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

@article{Parag:2021:10.1101/2021.09.08.21263270,
author = {Parag, KV and Donnelly, CA},
doi = {10.1101/2021.09.08.21263270},
title = {Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers},
url = {http://dx.doi.org/10.1101/2021.09.08.21263270},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>We find that epidemic resurgence, defined as an upswing in the effective reproduction number (<jats:italic>R</jats:italic>) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where <jats:italic>R</jats:italic> falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>The timely detection of epidemic resurgence (i.e., upcoming waves of infected cases) is crucial for informing public health policy, providing valuable signals for implementing interventions and identifying emerging pathogenic variants or important population-level behavioural shifts. Increases in epidemic transmissibility, parametrised by the time-varying reproduction number, <jats:italic>R</jats:italic>, commonly signify resurgence. While many studies have improved computation
AU  - Parag,KV
AU  - Donnelly,CA
DO  - 10.1101/2021.09.08.21263270
PY  - 2021///
TI  - Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers
UR  - http://dx.doi.org/10.1101/2021.09.08.21263270
ER  -
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