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Quantifying the information in noisy epidemic curves - Nature Computational Science

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Quantifying the information in noisy epidemic curves - Nature Computational Science

Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series.

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Quantifying the information in noisy epidemic curves - Nature Computational Science

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Quantifying the information in noisy epidemic curves - Nature Computational Science

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