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Shabman RS, Craig M, Laubenbacher R, Reeves D, Brown LL. NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases. Bull Math Biol 2024; 86:44. [PMID: 38512541 PMCID: PMC10957590 DOI: 10.1007/s11538-024-01276-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/23/2024]
Abstract
On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.
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Affiliation(s)
- Reed S Shabman
- National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA.
| | - Morgan Craig
- Department of Mathematics and Statistics, Sainte-Justine University Hospital Research Centre, Université de Montréal, Montreal, Canada
| | | | - Daniel Reeves
- Department of Global Health, University of Washington, Seattle, WA, 98195, USA
| | - Liliana L Brown
- National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA.
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Roy S, Biswas P, Ghosh P. Determining the rate of infectious disease testing through contagion potential. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002229. [PMID: 37531354 PMCID: PMC10395932 DOI: 10.1371/journal.pgph.0002229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 07/06/2023] [Indexed: 08/04/2023]
Abstract
The emergence of new strains, varying in transmissibility, virulence, and presentation, makes the existing epidemiological statistics an inadequate representation of COVID-19 contagion. Asymptomatic individuals continue to act as carriers for the elderly and immunocompromised, making the timing and extent of vaccination and testing extremely critical in curbing contagion. In our earlier work, we proposed contagion potential (CP) as a measure of the infectivity of an individual in terms of their contact with other infectious individuals. Here we extend the idea of CP at the level of a geographical region (termed a zone). We estimate CP in a spatiotemporal model based on infection spread through social mixing as well as SIR epidemic model optimization, under varying conditions of virus strains, reinfection, and superspreader events. We perform experiments on the real daily infection dataset at the country level (Italy and Germany) and state level (New York City, USA). Our analysis shows that CP can effectively assess the number of untested (and asymptomatic) infected and inform the necessary testing rates. Finally, we show through simulations that CP can trace the evolution of the infectivity profiles of zones due to the combination of inter-zonal mobility, vaccination policy, and testing rates in real-world scenarios.
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Affiliation(s)
- Satyaki Roy
- Bioinformatics & Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, United States of America
| | - Preetom Biswas
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States of America
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States of America
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Gómez MC, Rubio FA, Mondragón EI. Qualitative analysis of generalized multistage epidemic model with immigration. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:15765-15780. [PMID: 37919988 DOI: 10.3934/mbe.2023702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
A model with multiple disease stages is discussed; its main feature is that it considers a general incidence rate, functions for death and immigration rates in all populations. We show via a suitable Lyapunov function that the unique endemic equilibrium is globally asymptotically stable. We conclude that, in order to obtain the existence and global stability of the equilibrium point of general models, conditions must be imposed on the functions present in the model. In addition, the model has no basic reproduction number due to the constant flow of infected people, which makes its eradication impossible; therefore, there is no equilibrium point free of infection.
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Affiliation(s)
- Miller Cerón Gómez
- Department of Mathematics and Statistics, University of Nariño, Pasto, Nariño, Colombia
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Chin JL, Chan LC, Yeaman MR, Meyer AS. Tensor-based insights into systems immunity and infectious disease. Trends Immunol 2023; 44:329-332. [PMID: 36997459 PMCID: PMC10411872 DOI: 10.1016/j.it.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/31/2023]
Abstract
Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.
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Affiliation(s)
- Jackson L Chin
- Department of Bioengineering, University of California Los Angeles (UCLA), Los Angeles, CA 90024, USA
| | - Liana C Chan
- The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA 90502, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA; Division of Infectious Diseases, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, USA; Division of Molecular Medicine, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Michael R Yeaman
- The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA 90502, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA; Division of Infectious Diseases, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, USA; Division of Molecular Medicine, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Aaron S Meyer
- Department of Bioengineering, University of California Los Angeles (UCLA), Los Angeles, CA 90024, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90024, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA 90024, USA.
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Bafandeh S, Khodadadi E, Ganbarov K, Asgharzadeh M, Köse Ş, Samadi Kafil H. Natural Products as a Potential Source of Promising Therapeutics for COVID-19 and Viral Diseases. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2023; 2023:5525165. [PMID: 37096202 PMCID: PMC10122587 DOI: 10.1155/2023/5525165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/19/2023] [Accepted: 03/24/2023] [Indexed: 04/26/2023]
Abstract
Background A global pandemic has recently been observed due to the new coronavirus disease, caused by SARS-CoV-2. Since there are currently no antiviral medicines to combat the highly contagious and lethal COVID-19 infection, identifying natural sources that can either be viricidal or boost the immune system and aid in the fight against the disease can be an essential therapeutic support. Methods This review was conducted based on published papers related to the herbal therapy of COVID-19 by search on databases including PubMed and Scopus with herbal, COVID-19, SARS-CoV-2, and therapy keywords. Results To combat this condition, people may benefit from the therapeutic properties of medicinal plants, such as increasing their immune system or providing an antiviral impact. As a result, SARS-CoV-2 infection death rates can be reduced. Various traditional medicinal plants and their bioactive components, such as COVID-19, are summarized in this article to assist in gathering and debating techniques for combating microbial diseases in general and boosting our immune system in particular. Conclusion The immune system benefits from natural products and many of these play a role in activating antibody creation, maturation of immune cells, and stimulation of innate and adaptive immune responses. The lack of particular antivirals for SARS-CoV-2 means that apitherapy might be a viable option for reducing the hazards associated with COVID-19 in the absence of specific antivirals.
