the 211 operated eyes, 73.2% were within +/-1.0 D of the target refraction after surgery. The best presenting distance vision was in patients within +/-1.0 D of plano and the best presenting near vision, in those with mild myopia (<-1.0 D to > or =2.0 D) (P= .005). However, patients with hyperopia (>+1.0 D) reported significantly better adjusted visual function than those with emmetropia or myopia (<-1.0 D). When the predicted use of an average-power IOL (median +21.5 D) was modeled, predicted visual acuity was significantly reduced (P= .001); however, predicted visual function was not significantly altered (P>.3).CONCLUSIONS: Accurate selection of postoperative refractive error was achieved by local surgeons in this rural area. Based on visual function results, aiming for mild postoperative myopia may not be suitable in this setting. Implanting average-power IOLs significantly reduced postoperative presenting vision, but not visual function.

Validating the accuracy of a model to predict the onset of myopia in children.

PURPOSE: To assess the sensitivity and specificity of models predicting myopia onset among ethnically Chinese children. METHODS: Visual acuity, height, weight, biometry (A-scan, keratometry), and refractive error were assessed at baseline and 3 years later using the same equipment and protocol in primary schools in Xiamen (China) and Singapore. A regression model predicting the onset of myopia < -0.75 diopters (D) after 3 years in either eye among Xiamen children was validated with Singapore data. RESULTS: Baseline data were collected from 236 Xiamen children (mean age, 7.82 ± 0.63 years) and from 1979 predominantly Chinese children in Singapore (7.83 ± 0.84 years). Singapore children were significantly taller and heavier, and had more myopia (31.4% vs. 6.36% < -0.75 D in either eye, P < 0.001) and longer mean axial length. Three-year follow-up was available for 80.0% of Xiamen children and 83.1% in Singapore. For Xiamen, the area under the receiver-operator curve (AUC) in a model including ocular biometry, height, weight, and presenting visual acuity was 0.974 (95% confidence interval [CI], 0.945-0.997). In Singapore, the same model achieved sensitivity, specificity, and positive predictive value of 0.844, 0.650, and 0.669, with an AUC of 0.815 (95% CI, 0.791-0.839). CONCLUSIONS: Accuracy in predicting myopia onset based on simple measurements may be sufficient to make targeted early intervention practical in settings such as Singapore with high myopia prevalence. Models based on cohorts with a greater prevalence of high myopia than that in Xiamen could be used to assess accuracy of models predicting more severe forms of myopia.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.