CDC Innovation Fund (iFund)
Call for CDC Innovation Fund applications for FY2020 is now open! Deadline to apply is November 1, 2019. These are intramural funds. Only CDC staff are eligible to apply.
The CDC Innovation Fund (iFund) seeks to promote the inventiveness and creativity of the CDC community in the design and development of new innovations which show promise for making a substantial impact on public health and how we accomplish our mission. The iFund provides intramural funding and support to CDC staff to develop initial proof of concept ‘prototypes’ and pilot projects, or scale up more mature projects that have been proven effective through pilot or replication studies.
CDC iFund Provides
- Up to a maximum of $150,000 to go towards your project with awards supporting a variety of projects from proof of concept to projects ready to implement or scale. We expect our innovators will be able to demonstrate greater evidence of effectiveness in order to get higher amounts of funding from the iFund. Actual investments may be significantly smaller than $100k.
- Funding for fiscal year 2020 (funds must be obligated by July 2020)
- Guidance and technical support specific to project implementation needs (EPLC, PRA, etc.)
Since its inception in 2011, the iFund has supported and financed over 90 mission-driven projects. This past August, OTI received over 35 proposals for consideration. The teams selected for FY2019 iFund support include:
Phase I (Idea Generation)
Center for Global Health: Artificial intelligence technology employing satellite image recognition to autonomously analyze humanitarian emergency camp management standards
Collaborators: Ashley Greiner, Amber Dismer, Rebecca Merrill, Sharmila Shetty, Ryan Mukherjee (Johns Hopkins University), Jeffrey Freeman (Johns Hopkins University); Mark Anderson (Executive Sponsor)
As of June 2018, an estimated 68.5 million people were currently displaced from their homes due to humanitarian emergencies; many are currently living in humanitarian emergency camps. Within these camps, minimum standards for living conditions and camp management have been established to mitigate health and safety/security risks of the camp population. However, assessing whether these standards are being met can be time consuming, requiring substantial financial and human resources, as well as on the ground assets, which may be difficult to deploy secondary to security concerns in conflict settings. Without timely and accurate estimates of emergency camp conditions, which dictate the health of that population, emergency response organizations are unable to tailor interventions and effectively address the population needs. We are proposing an artificial intelligence capability aimed at quickly and accurately assessing a humanitarian camp setting for adherence to established minimum standards in camp management by utilizing satellite image recognition analyses. If successful, the resulting technology would allow for rapid and autonomous analysis of an emergency camp setting. This would revolutionize the way humanitarian organizations are able to conduct these assessments – immediately informing response stakeholders, leading to faster and more targeted interventions, ultimately reducing the health risks of the camp population while saving vital human and financial resources.
National Institute for Occupational Safety and Health: Crowdsourcing Artificial Intelligence for Natural Language Processing of Injury Narratives
Collaborators: Carlos Siordia, Stephen Bertke, Audrey A Reichard, Mick Ballesteros, Stacey Marovich, Jeff Purdin; Mark Anderson (Executive Sponsor)
Millions of Americans are injured at work in the U.S. and about five thousand are killed at work yearly. This is why NIOSH, the Bureau of Labor and Statistics (BLS), and the Occupational Safety and Health Administration contracted with the National Academies of Science, Engineering, and Medicine (NAS) to develop a Consensus Study Report on how occupational safety and health surveillance can be improved. They recommended federal agencies publish data and develop publicly available natural language processing tools. Use of artificial intelligence/machine learning (AI/ML) to automate the assignment of codes through natural language processing will help build a more cost-effective surveillance system with higher caliber data. AI may reduce and/or eliminate the time-consuming, expensive approach of human-coding. Our project responds to NAS recommendations and is motivated by the desire to reduce the millions of work-related injuries in the U.S. Our project has three goals: (1) publish one training dataset; (2) crowdsource AI programmers; and (3) publish all resulting products to allow others to machine-code their injury narratives to Occupational Injury and Illness Classification System (OIICS). OIICS is used by BLS, NIOSH, and occupational epidemiologists to code injuries. Our project builds on CDC’s expanding efforts to crowdsource, use open source programming, and use AI housed in the Center for Surveillance, Epidemiology, and Laboratory Services Informatics Innovation Unit. Our innovative project merges crowdsourcing, open source programming, and AI.