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Affiliation(s)
- Soheila Bafandeh
- Research Center for Pharmaceutical Nanotechnology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ehsaneh Khodadadi
- Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA
| | - Khudaverdi Ganbarov
- Research Laboratory of Microbiology and Virology, Baku State University, Baku, Azerbaijan
| | - Mohammad Asgharzadeh
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Şükran Köse
- Department of Infectious Diseases and Clinical Microbiology, Dokuz Eylül Üniversitesi, Izmir, Turkey
| | - Hossein Samadi Kafil
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
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Semendyaeva NL, Orlov MV, Rui T, Enping Y. Analytical and Numerical Investigation of the SIR Mathematical Model. COMPUTATIONAL MATHEMATICS AND MODELING 2023. [PMCID: PMC10074335 DOI: 10.1007/s10598-023-09572-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
This is a theoretical study of the SIR model — a popular mathematical model of the propagation of infectious diseases. We construct a solution of the Cauchy problem for a system of two ordinary differential equations describing in integral form the concentration dynamics of infected and recovered individuals in an immune population. A qualitative analysis is carried out of the stationary system states using the Lyapunov function. An expression is obtained for the coordinates of the equilibrium points in terms of the Lambert W-function for arbitrary initial values. The application of the SIR model for the description of COVID-19 propagation dynamic is demonstrated.
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Affiliation(s)
- N. L. Semendyaeva
- grid.14476.300000 0001 2342 9668Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
- Faculty of Computational Mathematics and Cybernetics, Shenzhen MSU-BIT University, Shenzhen, China
| | - M. V. Orlov
- grid.14476.300000 0001 2342 9668Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
| | - Tang Rui
- Faculty of Computational Mathematics and Cybernetics, Shenzhen MSU-BIT University, Shenzhen, China
| | - Yang Enping
- Faculty of Computational Mathematics and Cybernetics, Shenzhen MSU-BIT University, Shenzhen, China
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Hoerter A, Arnett E, Schlesinger LS, Pienaar E. Systems biology approaches to investigate the role of granulomas in TB-HIV coinfection. Front Immunol 2022; 13:1014515. [PMID: 36405707 PMCID: PMC9670175 DOI: 10.3389/fimmu.2022.1014515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/20/2022] [Indexed: 09/29/2023] Open
Abstract
The risk of active tuberculosis disease is 15-21 times higher in those coinfected with human immunodeficiency virus-1 (HIV) compared to tuberculosis alone, and tuberculosis is the leading cause of death in HIV+ individuals. Mechanisms driving synergy between Mycobacterium tuberculosis (Mtb) and HIV during coinfection include: disruption of cytokine balances, impairment of innate and adaptive immune cell functionality, and Mtb-induced increase in HIV viral loads. Tuberculosis granulomas are the interface of host-pathogen interactions. Thus, granuloma-based research elucidating the role and relative impact of coinfection mechanisms within Mtb granulomas could inform cohesive treatments that target both pathogens simultaneously. We review known interactions between Mtb and HIV, and discuss how the structure, function and development of the granuloma microenvironment create a positive feedback loop favoring pathogen expansion and interaction. We also identify key outstanding questions and highlight how coupling computational modeling with in vitro and in vivo efforts could accelerate Mtb-HIV coinfection discoveries.
