Leveraging Computational Modeling to Understand Infectious Diseases
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CURRENT STATE OF THE SCIENCE OF DISEASE MODELING (P MACKLIN, SECTION EDITOR)
Leveraging Computational Modeling to Understand Infectious Diseases Adrianne L. Jenner 1,2 & Rosemary A. Aogo 3 & Courtney L. Davis 4 & Amber M. Smith 3 & Morgan Craig 1,2 Accepted: 16 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Purpose of Review Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation. Recent Findings Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps. Summary Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics. Keywords Infectious diseases . Viruses . Parasites . Bacteria . Mathematics . Computational modeling
Introduction Infectious diseases are caused by organisms or pathogens (bacteria, viruses, fungi, or parasites). The symptoms and etiology of infectious diseases vary widely, and this
This article is part of the Topical Collection on Current State of the Science of Disease Modeling * Morgan Craig [email protected] 1
Department of Mathematics and Statistics, Pavillon André-Aisenstadt, Université de Montréal, Montréal, QC H3C 3J7, Canada
2
Sainte-Justine University Hospital Research Centre, Montreal, Canada
3
Department of Pediatrics, University of Tennessee Health Science Centre, Memphis, USA
4
Natural Science Division, Pepperdine University, Malibu, USA
translates to vastly different treatments and case fatality ratios. As such, different approaches are needed to combat each disease, putting a strain on public health resources. Mathematical and computational modeling have long been employed to combat infectious diseases and improve the understanding of their development, dispersion, and treatment [1], with
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