April 9, 2020 at 01:00PM- April 9, 2020 at 02:30PM
Part I: Spatial Analytics, Presented by Mo Chen
Spatial analysis plays an important role not only in our everyday life and business, but also in the fight against the ongoing coronavirus outbreak. In this webinar we will see how the concept of spatial analysis was sparked due to an epidemic event in history. We will give an overview of spatiotemporal datasets, which serve as the foundation of almost all spatial analysis including RMDS’ Project Coronavirus. Attendees will also have a chance to see how mapping acts as a powerful tool in visualizing and informing the trend of coronavirus worldwide. Lastly, some examples will be shown to illustrate how some further spatial analysis can be done, on top of spatiotemporal datasets and mapping, to give us more confidence in winning this battle.
Part II: Epidemiological Modeling, Presented by Suyeon Ryu
In this webinar, we will discuss how we have built data-driven models upon coronavirus-related data collected from multiple sources in order to track and predict the spreading trend of the virus. Specifically, we will focus on the epidemiological SIR model to simulate the development of the coronavirus in different cities. The stochastic SIR model can estimate the termination date, infection rate, recovery rate, and R0 of the coronavirus. We will discuss how we used MCMC to estimate the distribution of epidemiological parameters, and once we have the distribution of parameters the future predictions come from simulations using the Monte Carlo method.
One of the most important use cases of ontologies is the calculation of similarity scores between a query and items annotated with classes of an ontology. The hierarchical structure of an ontology does not necessarily reflect all relevant aspects of the domain it is modelling, and this can reduce the performance of ontology-based search algorithms. For instance, the classes of phenotype ontologies may be arranged according to anatomical criteria, but individual phenotypic features may affect anatomic entities in opposite ways. Thus, "opposite" classes may be located in close proximity in an ontology; for example enlarged liver and small liver are grouped under abnormal liver size. Using standard similarity measures, these would be scored as being similar, despite in fact being opposites. In this paper, we use information about opposite ontology classes to extend two large phenotype ontologies, the human and the mammalian phenotype ontology. We also show that this information can be used to improve rankings based on similarity measures that incorporate this information. In particular, cosine similarity based measures show large improvements. We hypothesize this is due to the natural embedding of opposite phenotypes in vector space. We support the idea that the expressivity of semantic web technologies should be explored more extensively in biomedical ontologies and that similarity measures should be extended to incorporate more than the pure graph structure defined by the subclass or part-of relationships of the underlying ontologies.