Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.

Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.

The COVID-19 crisis has shown that classic sequential models for scientific research are too slow and do not easily encourage multidisciplinary scientific collaboration. The need to rapidly understand the causes of differing infection outcomes and vulnerabilities, to provide mechanistic frameworks for the interpretation of experimental and clinical data and to suggest drug and therapeutic targets and to design optimized personalized interventions all require the development of detailed predictive quantitative models of all aspects of COVID-19. Many of these models will require the use of common submodels describing specific aspects of infection ( e.g. , viral replication) but combine them in novel configurations. As a contribution to this development and as a proof-of-concept for some components of these models, we present a multi-layered 2D multiscale, multi-cell model and associated computer simulations of the infection of epithelial tissue by a virus, the proliferation and spread of the virus, the cellular immune response and tissue damage. Our initial, proof-of-concept model is built of modular components to allow it to be easily extended and adapted to describe specific viral infections, tissue types and immune responses. Immediately after a cell becomes infected, the virus replicates inside the cell. After an eclipse period, the infected cells start shedding diffusing infectious virus, infecting nearby cells, and secretes a short-diffusing cytokine signal. Neighboring cells can take up the diffusing extracellular virus and become infected. The cytokine signal calls for immune cells from a simple model of the systemic immune response. These immune cells chemotax and activate within the tissue in response to the cytokine profile. Activated immune cells can kill underlying epithelial cells directly or by secreting a short-diffusible toxic chemical. Infected cells can also die by apoptosis due to the stress of viral replication. We do not include direct cytokine mediated protective factors in the tissue or distinguish the complexity of the immune response in this simple model. Despite unrealistically fast viral production and immune response, the current base model allows us to define three parameter regimes, where the immune system rapidly controls the virus, where it controls the virus after extensive tissue damage, and where the virus escapes control and infects and kills all / cells. We can simulate a number of drug therapy concepts, like delayed rate of production of viral RNAs, reduced viral entry, and higher and lower levels of immune response, which we demonstrate with simulation results of parameter sweeps of select model parameters. From results of these sweeps, we found that successful containment of infection in simulation directly relates to inhibited viral internalization and rapid immune cell recruitment, while spread of infection occurs in simulations with fast viral internalization and slower immune response. In contrast to other simulations of viral infection, our simulated tissue demonstrates...