Jump ARCHES Spring 2020 Funded Grants - COVID-19 Priority Call
Special Jump ARCHES Call Addresses Solutions to Mitigate COVID-19 Pandemic
April 30, 2020 | Champaign, IL
Seventeen research projects are sharing nearly $800,000 in funding through the Jump ARCHES research and development program. The Jump Applied Research for Community Health through Engineering and Simulation (Jump ARCHES) program is a partnership between OSF HealthCare and The Grainger College of Engineering at the University of Illinois (U of I) at Urbana-Champaign.
“The University of Illinois and OSF systems are home to brilliant minds that can undoubtedly help us fight this disease, so we mobilized almost immediately to offer this special grant funding. ”
T. Kesh Kesavadas, Director, Health Care Engineering Systems Center
These projects were submitted to an unprecedented special call for Jump ARCHES proposals to address COVID-19, pandemics, and other public health crises through smart health, data analytics, AI, and other technologies. The ARCHES program supports research involving clinicians, engineers, and social scientists from OSF HealthCare, University of Illinois, and U of I College of Medicine in Peoria (UICOMP) to develop technologies and devices that could revolutionize medical training and health care delivery. A requirement of the grant applications was for solutions that could be deployed quickly, within four to six weeks.
“In this crisis mode where we are all working to leverage Jump Trading Simulation and Education Center and our talents to improve service for patients affected by COVID-19, the synergistic effect of engineering and clinical service breaks down traditional barriers and gets us more quickly to much-needed solutions,” said Dr. John Vozenilek, Vice President and Chief Medical Officer of Jump Simulation Center in Peoria.
“When COVID-19 was declared a pandemic, we felt that it was our responsibility to help researchers find solutions. We are pleased to fund proposals in a wide range of topics such as AI/modeling, contact tracing, new testing techniques, sterilization, and PPE,” said Dr. T. Kesh Kesavadas, Director of the Health Care Engineering Systems Center at University of Illinois at Urbana-Champaign and Engineer-in-Chief of Jump ARCHES. "The University of Illinois and OSF systems are home to brilliant minds that can undoubtedly help us fight this disease, so we mobilized almost immediately to offer this special grant funding."
Since its inception in 2014, Jump ARCHES has awarded more than $5.46 million in funding to collaborative projects between the three institutions and across many disciplines. The effort expanded opportunities with an additional major gift in 2019.
The COVID-19 keeping safe program
Brent Roberts, UIUC; Sarah Stewart de Ramirez, OSF HealthCare, Jump Trading Simulation & Education Center, UICOMP; Sanjay Patel, UIUC; John Paul, UIUC; William Sullivan, UIUC; Nick Allen, UIUC; Roopa Foulger, OSF HealthCare; Lisa Barker, Jump Trading Simulation and Education Center
To help communities open back up in the safest way possible, this platform will expand on the OSF Pandemic Health Worker Program for more extensive monitoring of individuals before, during, and after exposure to COVID-19. This platform will be combined with a COVID-19 status app that will document current coronavirus testing and immunological status, a state-of-the-art tracing of exposure to COVID-19 cases, and a set of digital systems to take advantage of and track psychosocial factors that influence exposure risk and psychological well-being.
A single-step, 10-minute, point-of-care COVID-19 diagnostic test using Activate Cleave & Count (ACC) technology
Brian Cunningham, UIUC; Anurup Ganguli, UIUC; John Farrell, OSF HealthCare; Taylor Canady, UIUC; Shreya Ghosh, UIUC
ACC technology takes advantage of the superior specificity of the CRISPR/Cas system to selectively recognize a unique target segment of the SARS-CoV-2 genome. Using newly invented ultrasensitive biosensor technology, this proposal addresses an important gap in the capabilities of any existing method to enable simple and inexpensive point of care COVID-19 diagnostic testing from a nasal swab to accurately and quickly diagnose infected patients.
