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

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-blind 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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Computers - ISSN 2073-431X