István Z. Reguly, Dávid Csercsik, János Juhász, Kálmán Tornai, Zsófia Bujtár, Gergely Horváth, Bence Keömley-Horváth, Tamás Kós, György Cserey, Kristóf Iván, Sándor Pongor, Gábor Szederkényi, Gergely Röst, Attila Csikász-Nagy
Abstract
Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data.
Introduction
Epidemic management includes a variety of control measures ranging from non-pharmaceutical interventions such as social distancing, testing and quarantining, to vaccination, hospitalisation, and beyond. Typically, control measures are differentially applied to various groups (compartments) of the society and decision-makers often need to refocus their intervention strategies as new infection hotspots or new virus variants emerge. Mathematical modelling is now increasingly used to inform decision-makers
Materials and methods
Here we present a modelling approach using the notions of control theory wherein a detailed, agent-based, microsimulation description was built for a mid-sized Hungarian town using realistic statistics on the population as well as on its daily movements. We used this Pandemics Simulator model (for brevity PanSim) to simulate the COVID-19 (SARS-CoV-2) pandemics starting from the onset of the second wave in the Autumn of 2020 and continuing through the Spring of 2021 until September 2021.
Although the presented results are focused on a single town, PanSim can be applied to larger populations, and in fact, our results were used to assist Hungarian decision-makers in designing control measures.
Discussion
The modelling framework described here was primarily developed to handle lockdown, quarantine, and vaccination scenarios, but PanSim also enables the analysis of hidden variables, which cannot always be measured in real life. Nevertheless, relevant new information also emerged from the simulation results. For instance, it shows that the autumn wave affected the various age groups quite similarly, while in the larger spring wave, distinct age groups were differentially involved.
Citation: Reguly IZ, Csercsik D, Juhász J, Tornai K, Bujtár Z, Horváth G, et al. (2022) Microsimulation based quantitative analysis of COVID-19 management strategies. PLoS Comput Biol 18(1): e1009693. https://doi.org/10.1371/journal.pcbi.1009693
Editor: Claudio José Struchiner, Fundação Getúlio Vargas: Fundacao Getulio Vargas, BRAZIL
Received: July 7, 2021; Accepted: November 29, 2021; Published: January 4, 2022.
Copyright: © 2022 Reguly et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All code and data available at https://github.com/khbence/pansim.
Funding: This work was carried out within the framework of the Hungarian National Development, Research and Innovation (NKFIH) Fund 2020-2.1.1-ED-2020-00003 and Thematic Excellence Programme (TKP2020-NKA-11). All authors were funded from these sources. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
