Imperial College London
Alternate

DrOliverRatmann

Faculty of Natural Sciences, Department of Mathematics

Reader in Statistics and Machine Learning for Public Good
 
 
 
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Contact

 

oliver.ratmann05 Website

 
 
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Location

 

525Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Penn:2023:10.1038/s42005-023-01265-2,
author = {Penn, MJJ and Laydon, DJJ and Penn, J and Whittaker, C and Morgenstern, C and Ratmann, O and Mishra, S and Pakkanen, MSS and Donnelly, CAA and Bhatt, S},
doi = {10.1038/s42005-023-01265-2},
journal = {Communications Physics},
title = {Intrinsic randomness in epidemic modelling beyond statistical uncertainty},
url = {http://dx.doi.org/10.1038/s42005-023-01265-2},
volume = {6},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in the epidemic process, and how these contributions vary over time. The aleatoric variance of an outbreak is itself a renewal equation where past variance affects future variance. We find that, superspreading is not necessary for substantial uncertainty, and profound variation in outbreak size can occur even without overdispersion in the offspring distribution (i.e. the distribution of the number of secondary infections an infected person produces). Aleatoric forecasting uncertainty grows dynamically and rapidly, and so forecasting using only epistemic uncertainty is a significant underestimate. Therefore, failure to account for aleatoric uncertainty will ensure that policymakers are misled about the substantially higher true extent of potential risk. We demonstrate our method, and the extent to which potential risk is underestimated, using two historical examples.
AU  - Penn,MJJ
AU  - Laydon,DJJ
AU  - Penn,J
AU  - Whittaker,C
AU  - Morgenstern,C
AU  - Ratmann,O
AU  - Mishra,S
AU  - Pakkanen,MSS
AU  - Donnelly,CAA
AU  - Bhatt,S
DO  - 10.1038/s42005-023-01265-2
PY  - 2023///
SN  - 2399-3650
TI  - Intrinsic randomness in epidemic modelling beyond statistical uncertainty
T2  - Communications Physics
UR  - http://dx.doi.org/10.1038/s42005-023-01265-2
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001012040300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://www.nature.com/articles/s42005-023-01265-2
UR  - http://hdl.handle.net/10044/1/109445
VL  - 6
ER  -
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