University of Maryland Researchers Awarded NSF RAPID Grants to Bolster COVID-19 Response

University of Maryland Researchers Awarded NSF RAPID Grants to Bolster COVID-19 Response

University of Maryland researchers have been awarded National Science Foundation (NSF) RAPID grants to address the current COVID-19 crisis.

NSF is working closely with the scientific research community to bolster the national response to COVID-19. The agency is funding dozens of research projects on COVID-19 to mobilize the scientific community to better understand and develop measures to respond to the virus.  NSF issued a letter to researchers inviting proposals for rapid response research grants related to the virus to help inform and educate the public about virus transmission and prevention, and develop effective strategies for addressing this challenge at the local, state and national levels.  Support for these efforts is made through NSF's Rapid Response Research (RAPID) funding mechanism, which enables the agency to quickly process and support research that addresses an urgent need.

Learn more about the University of Maryland recipients of NSF RAPID grants below:

Using Location-based Big-Data to Model People's Mobility Patterns During the COVID-19 Outbreak

Kathleen Stewart (Principal Investigator)
Debbie Niemeier (Co-Principal Investigator)
Junchuan Fan (Co-Principal Investigator)

The outbreak of COVID-19 in the U.S. provides an important opportunity for researchers to study the impacts of a rapidly expanding pandemic on human mobility. This research investigates how to measure changes in collective movement of people in response to the fast-evolving COVID-19 outbreak using large datasets of passively collected location data. It examines how locations within a state respond to public policy implementation and times of critical public messaging. Detailed knowledge on movement patterns of people can help public officials identify hotspots and critically isolated populations, as well as shed light on those groups who continue to travel for work or other purposes. This research contributes to improving the public response to an emergency and contributes to bridging different stakeholder mitigation strategies.

Detailed knowledge of how people respond to a fast-spreading global pandemic is very limited and our understanding of these responses is mostly for small areas. This research will use a near real-time location-based dataset passively collected through the use of location-based apps during the period of pandemic. The project will develop scalable, big location-based algorithms to extract trips and examine the evolution of mobility patterns throughout the pandemic, and identify different mobility patterns. The research team will develop map-reduce based distributed algorithms to scale up mobility measure calculations based on the big location-based data as well as develop entropy measures to capture the time-varying characteristics associated with the travel patterns, and design strategies to correct biases that may be present in the location data. The methods and results of this research will be useful for understanding mobility during other hazards that affect communities, such as severe flooding to understand how travel is changed as a result of imperatives stemming from both the hazard and policy directives.

Combining Big Data in Transportation with Hospital Health Data to Build Realistic "Flattening the Curves" Models during the COVID-19 Outbreak

Deb Niemeier (Principal Investigator)
Kartik Kaushik (Co-Principal Investigator)

The outbreak of COVID-19 in the U.S. provides an important opportunity for researchers to improve flattening curve models which can be used to assess and even spatially optimize health care during a rapidly expanding pandemic. This Rapid Response Research (RAPID) project will take advantage of the large-scale availability of location-sensing devices and apps that produce big data on mobility patterns that can be used to better optimize the use of healthcare facilities. This research brings together rapidly unfolding health data with real-time data on mobility. The researchers will examine how these two critical data resources can be linked to better inform policy, identify emerging hotspots, and target critical actions during a pandemic. This research will help public officials to better understand and adapt to changing conditions as a health emergency arises and expands.

The spread of the “flattening curves” graphic was significant in promoting public understanding of the criticality of social distancing. These curves, however, were based on simulated data. This research will collect and examine mobility data and public health data to model flattening curves using real data. The researchers will combine big data from location-based apps and cellphones with Electronic Medical records from UMMS hospitals, including data on COVID-19 tests, and patient demographics and prognostics. New modeling approaches that quantitatively measure change in collective movement behaviors in response to the fast-evolving COVID-19 outbreak will be linked to hospital usage and capacity. The methods of this research will extend our knowledge of highly integrated systems, like transportation and health, and better prepare the public for future disasters.

