Researchers are the first to model COVID-19 completion versus cessation in clinical trials using machine learning algorithms and ensemble learning. They collected 4,441 COVID-19 trials from ClinicalTrials.gov to build a testbed with 693 dimensional features created to represent each clinical trial. These computational methods can predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended. Stakeholders can leverage the predictions to plan resources, reduce costs, and minimize the time of the clinical study.
New research in flies indicates that prediction may be a universal principle among animal nervous systems to enable rapid behavioral changes.
A forthcoming research paper by Johns Hopkins Carey Business School Assistant Professor Robert Mislavsky, a marketing expert, looks at a little-examined area of probability forecasting.
Researchers will use big data analytics techniques to develop computational models to predict the spread of COVID-19. They will utilize forward simulation from a given patient and the propagation of the infection into the community; and backward simulation tracing a number of verified infections to a possible patient “zero.” The project also will provide quick and automatic contact tracing and leverages the researchers’ prior experience in modeling Ebola spread.
Researchers at Binghamton University, State University of New York are using machine learning to track the coronavirus and predict where it might surge next.
This week in the journal Frontiers, researchers describe a single function that accurately describes all existing available data on active COVID-19 cases and deaths—and predicts forthcoming peaks.