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Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation

View ORCID ProfileKV Parag, View ORCID ProfileCA Donnelly
doi: https://doi.org/10.1101/835181
KV Parag
1MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, UK
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  • For correspondence: k.parag@imperial.ac.uk
CA Donnelly
1MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, W2 1PG, UK
2Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
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Abstract

The effective reproduction number, Rt, is an important prognostic for infectious disease epidemics. Significant changes in Rt can forewarn about new transmissions or predict the efficacy of interventions. The renewal model infers Rt from incidence data and has been applied to Ebola virus disease and pandemic influenza outbreaks, among others. This model estimates Rt using a sliding window of length k. While this facilitates real-time detection of statistically significant Rt fluctuations, inference is highly k -sensitive. Models with too large or small k might ignore meaningful changes or over-interpret noise-induced ones. No principled k -selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory. We derive exact incidence prediction distributions and integrate these within an APE framework to identify the k best supported by available data. We find that this k optimises short-term prediction accuracy and expose how common, heuristic k -choices, which seem sensible, could be misleading.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 26, 2019.
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Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation
KV Parag, CA Donnelly
bioRxiv 835181; doi: https://doi.org/10.1101/835181
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Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation
KV Parag, CA Donnelly
bioRxiv 835181; doi: https://doi.org/10.1101/835181

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