Advanced Topic Modeling Methods to Analyze Text Responses in COVID-19 Survey Data

Philip Resnik (Principal Investigator)

As the COVID-19 pandemic continues, public and private organizations are deploying surveys to inform responses and policy choices. Survey designs using multiple choice responses are by far the most common -- "open ended" questions, where survey participants provide a longer-form written response, are used far less. This is true despite the fact that when you allow people to provide unconstrained spoken or text responses, it is possible to obtain richer, fine-grained information clarifying the other responses, as well as useful "bottom up" information that the survey designers did not know to ask for. A key problem is that analyzing the unstructured language in open-ended responses is a labor-intensive process, creating obstacles to using them especially when speedy analysis is needed and resources are limited. Computational methods can help, but they often fail to provide coherent, interpretable categories, or they can fail to do a good job connecting the text in the survey with the closed-end responses. This project will develop new computational methods for fast and effective analysis of survey data that includes text responses, and it will apply these methods to support organizations doing high-impact survey work related to COVID-19 response. This will improve these organizations' ability to understand and mitigate the impact of the COVID-19 pandemic.

This project's technical approach builds on recent techniques bringing together deep learning and Bayesian topic models. Several key technical innovations will be introduced that are specifically geared toward improving the quality of information available in surveys that include both closed- and open-ended responses. A common element in these approaches is the extension of methods commonly used in supervised learning settings, such as task-based fine-tuning of embeddings and knowledge distillation, to unsupervised topic modeling, with a specific focus on producing diverse, human-interpretable topic categories that are well aligned with discrete attributes such as demographic characteristics, closed-end responses, and experimental condition. Project activities include assisting in the analysis of organizations' survey data, conducting independent surveys aligned with their needs to obtain additional relevant data, and the public release of a clean, easy to use computational toolkit facilitating more widespread adoption of these new methods.

Assessing the Social Consequences of COVID-19

Long Doan (Principal Investigator)
Jessica Fish (Co-Principal Investigator)
Liana Sayer (Co-Principal Investigator)

This project examines the impacts of COVID-19 and states' and local governments' social distancing directives on behavior, time spent with others, use of technology, and mental and physical wellbeing. The objective of the project is to investigate these daily life impacts in real time and to analyze how these impacts are affected by sociodemographic characteristics that affect time use and well-being. Data are leveraged from several hundred respondents' daily time use before the pandemic along with data collected during and after the pandemic to create a natural experiment that isolates the effects of the pandemic on changes in behavior. Among the products of this research are evidence-based recommendations to address the social consequences of the pandemic.

This project collects data for the second and third waves of a three-wave panel study, the second wave during the pandemic with shelter-at-home and lockdown orders in place and the third wave after the pandemic has subsided and orders have been relaxed. Data for these two waves consist of survey responses and 24-hour time diaries collected from 2,000 respondents from online crowdsourcing platforms. This sample includes a smaller sample from whom data were collected before the pandemic. Data are collected on sociodemographics, typical sleep, work, and exercise patterns, and arrangements for housework and carework to investigate effects on time use and wellbeing.

Energy-Efficient Disinfection of Viral Bioaerosols in Public Spaces: Vital for Lifting of the "Stay-at-Home" Orders During the Covid-19 Outbreak

Jelena Srebric (Principal Investigator)

This project will provide an analytical framework to assess potential reduction of infection risks from COVID-19 viral bioaerosols in public spaces, including school buses, classrooms, and retail stores. Viral bioaerosols may cause infection for occupants staying both near and far away from infected people, whether staying indoors at the same time or not. Upper-room germicidal ultraviolet (UR-GUV) light can provide a real-time air disinfection solution with a relatively small energy footprint if its light effectively interacts with the bioaerosol both in the air and on surfaces. This project will develop and disseminate an open-source numerical analytical framework including assessment of UR-GUV disinfection and make it publicly available online to provide a free resource useful for helping to control the spread of airborne COVID-19 infections in public spaces. An effective, real-time, and sustainable engineering solution for air indoor space disinfection is an important precaution to help prevent the spread of COVID-19, particularly in the context of efforts to restart the nation's economy.

