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

数学模型在处理流行病时的建议策略中至关重要，从帮助制定个人治疗策略到指导国家公共卫生方法。目前的建模工作集中在传播，而不是集中在主机内的变化。有明确的证据表明，一些亚组的患者可能有更严重的疾病和更差的结果。这些关联的原因尚不清楚。我们已经开发了一个宿主内SARS-CoV-2感染的数学和计算模型，该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孤立地研究与严重COVID-19相关的特定免疫功能障碍。这在生物学实验中是很难实现的。我们的研究结果表明，削弱自然杀伤（NK）细胞的功能，对对抗病毒感染很重要，会以导致严重疾病的方式扭曲免疫反应。此外，我们的模型表明，操纵免疫细胞和感染细胞产生的防御分子水平来试图对抗感染可能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。 我们已经为这个项目中未来的代码开发奠定了重要的基础;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（启动感染的病毒量）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的生产模型，称为细胞因子（特别是称为I型干扰素的细胞因子）。我们将研究它们在调节途径中的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，例如各种药物调度和给药间隔，并为特定的患者子集改进治疗。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严重不适的人带来希望。它还将减少那些经历非关键但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也可能有助于改善对COVID-19（长期COVID）长期影响的管理。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，以及为什么他们可能最有可能被感染。这可以为公共卫生战略提供信息，以保护社会中最脆弱的成员。该项目完成后，有可能将我们的宿主内模型与人口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。这也将有助于理解如何以及为什么在不同的患者的传染性子集的异质性。使用我们的数学框架，我们还将创建一个数学工具，将允许其他传染病研究人员在未来模拟新出现的病原体的宿主内动力学。

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.