Monthly Archives: February 2022

Could we have used Influenza data early to improve our understanding of COVID-19?


Early in a pandemic, the lack of prior data is an obvious limitation to the development of reliable risk prediction models, resulting in delayed analysis. However, there may be a way to overcome this issue and speed up the analysis process: the use of a proxy disease. In a recently published article, Williams and colleagues developed a prediction tool for COVID-19 (named COVER) using influenza data from a federated network of electronic medical records prior to 2020. The model was then applied to 5 COVID-19 patient databases and validated, proving successful in predicting hospitalization, intensive services, and fatality based on 7 major predictors. In this guest blog post, Prof. Knaus from the University of Virginia discusses Williams and colleagues’ results.

Health Medical Evidence Medicine

Improving function prediction by considering proteins in terms of their constituent domains


Predicting protein function is an ongoing challenge with many different methods being developed to tackle this in recent years. In a recent study published in BMC Bioinformatics Perkins and colleagues describe DomFun, a Ruby gem that looks at the association between separate protein domains and function before combining them at the protein level to generate protein-function predictions.


Introducing the BMC Series SDG Editorial Board Members: Biplab Kumar Datta

BKD photo

Biplab Kumar Datta is an Editorial Board Member of BMC Public Health. He is an applied microeconomist working in the fields of global health and public health economics. After serving the U.S. Centers for Disease Control and Prevention (CDC) for nearly three years as a Prevention Effectiveness Fellow, he joined Augusta University as an Assistant Professor in the Institute of Public and Preventive Health.