Leveraging Computational Modeling to Understand Infectious Diseases

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.

COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes

“…Because the identification of immune mechanisms responsible for divergent disease outcomes can be difficult clinically, and experimental models and longitudinal data are only beginning to emerge, theoretical explorations are ideal [31]. Quantitative approaches combining mechanistic disease modelling and computational strategies are being increasingly leveraged to investigate inter-and intra-patient variability by, for example, developing virtual clinical trials [32][33][34].…”

COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes

“…Because identifying which immune mechanisms lead to divergent outcomes can be difficult clinically, and experimental models and longitudinal data are only beginning to emerge, theoretical explorations are ideal [25]. Quantitative approaches combining mechanistic disease modelling and computational strategies are being increasingly leveraged to investigate inter-and intra-patient variability by, for example, developing virtual clinical trials [26][27][28].…”

Quantifying dose-, strain-, and tissue-specific kinetics of parainfluenza virus infection

“…Mathematical models are useful to understand and quantify in vivo kinetics of a myriad of viral infections and have been used to analyze other respiratory viruses like influenza A virus (IAV) (reviewed in [6,[17][18][19]), respiratory syncytial virus (RSV) [20][21][22] and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [23][24][25][26]. A strength of these models is that they can estimate the rates of infection that are not easily measured within the laboratory or clinic (e.g., virus production and infected cell half-life) and define the primary infection processes that drive differing kinetics (e.g., between strains or doses; e.g., as in [27,28]) in addition to their magnitude.…”