Prediction of an Epidemic Curve: A Supervised Classification Approach
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Classification methods are widely used for identifying underlying groupings within datasets and predicting the class for new data objects given a trained classifier. This study introduces a project aimed at using a combination of simulations and classification techniques to predict epidemic curves and infer underlying disease parameters for an ongoing outbreak.
Six supervised classification methods (random forest, support vector machines, nearest neighbor with three decision rules, linear and flexible discriminant analysis) were used in identifying partial epidemic curves from six agent-based stochastic simulations of influenza epidemics. The accuracy of the methods was compared using a performance metric based on the McNemar test.
The findings showed that: (1) assumptions made by the methods regarding the structure of an epidemic curve influences their performance i.e. methods with fewer assumptions perform best, (2) the performance of most methods is consistent across different individual-based networks for Seattle, Los Angeles and New York and (3) combining classifiers using a weighting approach does not guarantee better prediction.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- A Sequential Phase 2b Trial Design for Evaluating Vaccine Efficacy and Immune Correlates for Multiple HIV Vaccine Regimens
- Prediction of an Epidemic Curve: A Supervised Classification Approach
- Using HIV Diagnostic Data to Estimate HIV Incidence: Method and Simulation
- Nonparametric and Semiparametric Analysis of Current Status Data Subject to Outcome Misclassification
- Sample Size for a Binomial Proportion with Autocorrelation
- Statistical Considerations in Determining HIV Incidence from Changes in HIV Prevalence
- Joint Modeling of HCV and HIV Infections among Injecting Drug Users in Italy Using Repeated Cross-Sectional Prevalence Data
- Using Approximate Bayesian Computation to Estimate Transmission Rates of Nosocomial Pathogens
- Joint Modeling of HCV and HIV Co-Infection among Injecting Drug Users in Italy and Spain Using Individual Cross-Sectional Data
