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Call for papers - Computational tools for infection control

Guest Editors

Francesco Branda, PhD, Campus Bio-Medico University of Rome, Italy
Gerardo Chowell, PhD, Georgia State University School of Public Health, Atlanta, USA
Shi Zhao, PhD, Tianjin Medical University, Tianjin, China

Submission Status: Open   |   Submission Deadline: 16 January 2025


BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Computational tools for infection control.

 The collection aims to focus on the convergence of two crucial issues in healthcare: the use and integration of computational tools such as artificial intelligence into the healthcare sector and the long-standing and now-growing issue of infection control, both at the population and individual levels, including disease outbreak detection and antimicrobial resistance.

Meet the Guest Editors

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Francesco Branda, PhD, Campus Bio-Medico University of Rome, Italy

Dr Francesco Branda is an Adjunct Professor at the Faculty of Medicine and Surgery at the Campus Bio-Medico University of Rome. His research interests are diverse, spanning various domains such as data analytics, epidemic intelligence systems, and public health risk studies. To address these questions, he developed novel methods that combine techniques from mathematical modeling and statistical inference (including AI and machine learning). His work focuses on epidemiological and statistical consulting in hospital settings, applying statistical and molecular methods in clinical settings, and analyzing climate-sensitive diseases like Dengue and Chikungunya and outbreaks and pandemics such as SARS-CoV-2, Mpox, and Ebola.

Gerardo Chowell, PhD, Georgia State University School of Public Health, Atlanta, USA

Dr Chowell is a Professor of Epidemiology and Biostatistics at the School of Public Health, Georgia State University, Atlanta. He previously served as the inaugural chair of the Department of Population Health Sciences at GSU. With over 16 years of experience, Dr Chowell specializes in studying the dynamics of infectious disease transmission and control. He integrates diverse data sources with mathematical, statistical, and epidemiological methods. His interdisciplinary research approach involves developing, evaluating, and applying rigorous quantitative tools. These tools are essential for investigating the transmission dynamics of infectious diseases and for generating evidence-based forecasts of epidemic trajectories.

Shi Zhao, PhD, Tianjin Medical University, Tianjin, China

Dr Shi Zhao is currently a professor at the School of Public Health, Tianjin Medical University, China, who previously earned his PhD degree in epidemiology and biostatistics from the Chinese University of Hong Kong. He has a general research interest in computational epidemiology, and specialized in using computational tools to solve a wide range of scientific problems about emerging infectious diseases. His expertise lies in mathematical and statistical modelling of infectious disease transmission dynamics with particular interest in outbreak investigation, risk assessment, and key epidemiological parameter inference. His research works contribute to the early warning and projection of infectious disease epidemics, e.g., COVID-19, which was recognized by China CDC, US CDC, and WHO.




 

About the Collection

BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Computational tools for infection control. 

The collection aims to focus on the convergence of two crucial issues in healthcare: the use and integration of computational tools such as artificial intelligence into the healthcare sector and the long-standing and now-growing issue of infection control, both at the population and individual levels, including disease outbreak detection and antimicrobial resistance.

The most recent years have revealed the potential of promising computational tools for addressing infection control, stemming from specific technical features of a subset of algorithms that could find powerful applications. Tailored machine learning models can analyze extensive microbiological and clinical datasets to identify patterns indicative of antimicrobial resistance (AMR) or to enhance personalized antibiotic treatments, thus mitigating the prevalence of AMR. Furthermore, multiple machine learning models have been developed to expedite the detection of disease outbreaks. The wide range of promising automated technologies goes beyond AI, including monitoring sensors, tracking systems, and rapid diagnostic devices that could significantly contribute to infection prevention, management, and control.

These tools entail several global constraints and challenges, such as the availability of financial and technological resources, the need to standardize protocols and procedures to ensure interoperability of systems and ensuring the security of health data in an international context. In addition, there are ethical and regulatory issues that need to be addressed, such as patient privacy and accountability in the use of advanced technologies in healthcare.

Image credit: © Marina / Stock.adobe.com

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select ''Computational tools for infection control'' from the dropdown menu.

Articles will undergo the journal’s standard  peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.