Converting a microwave oven into a mask sterilization unit
David Ruzic, UIUC; Eitan Barlaz, UIUC; Leyi Wang, UIUC; Helen Nguyen, UIUC
As the COVID-19 outbreak intensifies there will be a need to rapidly sterilize N95 and other face masks used in hospitals, nursing homes, and other locations. This proposal will investigate the effectiveness for sterilization and disinfection to offer additional research to initial reports that indicate microwaves can be an effective microbicide.
Supply-driven hospital resource planning and community engagement for COVID-19 treatment
Lavanya Marla, UIUC; Qiong Wang, UIUC; Benjamin Davis, Carle; Kurt Bloomstead, OSF HealthCare
Gaps exist in our understanding of how to simultaneously manage workforce and resource supplies in a pandemic over time. This proposal will develop algorithms for supply-side planning of both health care workforce and supplies tailored to pandemics by integrating resource inventory aspects and behavioral response to messaging. It will also generate knowledge on the right type of information dissemination to the community that models patients' response to help manage demand and not create congestion at hospitals within communities.
Development of a blood analysis technology for artificial intelligence-assisted, point-of-care decision making
Rohit Bhargava, UIUC; James McGee, OSF HealthCare; Tulika Chatterjee, OSF HealthCare, UICOMP
This project proposes to advance the complete blood count test using infrared (IR) spectroscopic imaging, which simultaneously measures both microscopic morphology and molecular composition. Using AI and data analytics techniques, the team will be able to perform a differential analysis of leukocytes (DIFF) to quantify the immune response of the body, while possibly providing new biochemical information for a more complete picture of the patient’s health and offer early warning of viral infections such as s COVID-19, pandemic flu, or similar health crises.
Testing the filtration efficiency of N95 respirators for health care employees and protecting public health in pandemic flu emergencies
Vishal Verma, UIUC; Matthew Bramlet, OSF HealthCare, Jump Trading Simulation & Education Center, UICOMP
This project proposes to solve the problem of quality assurance testing for Do-It-Yourself or rapidly made personal protective equipment (PPE). A two-tiered approach would include a robust testing facility that evaluates N95 respirators for their filtration ability and to conduct a fit test on the actual human subject to test the seal and particle removal efficiency.
Data-driven modeling, analysis, and simulation of epidemic processes: controlling COVID-19
Carolyn Beck, UIUC; Tamer Basar, UIUC; Joseph Kim, UICOMP
This project proposes to develop a comprehensive data-driven approach to the modeling, analysis, and control of epidemic processes over time-varying networks on multiple layers. This approach considers the impact of mitigation efforts. Ultimately, the project hopes to advance understanding of spread and control of epidemic processes over complex networks, focusing on infectious diseases, but the models can apply to the spread of computer viruses, misinformation, and adversarial processes over complex networks, such as those found in natural and engineered systems.
Maximizing the informational value of PCR-based COVID-19 tests through optimal pooled community testing
Hadi Meidani, UIUC; John Farrell, OSF HealthCare; Daniel Lakeland, Lakeland Applied Sciences LLC
During the present COVID-19 outbreak, the lack of available testing capacity, and resulting inability to broadly test the community at large scale leaves decision makers with essentially no information about the overall prevalence of the virus in the general community. This study allows for a new concept in community testing, in which an inexpensive screening procedure for thousands of patients can be developed using only one multi-well batch assay.
Healthier Homes: an assessment of opportunities to reduce risk of infectious disease transmission at home
Paul Francisco, UIUC; Beth Houser, OSF HealthCare
When an individual has become infected (or there is reason for concern that he/she may have be infected) there might be additional measures that can be performed within the home to reduce the risk of transmission to other family members and allow the individual to recover at home (assuming sufficiently mild infection). This study will look at methods for at-home care and ways to reduce viral load and airborne transmission.
Rapid SARS-CoV-2 detection from nasal swab extracts
Rashid Bashir, UIUC; Enrique Valera, UIUC; Anurup Ganguli, UIUC; Sarah Stewart de Ramirez, OSF HealthCare, Jump Trading Simulation & Education Center, UICOMP
Because PCR-based technologies remain expensive (in terms of instrument capital equipment and reagents), technically challenging, and labor intensive, there is an urgent need for low-cost portable platforms that can provide fast, accurate, and diagnosis at the point of care. This project proposes building on work already completed to test samples using reagents already developed in the Bashir Lab and a simple optical fluorescence reader (e.g. smartphone), avoiding the necessity of RNA extraction.
