Temporal Trends Analysis for Dengue Outbreak and Network Threats Severity Prediction Accuracy Improvement

Simulation of Dengue Outbreak Prediction

__ Neural Network Model (NNM), Hidden Markov Model (HMM) and Regression Model (RM) are developed to predict the spread of dengue outbreak in Malaysia. The case study covered dengue cases data from Selangor, which include seven mukims and eight administrative districts in year of 2004 and 2005. Specific criteria concerned are location, time (weeks) and intensity of dengue cases. Critical discuss...

Dengue Outbreak and Evaluation

4. OUTBREAK DETECTION 4.1. Type of surveillance  Passive or Active Surveillance  Comprehensive or Sentinel Surveillance  Syndromic or Laboratory-Based Surveillance  Indicator-Based or Event-Based Surveillance  Entomological Surveillance  Monitoring of Environmental and Social Risks 4.2. Surveillance procedures  Case-definition  Notification procedures 4.3. Type of signal  Alert signal ...

Dengue Outbreak and Evaluation

A Bayesian Network for Outbreak Detection and Prediction

Health care officials are increasingly concerned with knowing early whether an outbreak of a particular disease is unfolding. We often have daily counts of some variable that are indicative of the number of individuals in a given community becoming sick each day with a particular disease. By monitoring these daily counts we can possibly detect an outbreak in an early stage. A number of classica...

Accuracy Improvement of Mood Disorders Prediction using a Combination of Data Mining and Meta-Heuristic Algorithms

Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data. Method: Data collected in this applied developmental resear...