Comparative Biology Immune Agent-based Model

College of Medicine Given the importance and impact of pandemic viruses of bat origin, there is potential significant benefit in the comparative investigation into the differences in bat and human immune responses to viruses in terms of providing insight into what form future pandemics might manifest, and what types of potential therapies might be implemented. The practice of comparative biology can be potentially enhanced by the addition of mathematical and computational methods that can provide a means of dynamic knowledge representation to visualize and interrogate the putative differences between the two systems. Towards this end, we present an agent-based model of components of the immune system that encompasses and bridges the differences between bat and human responses to viral infection; we term this model the Comparative Biology Immune Agent-based Model, or CBIABM. To our knowledge this is the first mechanism-based computational/mathematical model that seeks to directly compare bat and human immune mechanisms and the consequences of those mechanistic differences, namely inflammasome activity and differences in Type 1 Interferon dynamics, in terms of resistance to viral infection. Simulation experiments with the CBIABM demonstrate the efficacy of bat-related changes of impaired inflammasome activation and constitutive production of Type 1 Interferons in conferring viral resistance. Furthermore, simulation studies suggest a crucial role of endothelial inflammasome activity as a mechanism for systemic bat viral resistance and the clinical manifestations and severity of disease in human viral infections. Future work will involve the additional of adaptive immune features to this initial version of the CBIABM and the incorporation of features and properties of specific viruses. We hope that this initial study will help inspire additional comparative modeling projects that use computational dynamic knowledge representation to link, compare, and contrast immunological functions shared across different species, and in so doing, provide insight and preparation for responses to future viral pandemics of zoonotic origin. Estimation of Epidemiological Key Parameters Using Viral Dynamics Model Keisuke Ejima Indiana University Bloomington Viral dynamics models have extensively been used in mathematical biology. The models helped us understand quantitative and qualitative characteristics of temporal dynamics of viral load. Recently, the models are used in epidemiological and clinical studies. Since COVID-19 pandemic started, our group has been exploring the utility of the models in providing implication for public health practice. Particularly, the model was used to estimate the following two key epidemiological parameters: incubation period and false-negative rate of PCR tests. Additionally, we demonstrated the utility of the model in distinguishing imported cases and locally infected cases at early phase of the pandemic. The model was also used to compute the sample size for clinical trials of antiviral treatment and design guideline to determine when to end isolation of COVID-19 patients. I would like to argue the possibility of collecting longitudinal viral load data and further application of the models to epidemiological and clinical studies. If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/