Rapid, contactless vital signs collection using computer vision and consumer technologies
Ramavarapu Sreenivas, UIUC; Roopa Foulger, OSF HealthCare; Brent Cross, Jump Trading Simulation & Education Center; Stefan Malmber, Dectivio LLC; Taha Khan, Dectivio LLC
The goal of the proposal is to develop a computer vision algorithm for rapidly assessing an individual’s key vital signs (temperature, heart rate, respiratory rate, and blood pressure) relevant to COVID-19 utilizing a consumer grade camera in the absence of contact or additional sensing elements not readily available (ambient temperature, sound). The algorithm should be appropriately containerized to integrate with on market electronic medical records and telehealth applications including Epic and Vidyo.
Ventilator duplication kit
Matthew Bramlet, OSF HealthCare, Jump Trading Simulation & Education Center, UICOMP; Jon Michel, OSF HealthCare; Brad Sutton, UIUC; Laura Villafan Roca, UIUC
The Ventilator Duplication Kit project aims to develop a rapidly deployable kit for using a single ventilator among multiple patients with varying ventilation needs by tailoring the delivered breaths to each individual. Research and development will start with MATLAB physics simulations of the pressure and flow of the ventilator-multi-patient system. The end product will be a system of one-way valves and additive resistive tubing along with flow and pressure monitors for each patient.
Data driven analytics to predict the dynamics of COVID-19 outbreak and the impact on health care providers, resources, and communities
William Bond, Jump Trading Simulation & Education Center, UICOMP; Roopa Foulger, OSF HealthCare; Sarah Stewart de Ramirez, OSF HealthCare, Jump Trading Simulation & Education Center, UICOMP; Ravi Iyer, UIUC; Roy Campbell, UIUC
This project proposes to use a novel analytical approach which incorporates artificial intelligence, data analytics, and machine learning to drive solutions to improve outcomes of the COVID-19 virus. Their solutions integrate formal infectious disease spreading models with uncertainty modeling approaches based on fuzzy logic aka computing based on degrees of truth. Additionally, the team will create a visual predictive analytics dashboard which can be used to allocate resources (staffing, equipment, medicine, space/location).
A rapid and affordable virus test for early warning of a pandemic
Joseph Irudayaraj, UIUC; Wen Ren, UIUC; William Bond, Jump Trading Simulation & Education Center, UICOMP
This initiative will develop a test kit that extracts nucleic acid sequences from a sample of blood or saliva which are amplified with primers and new technology that can look for biomarkers of the virus using colorimetry so detection by naked eyes is possible. Researchers hope to develop an all-in-one test kit costing no more than $5 USD.
Secure federated learning for collaborative diagnostics
Sanmi Koyejo, UIUC; Dakshita Khurana, UIUC; George Heintz, UIUC; William Bond, Jump Trading Simulation & Education Center, UICOMP
Open sharing of medical data is not viable because of data privacy and intellectual property concerns. This proposal leverages modern cryptographic tools to introduce a computational and software features for securely training predictive models using huge data sets distributed over several medical establishments; while ensuring patient privacy. Ultimately, they believe that such privacy-preserving distributed methods are key to rapid identification, risk assessment, prognosis, and diagnosis for community health crises such as the current COVID-19 pandemic.
Proposed plans for the fabrication of personal protective equipment (PPE) for local health care systems; N95 respirator
Martin Burke, UIUC; Irfan Ahmed, UIUC; Helen Nguyen, UIUC
With projected shortages of PPE and respirators, this project set out to develop and fit test a prototype N95 respirator (standard size) and 3D printed end-cap that can accept existing medical respiratory filters, pass a N95 respirator fit test, and be sterilized or sanitized by readily available procedures. This design approach will be to use some medical respiratory filters which are not expected to be in higher demand due to COVID-19 (e.g., filters used with anesthesia). All designs will be published as open source online.