Mathematical model to simulate SARS-CoV-2 infection within-host

模拟宿主内 SARS-CoV-2 感染的数学模型

• 批准号: EP/W007355/1
• 负责人: Ruth Bowness
• 金额: $ 9.96万
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW007355%2F1
• 关键词: Mathematical; model; simulate; SARS; CoV

Mathematical models are vital in advising strategy when dealing with pandemics, from helping to develop individual treatment strategies to guiding the national public health approach. Current modelling efforts concentrate on transmission and do not focus on variation within the host. There is clear evidence that some subgroups of patients are likely to have more severe disease and poorer outcomes. The reasons for these associations are not clear. We have developed a within-host mathematical and computational model of SARS-CoV-2 infection that is capable of simulating viral spread in lung cells. Preliminary results illustrate how our model is able to study, in isolation, particular immune dysfunctions associated with severe COVID-19. This is difficult to achieve with biological experiments. Our results have suggested that impairing the function of Natural Killer (NK) cells, important for combatting viral infections, skews the immune response in ways that cause severe disease. Additionally, our model shows that manipulating the levels of defence molecules that immune and infected cells produce to try and fight the infection can lead to severe viral infection, similar to that observed in severe COVID-19 patients. We have laid important groundwork for future code development in this project; parameterisation and validation, and application. In terms of application, we intend to investigate the influence of initial (and continual) viral load deposition (amount of virus that initiates infection) on the spread of infection. Additionally, we aim to consider more in-depth models of the production of defence molecules, known as cytokines (in particular a cytokine known as type I interferon). We will investigate their dysfunction in their regulatory pathway and their impact on the spread of infection. The model will enhance our understanding of COVID-19 pathophysiology. In this project we will integrate mathematical models that simulate drug distribution in the body. This will allow us to test alternative treatment strategies, such as various drug scheduling and dosing intervals, and refine therapy for specific subsets of patients. Many people remain vulnerable to this infection, but greater knowledge of how to deliver successful treatment strategies will provide hope for those who become critically unwell. It will also diminish the suffering of those who experience non-critical, but nonetheless unpleasant, disease. Understanding gained from our model simulations may also lead to improved management of the long-term effects of COVID-19 (long COVID).The nature of the model will allow us to investigate why different subgroups are at greater risk, and why they are perhaps most likely to become infected. This can inform public health strategy to protect the most vulnerable members of society. On completion of this project, there is scope to link our within-host model with population-level and environmental models. This could help us to understand more about the course of infectiousness in individuals, aiding guidance around self-isolation and ultimately helping to reduce transmission. It will also help to understand how and why there is heterogeneity in different subsets of patients' transmissibility.Using our mathematical framework, we will also create a mathematical tool that will allow other infectious disease researchers to model the within-host dynamics of newly emerging pathogens in the future.

数学模型对于应对流行病的策略建议至关重要，从帮助制定个体治疗策略到指导国家公共卫生方法。当前的建模工作集中于传播，而不关注宿主内的变异。有明确证据表明，某些亚组患者可能患有更严重的疾病和更差的结果。这些关联的原因尚不清楚。我们开发了一种 SARS-CoV-2 感染的宿主内数学和计算模型，能够模拟病毒在肺细胞中的传播。初步结果说明了我们的模型如何能够单独研究与严重 COVID-19 相关的特定免疫功能障碍。这是生物实验很难实现的。我们的研究结果表明，损害自然杀伤（NK）细胞的功能（对于对抗病毒感染很重要）会扭曲免疫反应，导致严重疾病。此外，我们的模型表明，操纵免疫细胞和受感染细胞产生的防御分子水平来尝试对抗感染可能会导致严重的病毒感染，类似于在严重的 COVID-19 患者中观察到的情况。 我们在这个项目中为以后的代码开发打下了重要的基础；参数化和验证以及应用。在应用方面，我们打算研究初始（和持续）病毒载量沉积（引发感染的病毒量）对感染传播的影响。此外，我们的目标是考虑更深入的防御分子产生模型，称为细胞因子（特别是称为 I 型干扰素的细胞因子）。我们将研究它们的调节途径功能障碍及其对感染传播的影响。该模型将增强我们对 COVID-19 病理生理学的理解。在这个项目中，我们将整合模拟药物在体内分布的数学模型。这将使我们能够测试替代治疗策略，例如各种药物安排和给药间隔，并针对特定患者亚群完善治疗。许多人仍然容易受到这种感染，但更多地了解如何提供成功的治疗策略将为那些病情严重的人带来希望。它还将减少那些患有非严重但仍然令人不快的疾病的人的痛苦。从我们的模型模拟中获得的理解还可能有助于改善对 COVID-19（长期 COVID）的长期影响的管理。该模型的性质将使我们能够调查为什么不同的亚组面临更大的风险，以及为什么他们最有可能被感染。这可以为保护社会最弱势成员的公共卫生战略提供信息。该项目完成后，我们可以将我们的宿主内部模型与人口水平和环境模型联系起来。这可以帮助我们更多地了解个人的传染过程，帮助指导自我隔离，并最终帮助减少传播。它还将有助于理解不同患者的传染性子集如何以及为何存在异质性。利用我们的数学框架，我们还将创建一个数学工具，使其他传染病研究人员能够在未来对新出现的病原体的宿主内动态进行建模。

项目成果

Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages

• DOI: 10.1101/2022.05.06.490883
• 发表时间: 2022-05
• 期刊: bioRxiv
• 作者: Christopher F. Rowlatt;Mark A. J. Chaplain;D. Hughes;S. Gillespie;D. Dockrell;I. Johannessen;R. Bowness