Data suggested hospitalization as critical indicator of the severity of the COVID-19 pandemic, even at its early stages

Stefanie Fuderer; Christina Kuttler; Michael Hoelscher; Ludwig Christian Hinske; Noemi Castelletti; Stefanie Fuderer; Christina Kuttler; Michael Hoelscher; Ludwig Christian Hinske; Noemi Castelletti

doi:10.3934/mbe.2023452

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10304-10338. doi: 10.3934/mbe.2023452

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Research article

Data suggested hospitalization as critical indicator of the severity of the COVID-19 pandemic, even at its early stages

1.
Department of Mathematics, Technical University of Munich, Garching, Germany
2.
Division of Infectious Diseases and Tropical Medicine, Medical Center of the University of Munich, Munich, Germany
3.
German Center for Infection Research (DZIF), partner site Munich, Munich, Germany
4.
Center for International Health (CIH), University Hospital, Munich, Germany
5.
Department of Anesthesiology, University Hospital, Munich, Germany
6.
Institute of Radiation Medicine, Helmholtz Zentrum München, Neuherberg, Germany

Academic Editor: Daozhou Gao

Received: 20 October 2022 Revised: 02 December 2022 Accepted: 13 December 2022 Published: 31 March 2023

COVID-19 has been spreading widely since January 2020, prompting the implementation of non-pharmaceutical interventions and vaccinations to prevent overwhelming the healthcare system. Our study models four waves of the epidemic in Munich over two years using a deterministic, biology-based mathematical model of SEIR type that incorporates both non-pharmaceutical interventions and vaccinations. We analyzed incidence and hospitalization data from Munich hospitals and used a two-step approach to fit the model parameters: first, we modeled incidence without hospitalization, and then we extended the model to include hospitalization compartments using the previous estimates as a starting point. For the first two waves, changes in key parameters, such as contact reduction and increasing vaccinations, were enough to represent the data. For wave three, the introduction of vaccination compartments was essential. In wave four, reducing contacts and increasing vaccinations were critical parameters for controlling infections. The importance of hospitalization data was highlighted, as it should have been included as a crucial parameter from the outset, along with incidence, to avoid miscommunication with the public. The emergence of milder variants like Omicron and a significant proportion of vaccinated people has made this fact even more evident.
- SARS-CoV-2,
- modeling,
- differential equation,
- hospitalization,
- prediction,
- data analysis
Citation: Stefanie Fuderer, Christina Kuttler, Michael Hoelscher, Ludwig Christian Hinske, Noemi Castelletti. Data suggested hospitalization as critical indicator of the severity of the COVID-19 pandemic, even at its early stages[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10304-10338. doi: 10.3934/mbe.2023452

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Abstract

COVID-19 has been spreading widely since January 2020, prompting the implementation of non-pharmaceutical interventions and vaccinations to prevent overwhelming the healthcare system. Our study models four waves of the epidemic in Munich over two years using a deterministic, biology-based mathematical model of SEIR type that incorporates both non-pharmaceutical interventions and vaccinations. We analyzed incidence and hospitalization data from Munich hospitals and used a two-step approach to fit the model parameters: first, we modeled incidence without hospitalization, and then we extended the model to include hospitalization compartments using the previous estimates as a starting point. For the first two waves, changes in key parameters, such as contact reduction and increasing vaccinations, were enough to represent the data. For wave three, the introduction of vaccination compartments was essential. In wave four, reducing contacts and increasing vaccinations were critical parameters for controlling infections. The importance of hospitalization data was highlighted, as it should have been included as a crucial parameter from the outset, along with incidence, to avoid miscommunication with the public. The emergence of milder variants like Omicron and a significant proportion of vaccinated people has made this fact even more evident.