Phase II (Prototype and Test)
Center for Global Health: Renewable inkjet-printable platform for rapid infectious disease detection
Collaborators: Alice Sutcliffe, Ellen Dotson, Sarah Zohdy (Auburn University), Maria Soledad Peresin (Auburn University), Derrick Mathias (University of Florida), Ilari Filpponen (Auburn University); Barbara Marston (Executive Sponsor)
Improvements to medical diagnostics are necessary to address global health issues. Diagnostic platforms that can be used in a wide variety of settings, particularly those that are resource-limited are greatly needed. We propose a cost-effective, environmentally sustainable platform to allow for ink-jet printable, customizable diagnostic assays for use in all settings, from resource-limited clinics to commercial clinical laboratories to address this need global need for diagnostic tools.
National Center for Emerging Zoonotic Infectious Diseases: Detecting Rabid Bats and Decreasing the Incidence of Rabies in Bat Colonies Before They Bite
Collaborators: Yoshinori Nakazawa, Clint Morgan, James Ellison, Christina Hutson, Todd Smith; Kim Hummel (Executive Sponsor)
Recent emerging infectious diseases such as Marburg, Nipah and SARS share a common theme: they are all zoonotic, and their natural reservoir is assumed to be bats. The overall goal of this project is to reduce human mortality associated with bats by reducing the number of rabid individuals within a population. We will test the use of an innovative ultrasound lure trap to selectively attract and remove bats infected with rabies virus.
National Institute for Occupational Safety and Health: Oil and Gas Virtual Reality Training Prototype
Collaborators: Gino Fazio, CAPT Duane Hammond, Dylan Neu, Kevin Menchaca, Dr. Thomas Cunningham; Christina Spring (Executive Sponsor)
Achieving success by working hard is the promise of the American Dream. In the oil and natural gas extraction industry, over 420,000 Americans sweat, strain, and risk their lives to reach that dream. Earning a living in this industry means increased risk for exposure to harmful gases and vapors, oxygen-deficient atmospheres, and fire and explosions. These exposures can have immediate health effects, including loss of consciousness and death. Between 2003 and 2016, 1,485 oil and gas workers were killed on the job, resulting in an annual fatality rate more than six times higher than the rate among all U.S. workers. The past 10 years have seen significant growth in computer based safety training; however, many of these programs lack hands-on participation. Research has shown that most adult learners acquire knowledge and skills more efficiently when they participate in hands-on training. This suggests that an ideal training exercise should provide an immersive and realistic environment for which to interact, identify hazards, and safely acquire application-oriented experience. We propose to develop and evaluate an immersive and realistic safety training environment in virtual reality (VR) to better educate high-risk employees on the safe practices to avoid the fatal hazards associated with their work.
Phase III (Implement and Scale)
National Center for Emerging Zoonotic Infectious Diseases: Enhanced sensitivity of rickettisal disease and scrub typhus diagnostics: large scale clinical validation and implementation
Collaborators: Cecilia Kato, Ida Chung, Leonard Peruski, Megan Reller (Duke University), J. Stephen Dumler (Department of Defense); Lyle Petersen (Executive Sponsor)
No reliable diagnostic tests are available for the early stage diagnosis of rickettsial diseases and scrub typhus (RDST). Undifferentiated febrile illness studies in (sub)tropical regions show rates up to 28%, and have been described as newly emerging and reemerging diseases world-wide, although the true burden of disease is unknown because current diagnostic tests lack proper sensitivity at the early acute stage. These are perceived to be rare diseases because of the lack of confirmed results so doctors may not know to consider rickettsiosis in their differential, leading to ineffective treatment and possibly death. Validation of highly sensitive RDST molecular assays that detect rRNA message (higher abundance in patient samples). Lyophilized test kits for clinical lab and use in low resource settings will be qualified and tested with patient samples with both clinical and portable instrumentation. No such assays are available for the detection of RDST. This work will help establish the utility and limitations of these assays. For the detection of RDST infections at the early stage of illness for patient treatment; increased accuracy of disease burden and distribution both domestically and globally; rapid outbreak testing support; and the ability to detect a potential biological threat.
