An epidemiological modelling approach for Covid19 via data assimilation
File(s)
Working paper
Author(s)
Nadler, Philip
Wang, Shuo
Arcucci, Rossella
Yang, Xian
Guo, Yike
Type
Working Paper
Abstract
The global pandemic of the 2019-nCov requires the evaluation of policy
interventions to mitigate future social and economic costs of quarantine
measures worldwide. We propose an epidemiological model for forecasting and
policy evaluation which incorporates new data in real-time through variational
data assimilation. We analyze and discuss infection rates in China, the US and
Italy. In particular, we develop a custom compartmental SIR model fit to
variables related to the epidemic in Chinese cities, named SITR model. We
compare and discuss model results which conducts updates as new observations
become available. A hybrid data assimilation approach is applied to make
results robust to initial conditions. We use the model to do inference on
infection numbers as well as parameters such as the disease transmissibility
rate or the rate of recovery. The parameterisation of the model is parsimonious
and extendable, allowing for the incorporation of additional data and
parameters of interest. This allows for scalability and the extension of the
model to other locations or the adaption of novel data sources.
interventions to mitigate future social and economic costs of quarantine
measures worldwide. We propose an epidemiological model for forecasting and
policy evaluation which incorporates new data in real-time through variational
data assimilation. We analyze and discuss infection rates in China, the US and
Italy. In particular, we develop a custom compartmental SIR model fit to
variables related to the epidemic in Chinese cities, named SITR model. We
compare and discuss model results which conducts updates as new observations
become available. A hybrid data assimilation approach is applied to make
results robust to initial conditions. We use the model to do inference on
infection numbers as well as parameters such as the disease transmissibility
rate or the rate of recovery. The parameterisation of the model is parsimonious
and extendable, allowing for the incorporation of additional data and
parameters of interest. This allows for scalability and the extension of the
model to other locations or the adaption of novel data sources.
Date Issued
2020-04-25
Citation
2020
Publisher
arXiv
Copyright Statement
© 2020 The Author(s)
Identifier
http://arxiv.org/abs/2004.12130v1
Subjects
stat.AP
stat.AP
cs.LG
stat.ME
stat.ML
Notes
8 pages
Publication Status
Published