The project will develop numerical methods based on Computational Fluid Dynamics (CFD) to reproduce the processes for viral bioaerosols spread by indoor airflow, removed by exhaust, inactivated by UR-GUV, inhaled by the occupants, and deposited onto surfaces in public spaces of varied spatial scales, ventilation systems, as well as population size and density. This project will also optimize the application of ceiling fans to improve UR-GUV disinfection efficacy. The investigation will provide new insight on infection risk due to viral aerosols and infection control by UR-GUV for surfaces contaminated by viral bioaerosols. In addition, the project will consider two UV-C sources, one by traditional mercury vapor UV-C lamps (UV-C-MV) and another by UV-C-LED for their energy efficiency. The comparison of the two UV-C sources in terms of disinfection, energy efficiencies, and operation cost holds promise for a sustainable UR-GUV solution for minimizing infection risk in public spaces.

Supply Chain Portal to Serve Entrepreneurs Producing Critical Items in Response to COVID-19

Louiqa Raschid (Principal Investigator)

This COVID-19 RAPID project combines the efforts of the NSF Convergence Accelerator Business Open Knowledge Network (BOKN) and Manufacturing Open Knowledge Network (MOKN) in order to develop a knowledge resource to support the discovery of manufacturers and materials suppliers to help assemble new supply chains, particularly focusing on personal protective equipment (PPE), such as ventilators. The BOKN encodes information about businesses and their capabilities, while the MOKN encodes manufacturing information about goods. By combining information and capabilities from both networks, this integrative COVID RAPID project will develop search and matching tools that will help entrepreneurs and manufacturers to adapt swiftly to the supply chains and processes needed to produce new types of products. The key information along with analysis capabilities for performing information extraction, data cleaning, and data representation will be accessible via a web portal, initially focusing on supply chains for PPE. The resources developed can be used equally well by small businesses and entrepreneurs as well as more established organizations.

The project will harness data from a diverse set of sources, including manufacturing designs open-sourced by manufacturers; component information from shipping manifests; and manufacturing capabilities of firms sourced from websites and social media pages. Services provided via the web portal will enable users to find data, determine where to source components, and/or which designs to produce from these components. The project will develop an end-to-end system for generating, representing and populating new supply chains and processes focusing, initially, on the manufacture of PPE. These objectives will be achieved by the creation of enhanced interfaces for navigating company information, including relationships to other companies and profile information about each business. Learned representations of manufacturing firms will be developed in vector space models to better capture manufacturing capabilities and to investigate fuzzy matching capabilities for materials, parts, and sub-components. The materials and parts mentioned in patent claims for PPE will be of particular interest. The project will create the relationships between bill of lading data and domestic importers, and potential suppliers of materials and parts. Users will be able to integrate offerings from multiple information sources to rapidly meet emergent production needs, beginning initially with PPE, but extendibly to other critical products.

A "Citizen Science" Approach to Examine COVID-19 Social Distancing Effects on Children's Language Development

Yi Ting Huang (Principal Investigator)

The COVID-19 pandemic is a significant threat to learning and language development for large numbers of children. Such challenges are compounded for those facing social and economic adversity, factors that are associated with decreased parental interactions, child development, and school achievement. This study examines the scope and magnitude of learning impacts from COVID19 pandemic by engaging families as "Citizen Scientists" who will track their children's language use during the crisis. Social-distancing policies vary by state, enabling the researchers to compare how these different decisions affect children's language development. This will help policymakers and educators make more informed decisions, both about crisis management and strategies to mitigate negative effects of crisis-related policies. More broadly, this work will make important contributions to the science of language learning, which in turn will help clinicians and educators best address the needs of children from varying demographics. Finally, by using a Citizen Science paradigm, this project establishes a conduit for science outreach and education.

