Imperial College London
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DrRossellaArcucci

Faculty of Engineering, Department of Earth Science & Engineering

Senior Lecturer in Data Science and Machine Learning
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Nadler:2020,
author = {Nadler, P and Wang, S and Arcucci, R and Yang, X and Guo, Y},
publisher = {arXiv},
title = {An epidemiological modelling approach for Covid19 via data assimilation},
url = {http://arxiv.org/abs/2004.12130v1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - The global pandemic of the 2019-nCov requires the evaluation of policyinterventions to mitigate future social and economic costs of quarantinemeasures worldwide. We propose an epidemiological model for forecasting andpolicy evaluation which incorporates new data in real-time through variationaldata assimilation. We analyze and discuss infection rates in China, the US andItaly. In particular, we develop a custom compartmental SIR model fit tovariables related to the epidemic in Chinese cities, named SITR model. Wecompare and discuss model results which conducts updates as new observationsbecome available. A hybrid data assimilation approach is applied to makeresults robust to initial conditions. We use the model to do inference oninfection numbers as well as parameters such as the disease transmissibilityrate or the rate of recovery. The parameterisation of the model is parsimoniousand extendable, allowing for the incorporation of additional data andparameters of interest. This allows for scalability and the extension of themodel to other locations or the adaption of novel data sources.
AU  - Nadler,P
AU  - Wang,S
AU  - Arcucci,R
AU  - Yang,X
AU  - Guo,Y
PB  - arXiv
PY  - 2020///
TI  - An epidemiological modelling approach for Covid19 via data assimilation
UR  - http://arxiv.org/abs/2004.12130v1
UR  - http://hdl.handle.net/10044/1/79398
ER  -
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