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Viruses
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Infectious Disease Epidemiology and Transmission Dynamics: 3rd Edition
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Infectious Disease Epidemiology and Transmission Dynamics: 3rd Edition

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

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 2510

Special Issue Editors

Dr. Zhanwei Du

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Guest Editor
School of Public Health, University of Hong Kong, Hong Kong, China
Interests: computational epidemiology; viral epidemiology; infectious diseases
Special Issues, Collections and Topics in MDPI journals
Dr. Lin Wang

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Guest Editor
Department of Genetics, University of Cambridge, Cambridge, UK
Interests: infectious disease dynamics; epidemiology; Bayesian modeling; machine learning; immunology; pathogen evolution
Special Issues, Collections and Topics in MDPI journals
Dr. Zeynep Ertem

E-Mail Website
Guest Editor
Systems Science and Industrial Engineering Department, Thomas J. Watson College of Engineering and Applied Science, State University of New York at Binghamton, Binghamton, NY 13902, USA
Interests: infectious disease modeling; healthcare analytics; operations research; medical decision making; outcomes research; computational biology; public health
Special Issues, Collections and Topics in MDPI journals
Dr. Jose Luis Herrera Diestra

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Guest Editor
Department of Integrative Biology, College of Natural Science, University of Texas at Austin, Austin, TX, USA
Interests: mathematical modeling of infectious diseases; contact network epidemiology; network science; computational biology
Special Issues, Collections and Topics in MDPI journals
Dr. Yuan Bai

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Guest Editor
School of Public Health, University of Hong Kong, Hong Kong, China
Interests: computational epidemiology; viral epidemiology; infectious diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To understand the epidemiology and transmission dynamics of infectious diseases (e.g., COVID-19, influenza, respiratory syncytial virus, arboviruses, human papillomavirus, and human immunodeficiency virus), epidemiologists and mathematical modelers are continuously developing new methods to characterize transmission patterns and transmission mechanisms, estimate infection and disease burdens, reconstruct transmission history, forecast disease trends and healthcare demands, and evaluate the effectiveness or cost-effectiveness of intervention policies (e.g., mass testing, vaccination champions, and stockpiling of antivirals). This Special Issue of Viruses welcomes submissions of epidemiological and infectious disease modeling studies. In particular, research that is relevant to the evaluation of the epidemiological and/or economic impacts of ramping up treatments using mass testing, existing or next-generation antivirals, and vaccinations is welcome.

This Special Issue aims to explore different research areas and to collect articles that focus on infectious disease epidemiology and transmission dynamics. Furthermore, we expect to gain more insight into the applications of such approaches in various areas such as public health, health economics, health informatics, evolution, immunity, and medical affairs.

We encourage the submission of high-quality original research, reviews, protocols, and perspective articles to this Special Issue. Areas of interest include, but are not limited to, the following topics:

  • Infectious disease epidemiology and transmission dynamics;
  • Spatial and temporal transmission patterns of infectious diseases;
  • Effects of pharmaceutical interventions such as mass testing, vaccines, or antivirals;
  • Inference of key epidemiological parameters;
  • Phylogenetics and phylodynamics;
  • Seroepidemiology.

Dr. Zhanwei Du
Dr. Lin Wang
Dr. Zeynep Ertem
Dr. Jose Luis Herrera Diestra
Dr. Yuan Bai
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

  • infectious disease epidemiology
  • mathematical modeling
  • computational epidemiology
  • pharmaceutical interventions (e.g., mass testing, vaccination and antiviral)
  • infection and fatality burden
  • serology
  • phylogenetics and phylodynamics

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Related Special Issues

Published Papers (2 papers)

