A STUDY OF A PARAMETRIC YIELD OPTIMIZATION ALGORITHM

Master'sMASTER OF ENGINEERIN

Team 7: Topological Analysis of Infrastructure Network

from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 6 Workshop 18Critical Infrastructures work together to produce goods and services. For example, the power station generates electricity and the water purification station uses the electricity to produce drinking water. Disruption of Civil Infrastructures will affect our national security, economic well being and way of life. This provides a primary motivation to model and understand the interaction between infrastructures. Based on our works in military modeling and simulation (M&S), we have extended these M&S methodologies to the area of Critical Infrastructure Protection. However, we observed that it take a reasonable amount of modeling effort to model a large network of infrastructures

Feasibility Analysis of a Prevention Programme for Patients with Early Chronic Disease using A Mathematical Model

10.1080/20476965.2020.1860654Health Systems11275-8

Predicting mortality in patients diagnosed with advanced dementia presenting at an acute care hospital: the PROgnostic Model for Advanced DEmentia (PRO-MADE)

Abstract Background Challenges in prognosticating patients diagnosed with advanced dementia (AD) hinders timely referrals to palliative care. We aim to develop and validate a prognostic model to predict one-year all-cause mortality (ACM) in patients with AD presenting at an acute care hospital. Methods This retrospective cohort study utilised administrative and clinical data from Tan Tock Seng Hospital (TTSH). Patients admitted to TTSH between 1st July 2016 and 31st October 2017 and identified to have AD were included. The primary outcome was ACM within one-year of AD diagnosis. Multivariable logistic regression was used. The PROgnostic Model for Advanced Dementia (PRO-MADE) was internally validated using a bootstrap resampling of 1000 replications and externally validated on a more recent cohort of AD patients. The model was evaluated for overall predictive accuracy (Nagelkerke’s R2 and Brier score), discriminative [area-under-the-curve (AUC)], and calibration [calibration slope and calibration-in-the-large (CITL)] properties. Results A total of 1,077 patients with a mean age of 85 (SD: 7.7) years old were included, and 318 (29.5%) patients died within one-year of AD diagnosis. Predictors of one-year ACM were age > 85 years (OR:1.87; 95%CI:1.36 to 2.56), male gender (OR:1.62; 95%CI:1.18 to 2.22), presence of pneumonia (OR:1.75; 95%CI:1.25 to 2.45), pressure ulcers (OR:2.60; 95%CI:1.57 to 4.31), dysphagia (OR:1.53; 95%CI:1.11 to 2.11), Charlson Comorbidity Index ≥ 8 (OR:1.39; 95%CI:1.01 to 1.90), functional dependency in ≥ 4 activities of daily living (OR: 1.82; 95%CI:1.32 to 2.53), abnormal urea (OR:2.16; 95%CI:1.58 to 2.95) and abnormal albumin (OR:3.68; 95%CI:2.07 to 6.54) values. Internal validation results for optimism-adjusted Nagelkerke’s R2, Brier score, AUC, calibration slope and CITL were 0.25 (95%CI:0.25 to 0.26), 0.17 (95%CI:0.17 to 0.17), 0.76 (95%CI:0.76 to 0.76), 0.95 (95% CI:0.95 to 0.96) and 0 (95%CI:-0.0001 to 0.001) respectively. When externally validated, the model demonstrated an AUC of 0.70 (95%CI:0.69 to 0.71), calibration slope of 0.64 (95%CI:0.63 to 0.66) and CITL of -0.27 (95%CI:-0.28 to -0.26). Conclusion The PRO-MADE attained good discrimination and calibration properties. Used synergistically with a clinician’s judgement, this model can identify AD patients who are at high-risk of one-year ACM to facilitate timely referrals to palliative care

Estimating transmission parameters for COVID-19 clusters by using symptom onset data, Singapore, January-April 2020

10.3201/eid2702.203018Emerging Infectious Diseases272582-58

Estimating transmission parameters for COVID-19 clusters by using symptom onset data, Singapore, January-April 2020

10.3201/eid2702.203018Emerging Infectious Diseases272582-58