Multiscale Modeling and Forecasting of COVID-19 and Respiratory Virus Dynamics

A special issue of Viruses (ISSN 1999-4915). This special issue belongs to the section "General Virology".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 741

Special Issue Editors


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Guest Editor
School of Public Health, Georgia State University, Atlanta, GA 30303, USA
Interests: mathematical and statistical modeling of infectious diseases, with emphasis on real-time forecasting, excess mortality estimation, and the role of socio-demographic factors in epidemic dynamics
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Guest Editor
Applied Mathematics, College of Applied Science, Kyung Hee University, Yongin 17104, Republic of Korea
Interests: mathematical and computational modeling combined with optimization techniques to study the epidemiology and transmission dynamics of infectious diseases and evaluate the effectiveness of various intervention strategies

Special Issue Information

Dear Colleague,

This Special Issue invites timely research on mathematical and computational approaches to characterizing and forecasting the evolving dynamics of COVID-19 and other respiratory viruses across individual and population scales. We encourage contributions which use compartmental models (e.g., SEIR-type), agent-based simulations, and hybrid frameworks that incorporate multiple data sources. We place special emphasis on real-time forecasting, excess mortality modeling, Bayesian inference, and parameter identifiability. Papers exploring comparative model performance (e.g., using AIC/BIC), uncertainty quantification, and the integration of behavioral, genomic, and surveillance data are also welcome. This Special Issue aims to foster multidisciplinary collaboration and showcase modeling innovations that can inform public health strategies in a post-pandemic world.

Prof. Dr. Gerardo Chowell
Prof. Dr. Sunmi Lee
Guest Editors

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Keywords

  • COVID-19 modeling
  • respiratory virus dynamics modeling
  • epidemic forecasting
  • parameter identifiability
  • multiscale transmission dynamics

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Published Papers (2 papers)

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Research

21 pages, 4582 KiB  
Article
Modeling the Complete Dynamics of the SARS-CoV-2 Pandemic of Germany and Its Federal States Using Multiple Levels of Data
by Yuri Kheifetz, Holger Kirsten, Andreas Schuppert and Markus Scholz
Viruses 2025, 17(7), 981; https://doi.org/10.3390/v17070981 - 14 Jul 2025
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Abstract
Background/Objectives: Epidemiological modeling is a vital tool for managing pandemics, including SARS-CoV-2. Advances in the understanding of epidemiological dynamics and access to new data sources necessitate ongoing adjustments to modeling techniques. In this study, we present a significantly expanded and updated version [...] Read more.
Background/Objectives: Epidemiological modeling is a vital tool for managing pandemics, including SARS-CoV-2. Advances in the understanding of epidemiological dynamics and access to new data sources necessitate ongoing adjustments to modeling techniques. In this study, we present a significantly expanded and updated version of our previous SARS-CoV-2 model formulated as input–output non-linear dynamical systems (IO-NLDS). Methods: This updated framework incorporates age-dependent contact patterns, immune waning, and new data sources, including seropositivity studies, hospital dynamics, variant trends, the effects of non-pharmaceutical interventions, and the dynamics of vaccination campaigns. Results: We analyze the dynamics of various datasets spanning the entire pandemic in Germany and its 16 federal states using this model. This analysis enables us to explore the regional heterogeneity of model parameters across Germany for the first time. We enhance our estimation methodology by introducing constraints on parameter variation among federal states to achieve this. This enables us to reliably estimate thousands of parameters based on hundreds of thousands of data points. Conclusions: Our approach is adaptable to other epidemic scenarios and even different domains, contributing to broader pandemic preparedness efforts. Full article
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13 pages, 5063 KiB  
Article
Multiscale Modeling of Hospital Length of Stay for Successive SARS-CoV-2 Variants: A Multi-State Forecasting Framework
by Minchan Choi, Jungeun Kim, Heesung Kim, Ruarai J. Tobin and Sunmi Lee
Viruses 2025, 17(7), 953; https://doi.org/10.3390/v17070953 - 6 Jul 2025
Viewed by 331
Abstract
Understanding how hospital length of stay (LoS) evolves with successive SARS-CoV-2 variants is central to the multiscale modeling and forecasting of COVID-19 and other respiratory virus dynamics. Using records from 1249 COVID-19 patients admitted to Chungbuk National University Hospital (2021–2023), we quantified LoS [...] Read more.
Understanding how hospital length of stay (LoS) evolves with successive SARS-CoV-2 variants is central to the multiscale modeling and forecasting of COVID-19 and other respiratory virus dynamics. Using records from 1249 COVID-19 patients admitted to Chungbuk National University Hospital (2021–2023), we quantified LoS across three distinct variant phases (Pre-Delta, Delta, and Omicron) and three age groups (0–39, 40–64, and 65+ years). A gamma-distributed multi-state model—capturing transitions between semi-critical and critical wards—incorporated variant phase and age as log-linear covariates. Parameters were estimated via maximum likelihood with 95% confidence intervals derived from bootstrap resampling, and Monte Carlo iterations yielded detailed LoS distributions. Omicron-phase stays were 5–8 days, shorter than the 10–14 days observed in earlier phases, reflecting improved treatment protocols and reduced virulence. Younger adults typically stayed 3–5 days, whereas older cohorts required 8–12 days, with prolonged admissions (over 30 days) clustering in the oldest group. These time-dependent transition probabilities can be integrated with real-time bed-availability alert systems, highlighting the need for variant-specific ward/ICU resource planning and underscoring the importance of targeted management for elderly patients during current and future pandemics. Full article
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