Imperial College London
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DrLilithWhittles

Faculty of Medicine, School of Public Health

Lecturer
 
 
 
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l.whittles

 
 
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Location

 

School of Public HealthWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Funk:2020:10.1101/2020.11.11.20220962,
author = {Funk, S and Abbott, S and Atkins, BD and Baguelin, M and Baillie, JK and Birrell, P and Blake, J and Bosse, NI and Burton, J and Carruthers, J and Davies, NG and De, Angelis D and Dyson, L and Edmunds, WJ and Eggo, RM and Ferguson, NM and Gaythorpe, K and Gorsich, E and Guyver-Fletcher, G and Hellewell, J and Hill, EM and Holmes, A and House, TA and Jewell, C and Jit, M and Jombart, T and Joshi, I and Keeling, MJ and Kendall, E and Knock, ES and Kucharski, AJ and Lythgoe, KA and Meakin, SR and Munday, JD and Openshaw, PJM and Overton, CE and Pagani, F and Pearson, J and Perez-Guzman, PN and Pellis, L and Scarabel, F and Semple, MG and Sherratt, K and Tang, M and Tildesley, MJ and Van, Leeuwen E and Whittles, LK},
doi = {10.1101/2020.11.11.20220962},
title = {Short-term forecasts to inform the response to the Covid-19 epidemic in the UK},
url = {http://dx.doi.org/10.1101/2020.11.11.20220962},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Short-term forecasts of infectious disease can aid situational awareness and planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the UK that were generated at regular intervals starting at the end of March 2020, in order to monitor expected healthcare utilisation and population impacts in real time.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We evaluated the performance of individual model forecasts generated between 24 March and 14 July 2020, using a variety of metrics including the weighted interval score as well as metrics that assess the calibration, sharpness, bias and absolute error of forecasts separately. We further combined the predictions from individual models into ensemble forecasts using a simple mean as well as a quantile regression average that aimed to maximise performance. We compared model performance to a null model of no change.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In most cases, individual models performed better than the null model, and ensembles models were well calibrated and performed comparatively to the best individual models. The quantile regression average did not noticeably outperform the mean ensemble.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Ensembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.</jats:p></jats:sec>
AU  - Funk,S
AU  - Abbott,S
AU  - Atkins,BD
AU  - Baguelin,M
AU  - Baillie,JK
AU  - Birrell,P
AU  - Blake,J
AU  - Bosse,NI
AU  - Burton,J
AU  - Carruthers,J
AU  - Davies,NG
AU  - De,Angelis D
AU  - Dyson,L
AU  - Edmunds,WJ
AU  - Eggo,RM
AU  - Ferguson,NM
AU  - Gaythorpe,K
AU  - Gorsich,E
AU  - Guyver-Fletcher,G
AU  - Hellewell,J
AU  - Hill,EM
AU  - Holmes,A
AU  - House,TA
AU  - Jewell,C
AU  - Jit,M
AU  - Jombart,T
AU  - Joshi,I
AU  - Keeling,MJ
AU  - Kendall,E
AU  - Knock,ES
AU  - Kucharski,AJ
AU  - Lythgoe,KA
AU  - Meakin,SR
AU  - Munday,JD
AU  - Openshaw,PJM
AU  - Overton,CE
AU  - Pagani,F
AU  - Pearson,J
AU  - Perez-Guzman,PN
AU  - Pellis,L
AU  - Scarabel,F
AU  - Semple,MG
AU  - Sherratt,K
AU  - Tang,M
AU  - Tildesley,MJ
AU  - Van,Leeuwen E
AU  - Whittles,LK
DO  - 10.1101/2020.11.11.20220962
PY  - 2020///
TI  - Short-term forecasts to inform the response to the Covid-19 epidemic in the UK
UR  - http://dx.doi.org/10.1101/2020.11.11.20220962
ER  -
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