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Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens (UGA), GA 30602, USA
Department of Biology, Clarkson University, Potsdam, NY 13699, USA
Escherichia coli Group, Institute—Foundation of Health Research (FIDIS)—University Hospital Complex of Santiago de Compostela University (CHUS), 15706 Santiago de Compostela, Spain
Division of Infection Medicine, Department of Clinical Sciences, Lund University, 221 84 Lund, Sweden
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(12), 2486;
Received: 12 March 2019 / Revised: 23 May 2019 / Accepted: 12 June 2019 / Published: 18 June 2019
(This article belongs to the Section Applied Biosciences and Bioengineering)
PDF [1349 KB, uploaded 18 June 2019]


Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination. View Full-Text
Keywords: health engineering; mathematic models; antimicrobial resistance; infection disease health engineering; mathematic models; antimicrobial resistance; infection disease

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Cartelle Gestal, M.; Dedloff, M.R.; Torres-Sangiao, E. Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread. Appl. Sci. 2019, 9, 2486.

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