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.

Keywords: Bacteria; Computational modeling; Infectious diseases; Mathematics; Parasites; Viruses.

Fig. 1

Modeling infectious diseases occurs at multiple scales. At the intracellular scale, models have been used to understand the pathobiology of infectious pathogens, their replication, and within-host cellular dynamics so that more effective and targeted treatments may be developed. Left panel: Agent-based models can be used to model intracellular dynamics where the probability of infection is a function of receptors r and the movement of pathogens can be described as discrete lattice jumps with a given probability p_{i, j}. Center panel: A large majority of infectious diseases cause a significant systemic reaction and can be captured at both the tissue- and systemic-level by in-host models. ODE systems where target cells (T), infected cells (I), and virus (V) modeling infection, replication, and cell death have been used to understand within-host dynamics. Right panel: At the population level, the spread of infectious diseases within a local community to the global scale is crucial to our understanding of how diseases can be stopped and pandemics prevented. Similar modeling systems to within-host models can be used to model disease epidemiology where susceptible individuals (S) become infected (I) and then eventually recover (R)

Fig. 2

Within-host and between-host modeling of bacterial infections. (a) Davis et al. [•] developed a model for the key interactions between Shigella and the host’s humoral immune system using a detailed ODE system represented in their schematic. The distinction between bacterial pathogenesis (blue) and antibody and B cell dynamics (red) is denoted in a biological representation (left) and in the mathematical model (right). Prior to infection of epithelial cells, Shigella, S, can be removed by antibodies IgA, A, IgG, and G or engulfed by macrophages. Reprinted open access [•]. (b) Salvatore et al. [32] developed a method to study drug resistance-associated mutations from Mycobacterium tuberculosis strains causing multiple cases in a household. Households were matched by the number of antibiotics to which the household strain was resistant, the number of household contacts and the follow up time. Their case-control design presents a useful approach for assessing in vivo fitness effects of drug resistance mutations. Reprinted with permission from [32]

Fig. 3

Standard models for malaria. Khoury et al. [4] reviewed the main within-host models for malaria infections. (a) The Plasmodium falciparum life cycle begins with (A) infection in a host by an infectious mosquito. (B) Sporozoites traverse the bloodstream to the liver where they infect liver cells and replication and rupture to release merozoites into the blood. (C) Merozoites invade red blood cells (RBCs), and the parasite matures into schizont, which rupture RBCs to create more merozoites. (D) Some parasites commit to sexual development producing gametocytes (E), which are then taken up by mosquitos during a blood meal. (b) The standard ODE model of Plasmodium infection considers uninfected RBCs (U) and parasitized RBCs (P), which are created when a merozoite (M) infects an RBC. (c–d) Age-structured models are also used to model Plasmodium infection with either (c) an ODE system representing the stages of development of the parasitized RBC compartment or its equivalent partial differential equation formalism. Reprinted with permission from [4]

Fig. 4

Viral modeling in HIV and influenza. (a) Hill et al. [73] described how an augmented viral dynamics model can be used to simulate antiretroviral therapy and the evolution of drug resistance in HIV. Uninfected cells (U) become infected (I) from infection by virus (V). Infected cells can become latently infected (L) and can be reactivated to produce actively infected cells. Latently infected cells are a crucial compartment to consider for long-term therapy outcomes as temporary administration of fully suppressive therapy can falsely be predicted to cure infection. Simulating an example of a drug taken daily with 70% probability, the impact on the viral load, and R₀ for wild-type and resistant strains is significantly different over 20 days. Reprinted open access [73]. (b) Smith [91] summarized the major components of modeling viral infections, including innate and adaptive immune response (IR) dynamics. Viral dynamics are represented by an ODE viral kinetic model with an eclipse phase (I₁) and an infected cell clearance which is a function of the infection cell population. Macrophages (M) play a major role in the innacte IR clearing up virus and also producing cytokines which block the production of new virions. In the adaptive immune response, T cells (E) play a major role in the clearance of infected cells with the addition of activated B cells (B) and antibody (A) production leading to eventual viral clearance. Viral load dynamics observed in actue and chronic infections can be significantly different depending on the underlying viral and IR kinetics. Reprinted from Current Opinion in Systems Biology, Volume 12, A.M. Smith, Validated Models of Immune Response to Virus Infection, Page 47, Copyright (2018), with permission from Elsevier