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Understanding biases in epidemic models important when making public health predictions

fection when fitted to initial outbreak data."

The idea for the project came about when the Centers for Disease Control and Prevention approached Rohani to assist in the modeling of potential smallpox introduction. "I noticed that when we assumed different distributions of the infectious period in the models, we observed very different epidemic curves," said the researcher. Conversations with Wearing and Keeling followed, alternative models were compared and data from an influenza outbreak were used to illustrate the potential bias in model predictions.

The impact of such differences on predicting the spread of new, highly transmissible diseases could be important to public health workers, said Wearing. Underestimating the transmissibility of an infection could lead to predicting inadequate levels of disease management, such as contact tracing and quarantining of those exposed. However, Wearing was careful to stress that current health protection programs have shown their effectiveness in such situations as the recent SARS outbreak in Southeast Asia.

"We are highlighting what 'may happen' if we don't pay careful attention to the inherent assumptions in the models that we fit to data," said Wearing. "The key point is that uncertainty about the latent and infectious period distributions should be taken into account when making quantitative predictions for public health use."


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Source:University of Georgia


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