Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.7
3.5
Journals
Viruses
Special Issues
Multiscale Modeling and Forecasting of COVID-19 and Respiratory Virus Dynamics
share announcement

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: 15 May 2026 | Viewed by 2667

Special Issue Editors

Prof. Dr. Gerardo Chowell

E-Mail Website
Guest Editor
School of Public Health, Georgia State University, Atlanta, GA 30303, USA
Interests: mathematical and statistical modeling of infectious diseases; epidemic dynamics; real-time forecasting; socio-demographic factors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sunmi Lee

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Viruses is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

27 pages, 10004 KB  
Article
Nowcast-It: A Practical Toolbox for Real-Time Adjustment of Reporting Delays in Epidemic Surveillance
by Amna Tariq, Ping Yan, Amanda Bleichrodt and Gerardo Chowell
Viruses 2025, 17(12), 1598; https://doi.org/10.3390/v17121598 (registering DOI) - 10 Dec 2025
Abstract
One difficulty that arises in tracking and forecasting real-time epidemics is reporting delays, which are defined as the inherent delay between the time of symptom onset and the time a case is reported. Reporting delays can be caused by delays in case detection, [...] Read more.
One difficulty that arises in tracking and forecasting real-time epidemics is reporting delays, which are defined as the inherent delay between the time of symptom onset and the time a case is reported. Reporting delays can be caused by delays in case detection, symptom onset after infection, seeking medical care, or diagnostics, and they distort the accurate forecasting of diseases during epidemics and pandemics. To address this, we introduce a practical nowcasting approach grounded in survival analysis and actuarial science, explicitly allowing for non-stationarity in reporting delay patterns to better capture real-world variability. Despite its relevance, no flexible and accessible toolbox currently exists for non-stationary delay adjustment. Here, we present Nowcast-It, a tutorial-based toolbox that includes two components: (1) an R code base for delay adjustment and (2) a user-friendly R-Shiny application to enable interactive visualization and reporting delay correction without prior coding expertise. The toolbox supports daily, weekly, or monthly resolution data and enables model performance assessment using metrics such as mean absolute error, mean squared error, and 95% prediction interval coverage. We demonstrate the utility of Nowcast-It toolbox using publicly available weekly Ebola case data from the Democratic Republic of Congo. We and others have adjusted for reporting delays in real-time analyses (e.g., Singapore) and produced early COVID-19 forecasts; here, we package those delay adjustment routines into an accessible toolbox. It is designed for researchers, students, and policymakers alike, offering a scalable and accessible solution for addressing reporting delays during outbreaks. Full article
(This article belongs to the Special Issue Multiscale Modeling and Forecasting of COVID-19 and Respiratory Virus Dynamics)
►▼ Show Figures

Figure 1

36 pages, 23686 KB  
Article
Integrating Machine Learning with Hybrid and Surrogate Models to Accelerate Multiscale Modeling of Acute Respiratory Infections
by Andrey Korzin, Maria Koshkareva and Vasiliy Leonenko
Viruses 2025, 17(12), 1541; https://doi.org/10.3390/v17121541 - 25 Nov 2025
Viewed by 501
Abstract
Accurate, efficient, and explainable modeling of the dynamics of acute respiratory infections (ARIs) remains, in many aspects, a significant challenge. While compartmental models such as SIR (Susceptible–Infected–Recovered) remain widely used for that purpose due to their simplicity, they cannot capture the complicated multiscale [...] Read more.
Accurate, efficient, and explainable modeling of the dynamics of acute respiratory infections (ARIs) remains, in many aspects, a significant challenge. While compartmental models such as SIR (Susceptible–Infected–Recovered) remain widely used for that purpose due to their simplicity, they cannot capture the complicated multiscale nature of disease progression which unites individual-level interactions affecting the initial phase of an outbreak and mass action laws governing the disease transmission in its general phase. Individual-based models (IBMs) offer a detailed representation capable of capturing these transmission nuances but have high computational demands. In this work, we explore hybrid and surrogate approaches to accelerate forecasting of acute respiratory infection dynamics performed via detailed epidemic models. The hybrid approach combines IBMs and compartmental models, dynamically switching between them with the help of statistical and ML-based methods. The surrogate approach, on the other hand, replaces IBM simulations with trained autoencoder approximations. Our results demonstrate that the usage of machine learning techniques and hybrid modeling allows us to obtain a significant speed–up compared to the original individual-based model—up to 1.6–2 times for the hybrid approach and up to 104 times in case of a surrogate model—without compromising accuracy. Although the suggested approaches cannot fully replace the original model, under certain scenarios they make forecasting with fine-grained epidemic models much more feasible for real-time use in epidemic surveillance. Full article
(This article belongs to the Special Issue Multiscale Modeling and Forecasting of COVID-19 and Respiratory Virus Dynamics)
►▼ Show Figures

Figure 1

21 pages, 4582 KB  
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
Viewed by 1001
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
(This article belongs to the Special Issue Multiscale Modeling and Forecasting of COVID-19 and Respiratory Virus Dynamics)
►▼ Show Figures

Figure 1

13 pages, 5063 KB  
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 859
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
(This article belongs to the Special Issue Multiscale Modeling and Forecasting of COVID-19 and Respiratory Virus Dynamics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop