Cross-scale Interactions and Catastrophic Events: Towards a Predictive Model of the Spread of Vector-borne Diseases

Predicting the processes and environmental drivers of incursion and expansion of vector-borne diseases is a major challenge that needs to be met for geographically-extensive diseases leading to catastrophic events. In many cases, spread of disease is mediated by spatial and temporal heterogeneity in climate and other environmental drivers where geospatial data are increasingly available. These data can be used as part of a predictive disease ecology paradigm provided the diverse data can be synthesized and harmonized with fine-scale, highly-resolved data on vector and host responses to their environment. A multi-scale big data-model integration approach using human-guided machine learning was developed to objectively evaluate the importance of a large suite of spatially-distributed environmental variables (several hundred) to the spread of vector-borne diseases. Vesicular stomatitis (VS) and West Nile provide examples of this approach where new insights about the underlying processes driving disease dynamics were elucidated. For more information see: https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3157 https://esajournals.onlinelibrary.wiley.com/doi/10.1002/fee.2194 The Digital Patient Project C. Donald Combs, PhD, FSSH, Vice President and Dean School of Health Professions Eastern Virginia Medical School Abstract: Twenty-five years ago physiologists around the world began to work explicitly toward the creation of a virtual physiological human. Fifteen years ago, the European Union funded the Discipulus Project, which developed a roadmap toward the creation of a digital patient before the project ended in 2012. Since then, a number of research teams have continued to develop whole body simulation tools such as BioGears, HumMod and Muse. Recently, a few researchers have begun to discuss the concept of digital twinning as it might apply to health and healthcare. The Digital Patient Project intends to build on the work of these predecessors to update and expand that research. We will address questions related to the creation of a platform integrating data from the molecular to the neighborhood and community. Data used will incorporate individual patient data and as well as data gleaned from published clinical and population health research. Ultimately, the Digital Patient Platform will be deployed in the analysis, diagnosis and treatment of both individual patients and communities. 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/