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Affiliation(s)
- Alexis Hoerter
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Eusondia Arnett
- Host-Pathogen Interactions Program, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Larry S. Schlesinger
- Host-Pathogen Interactions Program, Texas Biomedical Research Institute, San Antonio, TX, United States
| | - Elsje Pienaar
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, United States
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Sood SK, Rawat KS, Kumar D. Analytical mapping of information and communication technology in emerging infectious diseases using CiteSpace. TELEMATICS AND INFORMATICS 2022; 69:101796. [PMID: 35282387 PMCID: PMC8901238 DOI: 10.1016/j.tele.2022.101796] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 01/24/2022] [Accepted: 02/28/2022] [Indexed: 11/05/2022]
Abstract
The prevalence of severe infectious diseases has become a major global health concern. Currently, the COVID-19 outbreak has spread across the world and has created an unprecedented humanitarian crisis. The proliferation of novel viruses has put traditional health systems under immense pressure and posed several serious issues. Henceforth, early detection, identification, rapid testing, and advanced surveillance systems are required to address public health emergencies. However, Information and Communication Technology (ICT) tackles several issues raised by this pandemic and significantly improves the quality of services in the health care sector. This paper presents an ICT-assisted scientometric analysis of infectious diseases, namely, airborne, food & waterborne, fomite-borne, sexually transmitted illnesses, and vector-borne illnesses. It assesses the international research status of this field in terms of citation structure, prolific journals, and country contributions. It has used the CiteSpace tool to address the visualization needs and in-depth insights of scientific literature to pinpoint core hotspots, research frontiers, emerging research areas, and ICT trends. The research finding reveals that mobile apps, telemedicine, and artificial intelligence technologies have greater scope to reduce the threats of infectious diseases. COVID-19, influenza, HIV, and malaria viruses have been identified as research hotspots whereas COVID-19, contact tracing applications, security and privacy concerns about users' data are the recent challenges in this field that need to address. The United States has produced higher research output in all domains of infectious diseases. Furthermore, it explores the co-occurrence network analysis and intellectual landscape of each domain of infectious diseases. It provides potential research directions and insightful clues to researchers and the academic fraternity for further research.
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Affiliation(s)
- Sandeep Kumar Sood
- Department of Computer Aplications, National Institute of Technology, Kurukshetra, Haryana 136119, India
| | - Keshav Singh Rawat
- Department of Computer Science and Informatics, Central University of Himachal Pradesh, Dharmashala, Himachal Pradesh 176215, India
| | - Dheeraj Kumar
- Department of Computer Science and Informatics, Central University of Himachal Pradesh, Dharmashala, Himachal Pradesh 176215, India,Corresponding author
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Shou Y, Johnson SC, Quek YJ, Li X, Tay A. Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system. Mater Today Bio 2022; 14:100269. [PMID: 35514433 PMCID: PMC9062348 DOI: 10.1016/j.mtbio.2022.100269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022]
Abstract
The lymph node (LN) is a vital organ of the lymphatic and immune system that enables timely detection, response, and clearance of harmful substances from the body. Each LN comprises of distinct substructures, which host a plethora of immune cell types working in tandem to coordinate complex innate and adaptive immune responses. An improved understanding of LN biology could facilitate treatment in LN-associated pathologies and immunotherapeutic interventions, yet at present, animal models, which often have poor physiological relevance, are the most popular experimental platforms. Emerging biomaterial engineering offers powerful alternatives, with the potential to circumvent limitations of animal models, for in-depth characterization and engineering of the lymphatic and adaptive immune system. In addition, mathematical and computational approaches, particularly in the current age of big data research, are reliable tools to verify and complement biomaterial works. In this review, we first discuss the importance of lymph node in immunity protection followed by recent advances using biomaterials to create in vitro/vivo LN-mimicking models to recreate the lymphoid tissue microstructure and microenvironment, as well as to describe the related immuno-functionality for biological investigation. We also explore the great potential of mathematical and computational models to serve as in silico supports. Furthermore, we suggest how both in vitro/vivo and in silico approaches can be integrated to strengthen basic patho-biological research, translational drug screening and clinical personalized therapies. We hope that this review will promote synergistic collaborations to accelerate progress of LN-mimicking systems to enhance understanding of immuno-complexity.
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Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2. Viruses 2022; 14:v14030605. [PMID: 35337012 PMCID: PMC8953050 DOI: 10.3390/v14030605] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/06/2023] Open
Abstract
We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.
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Abstract
PURPOSE OF REVIEW Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. RECENT FINDINGS The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. SUMMARY As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.
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Mokhtari A, Mineo C, Kriseman J, Kremer P, Neal L, Larson J. A multi-method approach to modeling COVID-19 disease dynamics in the United States. Sci Rep 2021; 11:12426. [PMID: 34127757 PMCID: PMC8203660 DOI: 10.1038/s41598-021-92000-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/01/2021] [Indexed: 12/12/2022] Open
Abstract
In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.
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Affiliation(s)
- Amir Mokhtari
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA.
| | - Cameron Mineo
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - Jeffrey Kriseman
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - Pedro Kremer
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - Lauren Neal
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - John Larson
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
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Jenner AL, Aogo RA, Alfonso S, Crowe V, Smith AP, Morel PA, Davis CL, Smith AM, Craig M. COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.01.05.425420. [PMID: 33442689 PMCID: PMC7805446 DOI: 10.1101/2021.01.05.425420] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8 + T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. AUTHOR SUMMARY Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.
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Affiliation(s)
- Adrianne L. Jenner
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Rosemary A. Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Sofia Alfonso
- Department of Physiology, McGill University, Montréal, Québec, Canada
| | - Vivienne Crowe
- Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada
| | - Amanda P. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Penelope A. Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Courtney L. Davis
- Natural Science Division, Pepperdine University, Malibu, California, USA
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Morgan Craig
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
- Department of Physiology, McGill University, Montréal, Québec, Canada
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