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Computers
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Data Science, Statistical Modelling, and ICT Applications for Global Health...
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Data Science, Statistical Modelling, and ICT Applications for Global Health and Epidemiological Resilience

A Special Issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 1 November 2026 | Viewed by 1804

Editors

Dr. Luiz Paulo Fávero

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Guest Editor
School of Economics, Business Administration and Accounting, University of São Paulo, São Paulo, Brazil
Interests: data science; machine learning; analytics; finance and economics
Prof. Dr. Marcos Dos Santos

E-Mail Website
Guest Editor
Operations Research Department, Naval College, Rio de Janeiro 20021-010, Brazil
Interests: involve artificial intelligence, machine learning, and operational research applied to strategic decision-support systems; focusing on developing and implementing multicriteria decision models, predictive algorithms, and uncertainty-handling techniques tailored to real-world problems; within the marine domain, issues related to naval sensor analysis, maritime traffic monitoring, ocean operations safety, and the assessment of technological vulnerabilities in maritime systems; interested in the use of oceanographic data, detection systems, risk modeling, and resource optimization to enhance marine-engineering processes
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Ângelo Lellis Moreira

E-Mail Website
Guest Editor
Operations Research Department, Fluminense Federal University (UFF), Niterói 24020-007, Brazil
Interests: application of operational research, machine learning, and artificial intelligence to support decision-making in complex environments; working with multicriteria models, dimensionality-reduction techniques, data-driven frameworks, and hybrid analytical approaches for strategic analysis; particularly interested in applying these methods to marine and naval problems, such as vessel performance analysis, naval systems operation, ocean monitoring, and risk assessment in maritime environments; exploring AI-based solutions for navigation, maritime safety, naval logistics, and strategic planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing complexity of global health challenges demands the integration of data science, statistical modelling, and information and communication technologies (ICT) to enhance our ability to understand, monitor, and respond to health-related phenomena worldwide. From emerging infectious diseases to chronic health conditions and socioeconomic disparities, the combination of quantitative methods and computational intelligence is indispensable for evidence-based policy, risk assessment, and resource optimization.

This Special Issue of Computers welcomes original research articles, methodological studies, reviews, and case applications addressing the intersection of mathematics, computing, and health sciences. We encourage submissions that employ innovative statistical, computational, and data-driven approaches to improve health outcomes, resilience, and preparedness for epidemiological and systemic emergencies.

Potential topics include, but are not limited to, the following:

  • Predictive and causal modelling for epidemiological surveillance and risk forecasting;
  • Statistical and computational models for early detection and mitigation of health crises;
  • Machine learning and AI applications in healthcare analytics, diagnostics, and prevention;
  • ICT-based platforms for global health monitoring, telemedicine, interoperability, and data integration;
  • Socioeconomic and demographic determinants of health and disease propagation;
  • Multilevel, Bayesian, and longitudinal modelling in population health studies;
  • Big data analytics and decision support systems in healthcare management and policy;
  • Simulation, optimization, and digital twins for healthcare system design and capacity planning;
  • Integration of statistical modelling and ICT for sustainable, data-driven public health strategies.

By fostering a multidisciplinary dialogue among statisticians, data scientists, epidemiologists, computer scientists, and public health experts, this Special Issue aims to advance the global understanding of health systems through quantitative innovation and digital transformation. We particularly encourage submissions with cross-country data, as well as ICT-enabled solutions for resilient, inclusive, and technology-driven health systems.