References

[1]	B. Ganesh, T. Rajakumar, M. Malathi, N. Manikandan, J. Nagaraj, A. Santhakumar, et al., Epidemiology and pathobiology of SARS-CoV-2 (COVID-19) in comparison with SARS, MERS: An updated overview of current knowledge and future perspectives, Clin. Epidemiol. Glob. Health, 10 (2021), 100694. https://doi.org/10.1016/j.cegh.2020.100694 doi: 10.1016/j.cegh.2020.100694
[2]	World Health Organization, WHO Director-General's Opening Remarks at the Media Briefing on COVID-19-11 March 2020, 2020. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
[3]	World Health Organization, WHO Coronavirus (COVID-19) Dashboard, 2022. Available from: https://covid19.who.int/
[4]	Robert Koch-Institute (RKI), Table showing current Covid-19 infections per day as time series, 2021. Available from: https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0/explore
[5]	Robert Koch-Institute (RKI), Kontaktpersonen-Nachverfolgung bei SARS-CoV-2-Infektionen, 2021. Available from: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Kontaktperson/Management.html; jsessionid = 78E4E999E14B2E6A3E8F02E5957D2C97.internet121?nn = 13490888#doc13516162bodyText14
[6]	Robert Koch-Institute (RKI), Tabelle mit den gemeldeten Impfungen nach Bundesländern und Impfquoten nach Altersgruppen, 2021. Available from: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Daten/Impfquotenmonitoring
[7]	Robert Koch-Institute (RKI), Kurz und Knapp: Faktenblätter zum Impfen - COVID-19 Impfung, 2021. Available from: https://www.rki.de/DE/Content/Infekt/Impfen/Materialien/Faktenblaetter/Faktenblaetter_inhalt.html
[8]	J. Schilling, A. Lehfeld, D. Schumacher, M. Diercke, S. Buda, W. Haas, et al., Krankheitsschwere der ersten COVID-19-Welle in Deutschland basierend auf den Meldungen gemäß Infektionsschutzgesetz, J. Health Monit., 5 (2020), 2–20. https://doi.org/10.25646/7169 doi: 10.25646/7169
[9]	Centers for Disease Control and Prevention (CDC), National Center for Immunization and Respiratory Diseases (NCIRD), Disease Burden of Flu, 2022. Available from: https://www.cdc.gov/flu/about/burden/index.html/
[10]	Robert Koch-Institute (RKI), Coronavirus SARS-CoV-2: Fallzahlen und Meldungen, 2021. Available from: https://www.rki.de/SharedDocs/FAQ/NCOV2019/FAQ_Liste_Fallzahlen_Meldungen.html
[11]	R. M. Anderson, The role of mathematical models in the study of HIV transmission and the epidemiology of AIDS, J. Acquir. Immune Defic. Syndr., 1 (1988), 241–256.
[12]	F. Brauer, C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology, Springer, New York, 2012.
[13]	D. Chumachenko, I. Meniailov, K. Bazilevych, T. Chumachenko, S. Yakovlev, Investigation of statistical machine learning models for COVID-19 epidemic process simulation: Random Forest, K-Nearest Neighbors, Gradient Boosting, Computation, 10 (2022), 2079–3197. https://doi.org/10.3390/computation10060086 doi: 10.3390/computation10060086
[14]	F. Ying, N. O'Clery, Modelling COVID-19 transmission in supermarkets using an agent-based model, PLoS One, 16 (2021), 0249821. https://doi.org/10.1371/journal.pone.0249821 doi: 10.1371/journal.pone.0249821
[15]	R. Nistal, M. de la Sen, J. Gabirondo, S. Alonso-Quesada, A. J. Garrido, I. Garrido, A Study on COVID-19 incidence in Europe through two SEIR epidemic models which consider mixed contagions from asymptomatic and symptomatic individuals, Appl. Sci., 11 (2021), 2076–3417. https://doi.org/10.3390/app11146266 doi: 10.3390/app11146266}