This project will recruit thousands of "volunteer researchers" to record data about their own family environment, parent-child conversations, and child language development using a web-based application accessible through a laptop or mobile phone. In addition to collecting survey responses, this app enables parents to make short audio recordings of their child's speech and build a scrapbook of developing language abilities over time. When paired with comprehensive recruitment, this platform will assemble speech samples that are both broad and deep and will support more accurate models of relations between children's learning and long- vs. short-term adversity. Additionally, the varied timing of social disruptions across locations permits both between-family and within-family comparisons of COVID-19 impacts, and yields estimates of effect sizes and modulation by race and socioeconomic status. The data will address questions of urgent societal interest, including a) how COVID-19 policies impact language-learning environments; b) how family stress changes children's language and communication behavior; and c) what impacts the COVID-19 crisis has on developmental outcomes. Moreover, since social disruptions affect a wide demographic and are largely outside family control, this project leverages the COVID-19 crisis as an unusually clean manipulation of social and economic adversity. This avoids confounds that are persistently problematic in existing research, and will deepen theoretical insight into the factors that affect children's language learning.

Forest Productivity and Expression in a Low-emissions Present: A RAPID Response to the COVID-19 Emissions Reduction Event

Nathan Swenson (Principal Investigator)

State and federal policies have significantly limited human activities to keep the U.S. population safe during the COVID-19 pandemic. This has resulted in a significant decrease of atmospheric inputs from the reduction in automobile and air travel. The unprecedented and dramatic reduction in traffic in major metropolitan areas where emissions are consistently high is transforming the atmosphere, even at continental scales. The COVID-19 event presents a unique, ephemeral, and rare opportunity to study how forests would respond to dramatically cleaner air in the United States. This award will explore how North American forests that have experienced a life-time of the byproducts of human transportation respond by examining responses from the genetic and molecular levels to the forest scale. The research will be conducted at a large forest plot near the Washington DC metropolitan area with a long history of forest research and adjacent to a National Ecological Observatory Network (NEON) tower. These linkages provide opportunities to scale the molecular research to potential ecosystem responses to emissions reduction efforts. The Education Office at Smithsonian Environmental Research Center (SERC), which works with thousands of high school students and their teachers every year will incorporate results into classroom activities at the SERC Education Center.

Knowing how trees and forested ecosystems respond to a transformed atmosphere is critical for providing projections of the Earth system under ongoing global change. This proposal provides a unique opportunity to explore the potential consequences of future policy by evaluating what could happen if emissions were dramatically reduced. The project provides an unprecedented opportunity to study the impacts from the genomic, physiological, population, community, ecosystem level given the ongoing research at these levels and leveraging existing infrastructure and data provided by the Smithsonian (Forest GEO), US Forest Service (FIA plots), and NSF (NEON). The research will focus on gene expression profiles of two species (beech and red maple) to explore whether they will exhibit parallel shifts favoring maximal growth in all size classes compared to pre-Covid-19 conditions. The research will examine how leaf chlorophyll content at the end of the growing season will predict gene expression differences. The research will also explore gene pathways that deal with reactive oxidative stress (ROS) reactions, repair, and stress signaling and the physiological responses for growth and reproduction for this and next growing season.

Understanding and Facilitating Remote Triage and Rehabilitation During Pandemics via Visual Based Patient Physiologic Sensing

Min Wu (Principal Investigator)
Donald Milton (Co-Principal Investigator)

This RAPID project plans to investigate visual-based physiological sensing technologies to facilitate remote triage and rehabilitation during pandemics, by using low-cost consumer-grade cameras to track such physiological conditions as respiration rate, heart rate, and blood oxygen saturation levels from videos. The physiological data can be visualized and archived, and shared by users with medical practitioners to understand and support remote triage and rehabilitation.

The proposed research can enhance the interaction between medical providers and patients, and help address a projected surge in telehealth needs due to COVID-19. The PI team plans to conduct the first-of-a-kind data collection, by incorporating the novel contact-free video sensing into a biomedical cohort study that is being rolled out by a public-health collaboration team. This cross-disciplinary opportunity of multimodal data collection will offer insights on the relationship of multiple biosensing modalities, and the data collected would facilitate the research on early detection of COVID-19 and related diseases. The visual-based physiological sensing will also help enhance the remote interaction between rehabilitation therapists and patients during pandemics.

