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High-throughput Detection Method for Influenza Virus
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Improved Discrimination of Influenza Forecast Accuracy Using Consecutive Predictions
Jeffrey Shaman1, Sasikiran Kandula1
1Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA.
Plos Currents
|October 30, 2015
Summary
Forecasting infectious disease spread, like influenza, is improving. New methods enhance accuracy by analyzing forecast consistency over time, providing a more reliable prediction of disease trends.
Area of Science:
- Epidemiology
- Computational Biology
- Public Health
Background:
- Infectious disease incidence prediction has advanced significantly.
- Accurate influenza epidemiology forecasts are crucial for public health preparedness.
Purpose of the Study:
- To improve the assessment of real-time influenza forecast accuracy.
- To explore novel methods for discriminating forecast reliability.
Main Methods:
- Utilizing ensemble simulations of local influenza transmission dynamics.
- Employing data assimilation and optimization with incidence observations.
- Quantifying ensemble agreement (variance) for real-time accuracy inference.
Main Results:
- Forecast expected accuracy can be enhanced by considering forecast persistence.
- The 'streak' or number of consecutive weeks of forecast convergence improves accuracy discrimination.
Conclusions:
- Combining ensemble agreement and forecast streak offers a more detailed accuracy assessment.
- This approach provides a more informative evaluation of infectious disease forecast reliability.
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