Microsimulation based quantitative analysis of COVID-19 management strategies

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

doi:10.1371/journal.pcbi.1009693

Microsimulation based quantitative analysis of COVID-19 management strategies

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

22 Citations (Scopus)

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. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.

Original language	English
Article number	e1009693
Journal	PLoS Computational Biology
Volume	18
Issue number	1
DOIs	https://doi.org/10.1371/journal.pcbi.1009693
Publication status	Published - Jan 2022

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10.1371/journal.pcbi.1009693

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Reguly, I. Z., Csercsik, D., Juhász, J., Tornai, K., Bujtár, Z., Horváth, G., Keömley-Horváth, B., Kós, T., Cserey, G., Iván, K., Pongor, S., Szederkényi, G., Röst, G., & Csikász-Nagy, A. (2022). Microsimulation based quantitative analysis of COVID-19 management strategies. PLoS Computational Biology, 18(1), Article e1009693. https://doi.org/10.1371/journal.pcbi.1009693

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title = "Microsimulation based quantitative analysis of COVID-19 management strategies",

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. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.",

author = "Reguly, {Istv{\'a}n Z.} and D{\'a}vid Csercsik and J{\'a}nos Juh{\'a}sz and K{\'a}lm{\'a}n Tornai and Zs{\'o}fia Bujt{\'a}r and Gergely Horv{\'a}th and Bence Ke{\"o}mley-Horv{\'a}th and Tam{\'a}s K{\'o}s and Gy{\"o}rgy Cserey and Krist{\'o}f Iv{\'a}n and S{\'a}ndor Pongor and G{\'a}bor Szederk{\'e}nyi and Gergely R{\"o}st and Attila Csik{\'a}sz-Nagy",

note = "Funding Information: 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. Publisher Copyright: Copyright: {\textcopyright} 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.",

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AU - Csercsik, Dávid

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AU - Tornai, Kálmán

AU - Bujtár, Zsófia

AU - Horváth, Gergely

AU - Keömley-Horváth, Bence

AU - Kós, Tamás

AU - Cserey, György

AU - Iván, Kristóf

AU - Pongor, Sándor

AU - Szederkényi, Gábor

AU - Röst, Gergely

AU - Csikász-Nagy, Attila

N1 - Funding Information: 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. Publisher Copyright: 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.

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N2 - 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. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.

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Microsimulation based quantitative analysis of COVID-19 management strategies

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