The intellectual merit of this effort lies in advancing promising engineering techniques of video-based contact-free physiological monitoring to support the rising needs of remote triage and rehabilitation during pandemics. The research findings and techniques developed address an important missing component in telehealth, which simultaneously achieves social-distancing, avoids hospital overcrowding, and prioritizes personal protective equipment in response to pandemics. By collaborating with another cohort study, an unprecedented multitude of data collected by the joint effort will provide key insights toward understanding and managing COVID-19 diseases and remote triage for future outbreaks. The timeliness of this opportunity cannot be met by any regular NSF programs other than the RAPID.

The project's broader impact lies in two aspects. The multidisciplinary effort will provide important new knowledge and insights toward understanding and developing technology capabilities for remote triage and rehabilitation, which will contribute to the early detection, spread control, and effective management and prevention of future epidemics. The techniques developed through the project to support tele-rehabilitation will have a strong potential to improve the adverse conditions and quality of life of the affected citizens.

Accelerating Phylodynamic Analyses of SARS-CoV-2

Michael Cummings (Principal Investigator)

Evolutionary analyses using genomic data are an essential component of the scientific response to the COVID-19 pandemic, which is caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). Inferring the evolutionary history, or phylogeny, of virus samples with sampling time and location information allows scientists to estimate the divergence of viral lineages in time and place. These analyses provide time estimates that predate sampling events. Information about mutations, and the rate of mutation, is inherent to these phylogenetic analyses such that specific viral linages with accelerated mutation rates, if they exist, can be identified. Furthermore, molecular phylodynamics includes not only evolutionary history but also information on viral genetic variation and viral population dynamics, again all in the context of geography and time. The software from this project will be used in SARS-CoV-2 research on: patterns of movement and migration; time of outbreak origin; rate of mutation and detection of significant mutations with potential health impact; prevalence in populations at different geographical scales; reproductive number and impact on policy; and infection-to-case reporting rates. Perhaps of most immediate impact is that software from this project will accelerate tracing and dating the origins of outbreaks in specific geographic regions where contact tracing is not effective. Contact tracing and phylogenetic analyses work on different scales, and thus are complementary. Together they provide a more comprehensive view of the transmission patterns for the current pandemic.

Phylodynamic analyses are particularly rich in terms of inferences, albeit at a considerable computational cost. This project will greatly accelerate phylogenetic and phylodynamic analysis of SARS-CoV-2 data sets, and facilitate their computation on National Science Foundation supported computing resources, academic computing centers, as well as cloud computing environments. Specific activities include designing new strategies for efficient parallel computation of large data sets from viral outbreaks focusing on SARS-CoV-2, developing strategies for removing the barriers to easy use of highly performant parallel phylogenetic and phylodynamic analyses, developing algorithms for implementing these new strategies on graphical processing units (GPUs), and working with others to improve the time to results for analyses of SARS-CoV-2 data sets. This RAPID award is made by the Division of Biological Infrastructure using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.

The Impact of COVID-19 on Job Loss and Job Creation

John Haltiwanger (Principal Investigator)
Erkut Yusuf Ozbay (Co-Principal Investigator)
Katharine Abraham (Co-Principal Investigator)
Sepehr Ghader (Co-Principal Investigator)

This research project will use anonymized real time cellular phone location data combined with other sources of data to investigate the employment effects of the COVID19 pandemic. The research will develop an innovative theoretical model of job destruction and job creation, at the granular level, in response to the pandemic and use the data assembled to estimate the model. The model does not only account for job destruction and creation at various locations but also changes in the types of jobs created as well as the changing industries in which the jobs are created at the various locations. The new model is likely to influence how researchers investigate the effects of pandemics on employment at various locations. The research results will provide important inputs into how to craft policies to counter the employment effects the current as well as future pandemics particularly, and economic disruptions generally. The results will also establish the US as the global leader in understanding the employment effects of pandemics and how to develop policies to reduce their effects.

