Novel Method Predicts if COVID-19 Clinical Trials Will Fail or Succeed

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 These computational methods can predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended.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.

Indigenous People Vital for Understanding Environmental Change

Grassroots knowledge from indigenous people can help to map and monitor ecological changes and improve scientific studies, according to Rutgers-led research. The study, published in the Journal of Applied Ecology, shows the importance of indigenous and local knowledge for monitoring ecosystem changes and managing ecosystems. The team collected more than 300 indicators developed by indigenous people to monitor ecosystem change, and most revealed negative trends, such as increased invasive species or changes in the health of wild animals. Such local knowledge influences decisions about where and how to hunt, benefits ecosystem management and is important for scientific monitoring at a global scale.