An Epidemiological Modelling Approach for Covid19 via Data Assimilation
Data Science Institute（数据科学研究所）；Imperial College London（伦敦帝国理工学院）
Philip Nadler, Shuo Wang, Rossella Arcucci, Xian Yang, Yike Guo
备注：
Initial conference version accepted at International Conference of Machine Learning(ICML) workshop. Extended journal version was published in the European Journal of Epidemiology (https://doi.org/10.1007/s10654-020-00676-7). Please cite as accordingly
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.
To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous work. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiologi…
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate …
Mathematical models have been used to understand the spread patterns of infectious diseases such as Coronavirus Disease 2019 (COVID-19). The transmission component of the models can be modelled in an age-dependent manner via introducing contact matrix for the population, which describes the contact rates between the age groups. Since social contact patterns vary from country to country, we can compare and group the countries using the corresponding contact matrices. In this paper, we present a framework for clustering countries based on their contact matrices with respect to an underlying epid…
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Constructing Contact and Connectivity Matrices for Infectious Disease Modelling
School of Mathematics and Statistics, University of St Andrews（圣安德鲁斯大学数学与统计学院）；Centre for Research into Ecological and Environmental Modelling, University of St Andrews（圣安德鲁斯大学生态与环境建模研究中心）；Department of Health, Medicine and Caring Sciences, Linköping University（林雪平大学健康、医学与护理科学学院）；School of Mathematics, Cardiff University（卡迪夫大学数学学院）；School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Australian Institute of Tropical Health and Medicine, James Cook University（詹姆斯库克大学澳大利亚热带健康与医学研究所）；Biomathematics and Statistics Scotland（苏格兰生物数学与统计学）；Department of Mathematical Sciences, University of Liverpool（利物浦大学数学科学系）；School of Mathematical Sciences, University of Nottingham（诺丁汉大学数学科学学院）；MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学单元）；Department of Mathematics, University of Manchester（曼彻斯特大学数学系）；Department of Statistics, University of Warwick（华威大学统计系）；School of Mathematics, University of Birmingham（伯明翰大学数学学院）
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