This research project builds on existing high frequency anonymized cellular telephone data at the Maryland Transportation Institute (MTI) to investigate the job destruction and job creation effects of COVID19. The PIs will combine the MTI data with other data sources (e.g. HERE, QCEW, etc.) and use the data and Dingell & Nieman method to construct occupational composition indices based on all 968 Occupational Employment Survey (OES) that allows for teleworking at various locations. The PIs will develop a model of job destruction and job destruction of the various job categories at particular locations. The PIs will then use the indices based on the data constructed to estimate the job destruction/creation model at the granular level. The panel structure of the data allows the PIs to study the short term as well as the long term employment effects of economic shocks. Besides the methodological innovation in this study, the results will also provide guidance on policies to counter the effects of the current and possibly future pandemics. The results will also establish the US as the global leader in understanding the employment effects of pandemics and how to develop policies to reduce its effects.

Coronavirus, New Patterns in Electricity Demand, and Energy Inequality

Yueming Qiu (Principal Investigator)
Destenie Nock (Co-Principal Investigator)nequality

The aim of this project is to advance national health and welfare through investigating the impact of the coronavirus pandemic on electricity demand. The pandemic has resulted in widespread stay-at-home policies meaning that, vulnerable populations such as those with low income, ethnic minorities, and the elderly might face a disproportionally higher increase in electricity expenditure. The likely inequitable energy impact on these groups could be a result of less energy-efficient homes, increased need for electrical appliances (i.e. school computers), and larger household sizes. The resulting higher energy expenditure burden might imply constraints of these groups to create a comfortable indoor environment, which is particularly vital to maintaining good health during a pandemic. This project will (1) quantify the electricity expenditure re-distribution and uncover how this relates to the wealth redistribution as lay-offs increase; (2) develop a deeper understanding of the pandemic's impact on the electricity grid for different consumer types. This work will inform policies that can reduce the energy burden of the most vulnerable populations whose job security, educational development, and mental health are linked to their ability to satisfy their energy demand, particularly during an international crisis.

In pursuit of this research the research team will conduct consumer-level statistical and machine learning analyses using large samples of individual-consumer-level hourly smart meter electricity demand data in the residential, commercial, industrial, and agricultural sectors in Phoenix metropolitan, Arizona, and Chicago metropolitan, Illinois, as well as electricity and heating gas consumption data in New York City, New York. This will be combined with detailed socio-demographic and building attribute information for residential consumers which will allow for a more detailed analysis of electricity impacts, and impacts on energy expenditure. We will also conduct a consumer survey examining the changes in electricity consumption behaviors due to the pandemic. The pandemic has also highlighted the need to improve the measurement of energy inequality and poverty. We will develop an index based on the distance in energy consumption levels to measure the inability of households to obtain sufficient energy services. Once developed this index can then be used by the utilities and policymakers to identify the consumers suffering from energy poverty in a timely and precise fashion. We will examine energy poverty in the context of load profiles so that our findings can inform utilities regarding what types of demand-side management practices such as time-of-use pricing can help or hurt the consumers suffering from energy poverty. The consumer-level analysis on the load profiles will provide heterogeneous impact estimates and thus can better help utilities forecast future impacts on load and evaluate the uncertainties. This is especially important because there might be long-term structural changes in the demand patterns so that the load curves may not rebound back to the original patterns when the virus is gone. Lastly, our consumer-level analysis will quantify the heterogeneous changes in the temperature-response functions (how consumers change their electricity consumption in response to temperature change) due to the crisis. Quantifying the impact of such a complex redistribution of electricity demand on the load shape will help utilities better plan for capacity and portfolio management. This will be particularly important if coronavirus is persistent throughout the summer when cooling loads are high, or when there is a possibility of more waves of coronavirus.

May 10, 2020