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Research

12 pages, 2007 KB  
Article
An Assessment of Regional Genetic Diversity of HIV-1
by Anastasiia Antonova, Anna V. Kuznetsova, Anna I. Kuznetsova, Aleksei Mazus, Ekaterina Loifman, Liudmila Grigoreva, Denis Kleimenov, Evgeniia Bykonia, Dmitry Shcheblyakov, Irina Favorskaya, Andrei Pochtovyi, Elena Tsyganova, Inna Kulikova, Andrei Plutnitskii, Vladimir Gushchin and Aleksandr Gintsburg
Viruses 2025, 17(12), 1568; https://doi.org/10.3390/v17121568 - 30 Nov 2025
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Abstract
This study aimed to assess the genetic diversity of HIV-1 in the Far Eastern Federal District (Russia) to implement effective anti-epidemic measures, including the development of an anti-HIV vaccine and the selection of optimal antigens. The first stage of the study included an [...] Read more.
This study aimed to assess the genetic diversity of HIV-1 in the Far Eastern Federal District (Russia) to implement effective anti-epidemic measures, including the development of an anti-HIV vaccine and the selection of optimal antigens. The first stage of the study included an analysis of HIV-1 nucleotide sequences obtained in Khabarovsk city from 2022 to 2024. The second stage of the study included an additional download of nucleotide sequences from the Los Alamos HIV Sequence Database for phylogenetic cluster analysis. Additionally, an analysis of drug resistance mutations was conducted. The results showed the following distribution of HIV-1 genetic variants: A6—72.15%, CRF63—10.13%, URFs—7.59%, C—5.06%, B—3.8%, and CRF157—1.27%. The phylogenetic cluster analysis revealed a statistically significant difference in the number of clusters depending on the genetic variant. Among drug resistance mutations (DRMs), those associated with nucleoside reverse transcriptase inhibitors (NRTIs) were the most frequently observed, accounting for 55.7% (95% CI: 44.75%—66.65%). The most commonly detected NRTI DRMs were A62V (43.04%) and M184V (13.92%). The results of this study highlight several important indicators for public health, particularly in the development of vaccines aimed at combating HIV infection. Full article
(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics: 3rd Edition)
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15 pages, 1502 KB  
Article
Impact of Viral Co-Detection on the Within-Host Viral Diversity of Influenza Patients
by Su Myat Han, Yoshiano Kubo, Alexis Robert, Marc Baguelin and Koya Ariyoshi
Viruses 2025, 17(2), 152; https://doi.org/10.3390/v17020152 - 23 Jan 2025
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Abstract
Numerous studies have documented the evidence of virus–virus interactions at the population, host, and cellular levels. However, the impact of these interactions on the within-host diversity of influenza viral populations remains unexplored. Our study identified 13 respiratory viral pathogens from the nasopharyngeal swab [...] Read more.
Numerous studies have documented the evidence of virus–virus interactions at the population, host, and cellular levels. However, the impact of these interactions on the within-host diversity of influenza viral populations remains unexplored. Our study identified 13 respiratory viral pathogens from the nasopharyngeal swab samples (NPSs) of influenza-like-illness (ILI) patients during the 2012/13 influenza season using multiplex RT-PCR. Subsequent next-generation sequencing (NGS) of RT-PCR-confirmed influenza A infections revealed all samples as subtype A/H3N2. Out of the 2305 samples tested, 538 (23.3%) were positive for the influenza A virus (IAV), while rhinovirus (RV) and adenoviruses (Adv) were detected in 264 (11.5%) and 44 (1.9%) samples, respectively. Among these, the co-detection of more than one virus was observed in ninety-six samples, and five samples showed co-detections involving more than two viruses. The most frequent viral co-detection was IAV–RV, identified in 48 out of the 96 co-detection cases. Of the total samples, 150 were processed for whole-genome sequencing (WGS), and 132 met the criteria for intra-host single-nucleotide variant (iSNV) calling. Across the genome, 397 unique iSNVs were identified, with most samples containing fewer than five iSNVs at frequencies below 10%. Seven samples had no detectable iSNVs. Notably, the majority of iSNVs (86%) were unique and rarely shared across samples. We conducted a negative binomial regression analysis to examine factors associated with the number of iSNVs detected within hosts. Two age groups—elderly individuals (>64 years old) and school-aged children (6–18 years old)—were significantly associated with higher iSNV counts, with incidence rate ratios (IRR) of 1.80 (95% confidence interval [CI]: 1.09–3.06) and 1.38 (95% CI: 1.01–1.90), respectively. Our findings suggest a minor or negligible contribution of these viral co-detections to the evolution of influenza viruses. However, the data available in this study may not be exhaustive, warranting further, more in-depth investigations to conclusively determine the impact of virus–virus interactions on influenza virus genetic diversity. Full article
(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics: 3rd Edition)
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