[16]	R. Nistal, M. de la Sen, J. Gabirondo, S. Alonso-Quesada, A. J. Garrido, I. Garrido, A Modelization of the Propagation of COVID-19 in regions of Spain and Italy with evaluation of the transmission rates related to the intervention measures, Biology, 10 (2021), 121. https://doi.org/10.3390/biology10020121 doi: 10.3390/biology10020121
[17]	J. Ozaki, Y. Shida, H. Takayasu, M. Takayasu, Direct modelling from GPS data reveals daily-activity-dependency of effective reproduction number in COVID-19 pandemic, Sci Rep., 12, (2022), 17888. https://doi.org/10.1038/s41598-022-22420-9 doi: 10.1038/s41598-022-22420-9
[18]	D. Okuonghae, A. Omame, Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria, Chaos, Solitons Fractals, 139 (2020), 110032. https://doi.org/10.1016/j.chaos.2020.110032 doi: 10.1016/j.chaos.2020.110032
[19]	M. V. Barbarossa, J. Fuhrmann, J. H. Meinke, S. Krieg, H. V. Varma, N. Castelletti, et al., Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios, PLoS ONE, 15, (2020). https://doi.org/10.1371/journal.pone.0238559 doi: 10.1371/journal.pone.0238559
[20]	Süddeutsche Zeitung, Coronavirus-Pandemie in Bayern: Rückblick April 2020, Süddeutsche Zeitung, 2020. Available from: https://www.sueddeutsche.de/bayern/coronavirus-bayern-rueckblick-april-1.4873340
[21]	Norddeutscher Rundfunk, Corona-Chronologie: November 2020, 2020. Available from: https://www.ndr.de/nachrichten/info/Corona-Chronologie-November-2020, coronachronologie128.html
[22]	Norddeutscher Rundfunk, Corona-Chronologie: Dezember 2020. Available from: https://www.ndr.de/nachrichten/info/Corona-Chronologie-Dezember-2020, coronachronologie130.html
[23]	Norddeutscher Rundfunk, Corona-Chronologie: Januar 2021, 2021. Available from: https://www.ndr.de/nachrichten/info/Corona-Chronologie-Januar-2021, coronachronologie134.html
[24]	Süddeutsche Zeitung, Was in München ab heute gilt, 2021. Available from: https://www.sueddeutsche.de/muenchen/muenchen-corona-notbremse-mittwoch-1.5263849
[25]	Süddeutsche Zeitung, Diese Corona-Regeln gelten nun in München, 2021. Available from: https://www.sueddeutsche.de/muenchen/muenchen-corona-lockerungen-gastronomie-ausgangssperre-1.5288681
[26]	Bayerische Staatsregierung, Pressemitteilungen: Bericht aus der Kabinettssitzung vom 4. Juni 2021, 2021. Available from: https://www.bayern.de/bericht-aus-der-kabinettssitzung-vom-4-juni-2021/
[27]	Deutsche Akademie der Naturforscher Leopoldina, Ad-hoc-Stellungnahmen zur Coronavirus-Pandemie: Zusammenfassung der Stellungnahmen zur Coronavirus-Pandemie, 2020.
[28]	H. Panknin, S. Schröder, Konsequente Schutzmaßnahmen gegen Ansteckung, Procare, 25, (2020), 20–22. https://doi.org/10.1007/s00735-020-1261-x doi: 10.1007/s00735-020-1261-x
[29]	Robert Koch-Institute (RKI), Übersicht zu besorgniserregenden SARS-CoV-2-Virusvarianten (VOC), 2021. Available from: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Virusvariante.html
[30]	Robert Koch-Institute (RKI), Berichte zu Virusvarianten von SARS-CoV-2 in Deutschland, 2021. Available from: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/DESH/Berichte-VOC-tab.html
[31]	Robert Koch-Institute (RKI), SARS-CoV-2: Virologische Basisdaten sowie Virusvarianten, 2021. Available from: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Virologische_Basisdaten.html; jsessionid = A1752CE0733B8B43969A64CD80B9B253.internet082?nn = 13490888
[32]	Landeshauptstadt München-Das offizielle Stadtportal, Coronavirus-Fälle in München: Aktuelle Zahlen, 2021. Available from: https://www.muenchen.de/rathaus/Stadtinfos/Coronavirus-Fallzahlen.html
