The Design and Utilisation of PanSim, a Portable Pandemic Simulator

The COVID-19 pandemic has presented a clear and present need for urgent decision making. Set in an environment of uncertain and unreliable data, and a diverse range of possible interventions, there is an obvious need for integrating HPC into workflows that include model calibration, and the exploration of the decision space. In this paper, we present the design of PanSim, a portable, performant, and productive agent-based simulator, which has been extensively used to model and forecast the pandemic in Hungary. We show its performance and scalability on CPUs and GPUs, then we discuss the workflows PanSim integrates into. We describe the heterogeneous, resource-constrained HPC environment available to us, and formulate a scheduling optimisation problem, as well as heuristics to solve them, to either minimise the execution time of a given number of simulations or to maximise the number of simulations executed in a given time frame. © 2022 IEEE.

Model-based epidemic data reconstruction using feedback linearization

In this paper, a model-based method is proposed for the reconstruction of non-measured epidemic data of the COVID-19 pandemic in Hungary. Only the data series showing the daily number of hospitalized people are used for the reconstruction together with a nonlinear dynamical model of epidemic spread containing 8 compartments. The unknown input of the model is the infection rate, which is computed through the solution of a feedback linearization-based asymptotic output tracking problem, where the reference is the actually observed number of hospitalized people. Computations show good match with of hospitalized people. Computations show good match with previous reconstruction results, and show a roughly 3.5-4-fold underdetection of infections until the Omicron wave. © 2022 IEEE.

Computation of COVID-19 epidemiological data in Hungary using dynamic model inversion

In this paper, we estimate epidemiological data of the COVID-19 pandemic in Hungary using only the daily number of hospitalized patients, and applying well-known techniques from systems and control theory. We use a previously published and validated compartmental model for the description of epidemic spread. Exploiting the fact that an important subsystem of the model is linear, first we compute the number of latent infected persons in time. Then an estimate can be given for the number of people in other compartments. From these data, it is possible to track the time dependent reproduction numbers via a recursive least squares estimate. The credibility of the obtained results is discussed using available data from the literature. © 2021 IEEE.