25gmds050 10.3205/25gmds050 urn:nbn:de:0183-25gmds0503 Meeting Abstract Bryzgalov Bryzgalov Aleksandr A

Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany

author Ponge Ponge Johannes J

University of Münster, Münster, Germany

author Suer Suer Janik J

University of Münster, Münster, Germany

author Krüger Krüger Tyll T

Wroclaw University of Science and Technology, Wroclaw, Poland

author Musundi Musundi Beryl B

Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany

author Xu Xu Chao C

Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany

author Horn Horn Johannes J

Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany

author Kahkashan Kahkashan Mahreen M

Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany

author Patzner Patzner Julian J

Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany

author Kretzschmar Kretzschmar Mirjam M

Utrecht University, Utrecht, Netherlands

author Mikolajczyk Mikolajczyk Rafael R

Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 weekend superspreading GEMS 20251103 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 050 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) V: Infectious disease and health care epidemiology Jena 20250907 20250911 Abstr. 316 TextIntroduction: So-called superspreaders and superspreading events were among the key drivers of the recent pandemic , . Generally, we can identify two possible sources of superspreading: a high probability of transmission during contact due to specific pathogen characteristics , and a high level of individual variability in contact rates . We focused on the latter in our analysis by inducing a weekend increase in contact rates while keeping the average weekly rate fixed. Moreover, since real-world contact patterns modulate the pace of epidemic spread as a function of the latent period , we studied the combined effect by varying the duration of the latent period.Methods: We used the German Epidemic Micro-Simulation System (GEMS) for our simulations. GEMS is an individual-based framework developed within the OptimAgent project. In GEMS, contacts are modeled across several settings: household, workplace, school class, and a global setting representing other potential interaction locations.We considered a synthetic population of 5,000,000 individuals, with an average household size of 2, an average workplace size of 10, and an average school class size of 20. All distributions were approximately Poisson-distributed. Initially, 0.1% of the population was infected with wild-type COVID-19 pathogen. However, the latent period was treated as a variable.After infection, each individual progresses through the SEIR compartments: susceptible, exposed, infectious, and recovered.Results: We compared two scenarios: one in which contacts between individuals were distributed uniformly throughout the week, and another where there was an increase in contacts within the global setting during the weekend. The total weekly mean number of contacts in each setting was kept the same in both scenarios. We also varied the latent period to analyze its coupling effect with the temporal structure of contacts.In the baseline scenario, we found that a longer latent period decreases the outbreak size, which is consistent with predictions from the classical equation-based SEIR model (see ). However, in the alternative scenario, varying the latent period resulted in the largest outbreak for a 6-day latent period and the smallest for a 2-day latent period.Conclusion: The latent period exhibits a coupling effect with the distribution of daily contact rates, leading to positive or negative resonance patterns in the spread of infection. This effect can be crucial in determining whether the epidemic remains subcritical (i.e., the effective reproduction number R0 < 1) or becomes supercritical (R0 > 1).The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. Illingworth CJ Hamilton WL Warne B Routledge M Popay A Jackson C Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections 2021 Elife e67308 Illingworth CJ, Hamilton WL, Warne B, Routledge M, Popay A, Jackson C, et al. Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections. Elife. 2021 Aug 24;10:e67308. Wegehaupt O Endo A Vassall A Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature 2023 BMC Public Health 1003 Wegehaupt O, Endo A, Vassall A. Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature. BMC Public Health. 2023 May 30;23(1):1003. Gómez-Carballa A Pardo-Seco J Bello X Martinón-Torres F Salas A Superspreading in the emergence of COVID-19 variants 2021 Trends Genet 1069-1080 Gómez-Carballa A, Pardo-Seco J, Bello X, Martinón-Torres F, Salas A. Superspreading in the emergence of COVID-19 variants. Trends Genet. 2021 Dec;37(12):1069-1080. Lloyd-Smith JO Schreiber SJ Kopp PE Getz WM Superspreading and the effect of individual variation on disease emergence 2005 Nature 355-9 Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005 Nov 17;438(7066):355-9. Zierenberg J Spitzner FP Dehning J Priesemann V Weigel M Wilczek M How contact patterns destabilize and modulate epidemic outbreaks 2023 New J Phys 053033 Zierenberg J, Spitzner FP, Dehning J, Priesemann V, Weigel M, Wilczek M. How contact patterns destabilize and modulate epidemic outbreaks. New J Phys. 2023 May 30;25(5):053033. Ponge J Horstkemper D Hellingrath B Bayer L Bock W Karch A Evaluating Parallelization Strategies for Large-Scale Individual-based Infectious Disease Simulations 2023 Winter Simulation Conference (WSC) 1088-1099 Ponge J, Horstkemper D, Hellingrath B, Bayer L, Bock W, Karch A. Evaluating Parallelization Strategies for Large-Scale Individual-based Infectious Disease Simulations. In: 2023 Winter Simulation Conference (WSC). p. 1088-1099. Towers S Chowell G Impact of weekday social contact patterns on the modeling of influenza transmission, and determination of the influenza latent period 2012 J Theor Biol 87-95 Towers S, Chowell G. Impact of weekday social contact patterns on the modeling of influenza transmission, and determination of the influenza latent period. J Theor Biol. 2012 Nov 7;312:87-95. 0 0 0 0