[33]	E. Callaway, Delta coronavirus variant: scientists brace for impact, Nature, (2021), 17–18. https://doi.org/10.1038/d41586-021-01696-3 doi: 10.1038/d41586-021-01696-3
[34]	BR24, Corona-Regeln: Was aktuell in Bayern gilt, 2021. Available from: https://www.br.de/nachrichten/bayern/corona-regeln-in-bayern-was-gilt, Sbtbuu0
[35]	Süddeutsche Zeitung, Corona in Bayern: Newsblog vom 5. bis zum 26. Juli 2021, 2021. Available from: https://www.sueddeutsche.de/bayern/corona-bayern-archiv-1.5342370
[36]	Süddeutsche Zeitung, Corona-Regeln Bayern: Welche Regeln aktuell in Bayern gelten, 2021. Available from: https://www.sueddeutsche.de/bayern/bayern-corona-regeln-2g-3g-maskenpflicht-1.4878824
[37]	C. Walter, F. Fischer, Interdisciplinary Supply Proof (IVENA): Improving Emergency Care through E-Health?, NOTARZT, 33, (2017), 50.
[38]	Robert Koch-Institute (RKI), Epidemiologischer Steckbrief zu SARS-CoV-2 und COVID-19, 2021. Available from: https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Steckbrief.html; jsessionid = 1E68F40DA18BE45CC49EE51EA8FA8CC8.internet051?nn = 2386228
[39]	S. Amit, G. Regev-Yochay, A. Afek, Y. Kreiss, E. Leshem, Early rate reductions of SARS-CoV-2 infection and COVID-19 in BNT162b2 vaccine recipients, Lancet, 397, (2021), 875–877. https://doi.org/10.1016/s0140-6736(21)00448-7 doi: 10.1016/s0140-6736(21)00448-7
[40]	H. Xin, Y. Li, P. Wu, Z. Li, E. H. Y. Lau, Y. Qin, et al., Estimating the latent period of coronavirus disease 2019 (COVID-19), Clin. Infect. Dis., 74 (2022), 1678–1681. https://doi.org/10.1093/cid/ciab746 doi: 10.1093/cid/ciab746
[41]	D. Buitrago-Garcia, D. Egli-Gany, M. J. Counotte, S. Hossmann, H. Imeri, A. M. Ipekci, et al., Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis, PLoS Med., 17, (2020). https://doi.org/10.1371/journal.pmed.1003346 doi: 10.1371/journal.pmed.1003346
[42]	Bayerisches Landesamt für Statistik, Die Datenbank des Bayerischen Landesamtes für Statistik, 2023. Available from: https://www.statistikdaten.bayern.de/genesis/online; jsessionid%3D6CB3363574391F9435FD661D9125B699?sequenz%3DtabelleErgebnis%26selectionname%3D12411-001
[43]	N. Potere, E. Valeriani, M. Candeloro, M. Tana, E. Porreca, A. Abbate, et al., Acute complications and mortality in hospitalized patients with coronavirus disease 2019: A systematic review and meta-analysis, Crit. Care, 24 (2020), 1–12. https://doi.org/10.1186/s13054-020-03022-1 doi: 10.1186/s13054-020-03022-1
[44]	M. Martcheva, An Introduction to Mathematical Epidemiology, Springer, New York, 2015.
[45]	Y. Goldberg, M. Mandel, Y. M. Bar-On, O. Bodenheimer, L. S. Freedman, E. Haas, et al., Waning immunity of the BNT162b2 vaccine: A nationwide study from Israel, New Engl. J. Med., (2021). https://doi.org/10.1056/nejmoa2114228 doi: 10.1056/nejmoa2114228
[46]	BR24, Covid-19 in den Kliniken: Die Welle der Ungeimpften, 2021. Available from: https://www.br.de/nachrichten/bayern/covid-19-in-den-kliniken-die-welle-der-ungeimpften, SjYWL3B
[47]	K. P. Burnham, D. R. Anderson, Model Selection and Multimodel Inference, Springer, New York, 2002.
[48]	M. L. Gavrilova, O. Gervasi, V. Kumar, C. J. K. Tan, D. Taniar, A. Laganà, et al., Computational science and its applications-ICCSA 2006: international conference, Glasgow, in ICCSA: International Conference on Computational Science and Its Applications, 3982 (2006).

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