Dr. Luiz Paulo Fávero
Prof. Dr. Marcos Dos Santos
Dr. Miguel Ângelo Lellis Moreira
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers 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 1800 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

  • data science in healthcare
  • epidemiological modelling
  • ICT for health
  • health data analytics
  • artificial intelligence and machine learning
  • global health and resilience
  • statistical modelling and decision support

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

Published Papers (2 papers)

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18 pages, 2632 KB  
Article
Vaccine Perception on Digital Platforms: Topic Modeling of YouTube Comments
by Uğurcan Sert, Esra Ersoy, Ömür Tosun and Irmak Hatıpoğlu
Computers 2026, 15(6), 360; https://doi.org/10.3390/computers15060360 - 3 Jun 2026
Viewed by 537
Abstract
Vaccination stands as a preeminent public health measure in the fight against infectious diseases, with a proven track record of significantly reducing morbidity and mortality rates. However, the presence of vaccine hesitancy and misinformation, particularly evident during the course of the pandemic, has [...] Read more.
Vaccination stands as a preeminent public health measure in the fight against infectious diseases, with a proven track record of significantly reducing morbidity and mortality rates. However, the presence of vaccine hesitancy and misinformation, particularly evident during the course of the pandemic, has emerged as a significant challenge. The present study analyzes public perceptions of vaccination by examining YouTube comments on 215 vaccine-related videos, which total over 94,000 comments. Employing advanced topic modeling techniques, such as Hierarchical Dirichlet Process (hLDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), the study identifies key themes, including vaccine safety, side effects, pharmaceutical ethics, and public trust in healthcare authorities. The findings indicate that debates frequently center on political, social, and scientific concepts. Vaccine hesitancy has emerged as a pervasive global phenomenon that transcends cultural boundaries. The dissemination of misinformation regarding the efficacy of vaccines and the safety of treatments, such as ivermectin, is a prevalent phenomenon on social media platforms. This poses significant challenges to public health efforts. The subjects of child vaccination and parental standpoints are also recurring topics of concern. This study underscores the pivotal function of digital platforms such as YouTube in influencing public attitudes regarding vaccination. This underscores the necessity for targeted communication strategies, advanced digital literacy, and proactive policies by social media platforms to address misinformation and promote evidence-based information. Such precautions are imperative to sustaining elevated vaccination rates and safeguarding public health in the digital age. Full article
(This article belongs to the Special Issue Data Science, Statistical Modelling, and ICT Applications for Global Health and Epidemiological Resilience)
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29 pages, 1593 KB  
Article
COVID-19 Mortality, Human Development, and Age Across the WHO Member States: A Longitudinal Multilevel Count Data Analysis
by José Clemente Jacinto Ferreira, Ana Paula Matias Gama, Luiz Paulo Fávero, Ricardo Goulart Serra, Patrícia Belfiore, Igor Pinheiro de Araújo Costa, Miguel Ângelo Lellis Moreira, Marcos dos Santos and Wilson Tarantin Junior
Computers 2026, 15(2), 136; https://doi.org/10.3390/computers15020136 - 22 Feb 2026
Viewed by 829
Abstract
This study aims to verify whether there is a statistically significant relationship between COVID-19 mortality rates, the Human Development Index (HDI), and population age across the World Health Organisation (WHO) member states. Despite the extensive literature on COVID-19 mortality and socio-demographic indicators, few [...] Read more.
This study aims to verify whether there is a statistically significant relationship between COVID-19 mortality rates, the Human Development Index (HDI), and population age across the World Health Organisation (WHO) member states. Despite the extensive literature on COVID-19 mortality and socio-demographic indicators, few studies explicitly integrate count data diagnostics, zero-inflation mechanisms, and multilevel longitudinal modelling to jointly capture cross-country heterogeneity and temporal dynamics. This study addresses this gap by applying a structured modelling framework that combines negative binomial, zero-inflated, and multilevel regression models to the WHO country-level data. For this purpose, two different statistical techniques were applied, namely: negative binomial regression modelling, zero-inflated negative binomial type for daily temporal exposure on 20 July 2020 and 20 July 2022, before and after the application of the first dose of the COVID-19 vaccine; and multilevel regression for two-level repeated measures data. Negative binomial regression estimates indicate statistically significant positive associations between HDI, age, and COVID-19 mortality rates before the application of the first dose of the vaccine. The variance decomposition from the definition of an unconditional model indicates significant variability in the occurrences of infection and death and between countries/states over time. Full article
(This article belongs to the Special Issue Data Science, Statistical Modelling, and ICT Applications for Global Health and Epidemiological Resilience)
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