An epidemiological modelling approach for COVID-19 via data assimilation

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 the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. 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 and measurement errors in the data. We use the model to conduct 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.

Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts

“…There is a variety of ODE models for describing Covid-19 spreading, many of them built on the idea behind the SIRD model but extending it by adding categories, e.g., [14][15][16][17]19], or by considering other compartmental variants [26]. Other approaches utilize delay differential equations (DDE) [27] or stochastic models, either in the form of stochastic differential equations [28], in discrete form [29] or by probabilistic means [30].…”

Forecasting intensive care unit demand during the COVID-19 pandemic: A spatial age-structured microsimulation model

“…Epidemiological models that forecast the spread of COVID-19 have been used in several countries to inform policymaking on the efficacy of past and future policy measures (see, for example, Alban et al 2020; Davies et al 2020; IHME et al 2020; IHME & Murray 2020; Jewell et al 2020; Kucharski et al 2020; Nadler et al 2020; Prem et al 2020; Ruktanonchai et al 2020; McCabe et al 2020, Hellewell et al 2020). Forecasts of infectious diseases are often based on compartmental mathematical models, which divide a population into states and assume rates of transition from one state to another (Brauer 2017).…”

Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts

“…There is a variety of ODE models for describing Covid-19 spreading, many of them built on the idea behind the SIRD model but extending it by adding categories, e.g., [19, 22, 26, 29, 40], or by considering other compartmental variants [27]. Other approaches utilize delay differential equations (DDE) [11] or stochastic models, either in the form of stochastic differential equations [41], in discrete form [16] or by probabilistic means [12].…”