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Background: Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named "Sentinel plus" which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. Methods: Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. Results: The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. Conclusions: This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis. [ABSTRACT FROM AUTHOR]

• A hybrid model is proposed based on the combination of SVR and Heun's methods. • The hybrid model forecasts flood maps and dike risks with multiple lead times. • The hybrid model outperforms conventional SVR models in long-term forecasting. • The hybrid model is much more efficient than hydraulic models. In response to the increasing threats of flash flooding, how to generate flood maps in real time has drawn great attention. In this study, a hybrid ML model was developed for flood map generation with multiple lead times based on the combination of the support vector regression (SVR) method and Heun's scheme for numerical correction, in which the hydraulic simulation results are used for model training while rainfall and flood sensor data are used as input variables (namely, the SVR-RFN model). To evaluate the performance of the hybrid model, two additional SVR models were developed and used as references for comparison—the first one used rainfall as input variables without sensor data (namely, the SVR-R model), and the second one added flood sensor data as input variables without numerical correction (namely, the SVR-RF model). The three models are tested and compared with a hydraulic model for a historical event in Sanyei River Basin, Taiwan. Using the hydraulic model as the benchmark, the hybrid model performed the best by reducing 63% of flood depth error and had the least deterioration of accuracy as lead time increased. The SVR-RF model performed the second by reducing the flood depth errors by 46% whereas the hydraulic model was more accurate than the SVR-R model. In a comparison of flood extents, the hydraulic model showed overestimation, the SVR-R model showed significant underestimation, whereas the two models with sensor data had balanced predictions. The flood maps generated by the hybrid model were applied for dike safety evaluation and showed a good capability in forecasting the risk of dike breach. [ABSTRACT FROM AUTHOR]