Imperial College London
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Professor Christl Donnelly CBE FMedSci FRS

Faculty of Medicine, School of Public Health

Visiting Professor
 
 
 
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Contact

 

c.donnelly Website

 
 
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Location

 

School of Public HealthWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Parag:2022:10.1101/2022.05.16.22275147,
author = {Parag, KV and Donnelly, CA and Zarebski, AE},
doi = {10.1101/2022.05.16.22275147},
title = {Quantifying the information in noisy epidemic curves},
url = {http://dx.doi.org/10.1101/2022.05.16.22275147},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>Abstract</jats:title><jats:p>Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters, such as the instantaneous reproduction number, <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> at time <jats:italic>t</jats:italic>, are often inferred from incident time series, with the aim of informing policymakers 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 those time series. While studies have proposed corrections for these issues, methodology for formally assessing how these sources of noise degrade <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub> estimate quality is lacking. By adapting Fisher information and experimental design theory, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections. This yields a novel metric, defined by the geometric means of reporting and cumulative delay probabilities, for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring <jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.</jats:p>
AU  - Parag,KV
AU  - Donnelly,CA
AU  - Zarebski,AE
DO  - 10.1101/2022.05.16.22275147
PY  - 2022///
TI  - Quantifying the information in noisy epidemic curves
UR  - http://dx.doi.org/10.1101/2022.05.16.22275147
ER  -
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