Integrating Data-Driven Insights into Mathematical Modeling of Infectious Diseases

A special issue of Tropical Medicine and Infectious Disease (ISSN 2414-6366).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 69

Special Issue Editors


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Guest Editor
Department of Mathematics and Statisctics, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa
Interests: applied mathematics; infectious disease; social modeling; mathematical modeling science

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Guest Editor
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S. Knoles Drive, P.O. Box 5693, Flagstaff, AZ 86011, USA
Interests: public health; data modeling; computational epidemiology; disease ecology; biostatistics
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Guest Editor
School of Public Health, Clarke International University, Kampala P.O.Box 7782, Uganda
Interests: epidemiology; public health; data modelling; primary healthcare

Special Issue Information

Dear Colleagues,

The modeling of infectious disease dynamics has become an indispensable tool in understanding and predicting the spread of diseases. With the rapid advancements in data collection techniques and the availability of large, complex datasets, there is now an unprecedented opportunity to enhance mathematical models with real-time data and insights. This Special Issue aims to explore the integration of data-driven methodologies into mathematical models of infectious diseases, focusing on how big data, machine learning, and advanced statistical techniques can improve the precision and predictive power of disease models.

Mathematical models have long been used to inform public health policies, understand epidemic behavior, and optimize intervention strategies. However, the integration of data-driven approaches, such as real-time epidemiological data, genetic sequencing, and environmental factors, is a critical step towards more accurate and dynamic models.

In this Special Issue, we invite contributions that investigate the fusion of data science with traditional epidemiological modeling, including but not limited to the following:

  1. Innovative Data-Driven Modeling Approaches: Exploring the use of machine learning, artificial intelligence, and other data-driven techniques to enhance the accuracy of epidemiological models, with a particular focus on predicting disease spread, identifying hotspots, and evaluating control strategies.
  2. Real-Time Surveillance and Data Integration: Examining the role of real-time data from surveillance systems (e.g., health records, mobile health data, social media) in informing and refining models of infectious diseases.
  3. Data-Driven Parameter Estimation and Model Calibration: Investigating novel methods for data assimilation, parameter estimation, and model calibration, allowing for more accurate and timely predictions of disease dynamics.
  4. Impact of Data Quality on Model Performance: Understanding how the quality and reliability of data (e.g., reporting biases, data gaps) influence the performance of mathematical models and strategies for addressing these challenges.
  5. Predictive Modeling of Emerging Infectious Diseases: Focusing on the application of data-driven models to anticipate and mitigate outbreaks of emerging infectious diseases, including modeling the risk of zoonotic disease transmission and cross-species barriers.

By combining the power of mathematical modeling with the richness of data-driven insights, this Special Issue will contribute to the ongoing evolution of predictive tools for infectious disease control. We welcome original research articles, review papers, and case studies that reflect the latest advances in the integration of data-driven methods into mathematical modeling.

Dr. Adejimi Adeniji
Dr. Kayode Oshinubi
Dr. Allan Komakech
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 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. Tropical Medicine and Infectious Disease 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 2700 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

  • mathematical modeling
  • infectious diseases
  • epidemiology
  • data-driven modeling
  • machine learning
  • predictive analytics
  • real-time surveillance
  • One Health
  • outbreak prediction
  • disease prevention

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Published Papers

This special issue is now open for submission.
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