Spatial covariance modeling for stochastic subgrid-scale parameterizations using dynamic mode decomposition

Stochastic parameterizations are increasingly being used in climate modeling to represent subgrid‐scale processes. While different parameterizations are being developed considering different aspects of the physical phenomena, less attention is given to technical and numerical aspects. In particular,empirical orthogonal functions (EOFs) are employed when a spatial structure is required. Here, we provide evidence they might not be the most suitable choice. By applying an energy‐consistent parameterization to the two‐layer quasi‐geostrophic (QG) model, we investigate the model sensitivity to apriori assumptions made on the parameterization. In particular, we consider here two methods to prescribe the spatial covariance of the noise:first, by using climatological variability patterns provided by EOFs,and second, by using time‐varying dynamics‐adapted Koopman modes, approximated by dynamic mode decomposition (DMD). The performance of the two methods are analyzed through numerical simulations of the stochastic system on a coarse spatial resolution and the outcomes compared to a high‐resoluti

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies

A multidisciplinary perspective on COVID-19 exit strategies

Lockdowns and associated measures imposed in response to the COVID-19 crisis inflict severe damage to society. Across the globe, scientists and policymakers study ways to lift measures while maintaining control of virus spread in circumstances that continuously change due to the evolution of new variants and increasing vaccination coverage. In this process, it has become clear that finding and analysing exit strategies, which are a key aspect of pandemic mitigation in all consecutive waves of infection, is not solely a matter of epidemiological modeling but has many different dimensions that need to be balanced and therefore requires input from many different disciplines. Here, we document an attempt to investigate exit strategies from a multidisciplinary perspective through the Science versus Corona project in the Netherlands. In this project, scientists and laypeople were challenged to submit (components of) exit strategies. A selection of these were implemented in a formal model, and we have evaluated the scenarios from a multidisciplinary perspective, utilizing expertise in epidemiology, economics, psychology, law, mathematics, and history. We argue for the integration of multidisciplinary perspectives on COVID-19 and more generally in pandemic mitigation, highlight open challenges, and present an agenda for further research into exit strategies and their assessmen

2 layers QG model with stochastic forcing

This project incudes the implementation of a numerical model solving the 2-layer Quasi-Geostrophic (QG) model with stochastic forcing as described in this paper. For details on the model and its numerical discretization/implementation please see the aforementioned paper

UQ_covid19

This repository contains the Python and R codes used for the paper titled: Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model