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Modeling Viral Diseases and Pandemics for Improved Medical Treatments and Public Health Policies

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 2331

Special Issue Editor

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Department of Electrical and Computer Engineering, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854, USA
Interests: multimedia security; wireless security; wireless networking; cryptography
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Special Issue Information

Aim and Scope

The COVID-19 pandemic has highlighted the need for accurately analyzing disease propagation both locally and globally, as well as the need for a close partnership between science, medicine, and public policy. One of the critical challenges that the ongoing pandemic has illustrated is that tracking and forecasting the evolution of a viral disease is particularly challenging in the modern era, where there is increased societal dependence on domestic and global travel for trade and commerce. Further complicating the challenge is the fact that viral diseases are prone to mutational evolution, with the initial disease often leading to distinct variants possessing uniquely different disease properties and resistance profiles.

While conventional epidemiological models are an important tool for guiding how we address the spread of an epidemic, there is nevertheless a need for improved technologies that incorporate real data from diagnostic testing to adapt models to rapid and often local changes in transmission characteristics. Further, models are needed that accurately reflect the co-existence and competition between different disease variants and how these variants spread at smaller and larger scales. In order to better inform public health policies, such epidemiological forecasting models should also take into account the broad variety of therapeutics and vaccine technologies that are emerging from the pharmaceutical pipeline. Ultimately, improvements in the mathematical and computational tools being used to predict disease propagation and population susceptibility, combined with drug and vaccine distribution strategies, will allow public health officials to tailor their own domestic health measures to thwart disease spread both locally and in the context of regional and global implications.

The purpose of the IJERPH Special Issue on “Modeling Viral Diseases and Pandemics for Improved Medical Treatments and Public Health Policies” is to serve as a venue that will improve information exchange between science, medicine, and public policy—ultimately empowering the control and mitigation of current and future viral diseases.           

Topics of Interest include (but are not limited to):

  • Models and forecasting tools for predicting viral disease spread;
  • Models and forecasting tools that capture disease variant dynamics;
  • Using epidemiological modeling to arrive at improved COVID-19 vaccination strategies;
  • Integrating pharmaceutical therapeutics (antivirals, antibodies, etc.) in modeling disease propagation and severity;
  • Integrating effects of public health control measures with forecasts;
  • Estimating health benefits versus economic impacts of public health control measures;
  • Population stratification methods for improved viral propagation modeling;
  • Inverse application of epidemiological models (e.g., using models to quantify asymptomatic carriers);
  • Forecasting and epidemiological control for target objectives (surge delay, hospitalization, ICU bed availability, etc.);
  • Integration of semi-real time testing data for model refinement and forecast updates;
  • Integration of public transportation data to improve local disease spread models;
  • Design of public health policies and decisions based on epidemiological models;
  • COVID-19 public health policies, supporting data analysis, and best practices;
  • Modeling disease propagation at the “small-scale” (e.g., disease spread in office buildings);
  • Methods for mitigating viral spread in office buildings and public spaces.

Submission Process

Articles submitted to the issue must contain significant relevance to scientific, computational modeling and/or public health policy related to viral epidemics. All submissions will be peer reviewed according to the IJERPH guidelines (https://www.mdpi.com/journal/ijerph/instructions) .  Submitted articles should not have been published or under review elsewhere. Submissions to this special issue IJERPH may include research articles, survey articles providing significant tutorial value, or public health policies and supporting data.  Prospective authors should consult the site for guidelines and information on paper submission.

Prof. Dr. Wade Trappe
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. International Journal of Environmental Research and Public Health 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 2500 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.


Applied epidemiology
Descriptive epidemiology
Modeling disease spread
Viral infection
Environmental factors
Disease-related cluster analysis
Antivirals and therapeutics
Local disease spread
Public health policy
Contact tracing
Public health surveillance
Data-driven epidemic forecasting
Hyperendemic disease
Herd Immunity
Viral Infectivity

Published Papers (1 paper)

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28 pages, 2130 KiB  
Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19
by Vahideh Vakil and Wade Trappe
Int. J. Environ. Res. Public Health 2022, 19(8), 4541; https://doi.org/10.3390/ijerph19084541 - 9 Apr 2022
Cited by 3 | Viewed by 1667
The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public [...] Read more.
The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic’s trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant. Full article
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