National Center for Emerging Zoonotic Infectious Diseases: Rabies Control in the Palm of Your Hand: A mobile application for real-time monitoring of animal rabies vaccination, surveillance, and laboratory results
Collaborators: Ryan Wallace, Todd Smith, Brett Petersen, Jesse Blanton, Emily Pieracci, Ray Ransom, Benjamin Monroe, Julie Cleaton, Andy Gibson, Fred Lohr, Gowri Yale (Mission Rabies), Kelly Crowdis (Haiti Veterinary Services); Jennifer McQuiston (Executive Sponsor)
Rabies is a neglected disease that causes human deaths in more than 150 countries, worldwide. Primarily spread through the bite of a rabid dog, the impoverished and children are over-represented amongst the 59,000 rabies deaths that occur each year. Despite the notoriety as the deadliest infectious disease in the world, a lack of surveillance and inefficiently enacted dog vaccination campaigns has hindered global control efforts. Recently, focal success stories have been reported, including programs run by both the CDC (Haiti) and Mission Rabies (India and Malawi). However, these successes involve intensive training and oversight by rabies subject matter experts, a logistical constraint that cannot be easily replicated in all 150 endemic countries. In an effort to overcome this logistical barrier, the Poxvirus and Rabies Branch (PRB) was awarded a Phase II iFund project to work with several international partners to develop mobile technologies that improve vaccination and surveillance in resource limited settings. The Phase II project was largely successful, and in this Phase III project will expand implementation, address user-feedback issues, and investigate potential cross-agent compatibility.
National Center for Emerging Zoonotic Infectious Diseases: Applied Spatiotemporal Forecasting – Aedes forecasting challenge
Collaborators: Michael Johansson, Matthew Biggerstaff, Juan Sanchez Montalvo; Lyle Petersen (Executive Sponsor)
Early in epidemics, public health officials face critical decisions, and reliable epidemic forecasts could be of high value. The Epidemic Prediction Initiative (EPI) is a cross-Center activity started in 2014 to advance the development of applied forecasting tools for public health. Collaborating with academic, industry, federal, state, and local stakeholders, EPI has coordinated forecasting challenges for influenza and dengue that have significantly altered the landscape of epidemic forecasting. However, EPI has only addressed seasonal epidemics on large geographical scales (national or regional) to date. Epidemics, especially of emerging pathogens, exhibit variation in both time and space, requiring preparedness and response activities tailored to finer spatial scales. The proposed project will further develop the existing EPI framework to support real-time small-geographic scale (state and county) forecasting and forecast evaluation. As a case study, the project will address the spatiotemporal distribution in U.S. counties of Aedes mosquitoes, which are the vectors of chikungunya, dengue, yellow fever, and Zika viruses. This project will have important benefits for CDC’s mission, including improving preparedness for arboviral invasion in the United States and developing forecasting methods and frameworks for influenza and other emerging diseases at the state and local level.
National Center for Environmental Health: Toxic Metal Nanoparticles in Electronic Cigarette Aerosol
Collaborators: R. Steven Pappas, Mark Fresquez, Naudia Gray, Nathalie Gonzalez-Jimenez, Christa Wright (Georgia State University); Cliff Watson (Executive Sponsor)
Since the introduction of electronic cigarettes and other electronic nicotine delivery systems (ENDS) within the United States, product sales have increased among youth and adults. The 2017 National Youth Tobacco Survey revealed that ENDS use among middle and high school students had become more prevalent than any other tobacco product. ENDS are rapidly developing technologies that are in their fourth generation of product design. Inhalation exposures to nanoparticles (particles with at least one dimension smaller than 100 nm) produced by ENDS devices are unintended consequences of the use of these products. Inhalation exposures to nanoparticles exhibit greater toxic effects than larger particles as a consequence of their greater surface area to volume ratios. The aim of this proposal is to determine the physico-chemical properties of ENDS nanoparticles that may influence adverse cellular outcomes. We propose to expand our total aerosol metal concentration data by using Dynamic Light Scattering and Single Particle-ICP-MS. Normal human bronchial epithelial and small airway epithelial cells will be used to assess toxicity of ENDS aerosol samples. The impact on public health is estimated to be significant, given the lack of reliable data presently available on potential exposures of ENDS users to toxic nanoparticles originating from ENDS devices.
For additional information, please contact us at OTI@cdc.gov.