As with any new situation, our LCSR professors have recognized the pressing need to develop open-source engineering solutions to address many aspects of the COVID-19 crisis. This page contains some of the new research opportunities that have emerged.

Telerobotics for the Intensive Care Unit (ICU)

For an infectious disease such as Covid-19, health care workers must don and doff personal protective equipment to enter the ICU, even if only to perform a simple task such as changing a setting on a ventilator or infusion pump.  LCSR researchers are developing a quickly deployable solution that will allow health-care workers to remotely operate equipment from outside the ICU. The LCSR team consists of Profs. Peter Kazanzides and Russell H. Taylor from the Department of Computer Science, Profs. Axel Krieger and Iulian Iordachita from the Department of Mechanical Engineering, and research scientists and technical staff members Balazs Vagvolgyi, Anton Deguet and Anna Goodridge. Clinical collaborators include critical care doctors at Johns Hopkins Hopsital and University of Maryland Medical Center, faculty from the Johns Hopkins School of Nursing, and respiratory specialists from the Johns Hopkins Hospital. In addition, the team is working the JHU Armstrong Institute for Patient Safety and Quality.

The team is exploring two robotic concepts: a Cartesian (XYZ) stage that is attached to the screen of the medical device and a conventional robot arm that is mounted near the medical device. The plan is to first deploy the XYZ robot on the most prevalent ventilator at Johns Hopkins Hospital, which contains a touchscreen interface, while refining both robot designs to enable interaction with more complex medical device interfaces, such as infusion pumps and ventilators with knobs. All robotic systems will include at least one camera to provide live video feedback to the operator outside the ICU. With some robot designs, the operator could command the robot arm to also video survey other parts of the ICU. All robot designs will include safety features, such as force sensing to ensure that they do not damage the equipment, and will be easily cleaned/disinfected.

Facilitating Machine Learning Research to Inform Coronavirus Response

The overarching goal of this project is to collect region-specific data so it can be understood why COVID-19 spreads differently in different communities. For example, can it be speculated that the density of New York City leads to a higher rate of infection, but what can that trajectory tell us about how COVID-19 might spread in, say, Miami?

By collecting data like population density and public transportation usage, we aim to identify similar areas and extend predictive models to regions with less advanced spread. By combining a similarity measure  relating New York City, Seattle, or LA, for example, to Miami, with a model for describing interventions, such as shelter-in-place orders, have affect COVID-19 transmission, we aim to understand how implementing or rolling back such interventions could affect transmission in the future.

So far, we’ve adopted an epidemiological model from Imperial College to estimate the effects of past interventions. Our model has shown that compared to the European Countries that Imperial College has focused on, most US counties are in the earlier stages of the disease and have yet to effectively “flatten the curve”. Moving forward, we hope to incorporate new information detailing compliance with stay-at-home orders based on foot-traffic data, examining the public’s response in a region-specific manner and its effect on the transmission rate of COVID-19.

The dataset we have collected to inform our research has won a Kaggle COVID-19 Dataset Award and is publicly available here: https://github.com/JieYingWu/COVID-19_US_County-level_Summaries

The project is led by Mathias Unberath and is the result of a herculean effort by a group of students at Johns Hopkins University and LCSR. Special thanks goes to Jie Ying Wu, Benjamin Killeen, Kinjal Shah, Anna Zapaishchykova, Philipp Nikutta, Aniruddha Tamhane, Shreya Chakraborty, Jinchi Wei, Tiger Gao, and Mareike Thies.

Alternative PPE Filter materials

The Covid-19 pandemic has created a shortage of personal protective equipment (PPE) for health care workers and first responders worldwide. The LCSR team is sourcing and testing alternate filter materials to be used in a respirator mask. The group is working on particulate testing for several types of filter materials as well as fit testing for open source mask designs and designs developed in house. The team working on the project consists of research scientist Anna Goodridge from the LCSR, in collaboration with the WSE Manufacturing team lead by Rich Middlestadt including Niel Leon, from the Department of Biomedical Engineering, Jeff Siewerdsen, Zachary Baker, and Paul Hage, from the Department of Electrical Engineering, Kevin Gilboy and from the Department of Environmental Health and Engineering in the Bloomberg School of Public Health, Ana Rule, Kirsten Koehler, Peter DeCarlo, and Ashley Newton. Clinical collaborators include critical care doctors at Johns Hopkins Hospital.