The COVID-19 Simulation Integrated Modeling (COVSIM)CovSim2 team brings deep experience in infectious disease modeling and engagement with public health agencies, including work on COVID-19 (as part of first year of funding from CDC and CSTE 2020-2021) and on influenza pandemic (2007 to 2018). Across the team, there is also previous experience in modeling and decision-making for cholera, malaria, HIV, Hepatitis C, guinea worm disease, obesity, diabetes, sepsis, and more. We pair our deep computational experience with domain expertise in public health, communication, and visualization to elicit feedback and disseminate results. Our team has extensive experience building complex models in a variety of clinical and policy settings and using them to inform decision making.

The objective of this RAPID project is to identify and document possible issues of the existing COVID-19 vaccine distribution and administration systems and propose solutions through collecting national vaccine distribution and administration data and quantifying lead times and various performance measures. We plan to collect day-to-day vaccine allocation and shipment data (to track the supply over time) and vaccine administration data from CDC and States. More information on data elements is available in the research plan section. While this data is available at CDC now, it is not available to the public and it is not archived for a long time (as is the case for N1H1 vaccination data) and will be lost if not collected now. We will work with reporters that have collaborated with us before to create a freedom of information act (FOIA) request to access and archive the data. State record this data as well; however, they do not follow a consistent way of reporting the data, and the majority of them only report cumulative data. It is not certain whether they record the daily data, and if so, how long they keep it. As such, the daily data is at great risk of being lost if not collected as soon as possible and many important vaccination data and trends will be lost if the day-to-day data is not available.

To support graduate students to build a knowledge graph in manufacturing supply chain - 1) integrating data from unstructured sources contained within manufacturing company websites; 2) building a knowledge Graph from a large scale text mining of manufacturing science literature.

The U.S. Centers for Disease Control and Prevention������������������s (CDC) ILINet surveillance system plays an important role in our understanding of influenza dynamics and is used to identify seasonal influenza disease burden, severity, epidemic onset and seasonality, but it suffers from reporting delays and limited, opportunistic sampling of the population. FluView for Fall 2020 shows relatively few visits for ILI. To date, approaches based on EHRs or medical claims have been found to be cumbersome for implementation on a dynamic, rolling horizon basis. Moreover, they usually require deep technical know-how to update when diagnosis codes change or new billing is added such as for telemedicine. We propose to use a data factory approach that provides dynamic, easy to update reporting, based on the Fast Healthcare Interoperability Resources (FHIR) standards for EHR. We will demonstrate the capabilities of the system using data from MedStar Health, a 10-hospital system serving Maryland, Virginia, and District of Columbia.

Matriculation and Well-Being Under Emergent Events (MWEE), will harness data from four campuses, engage communities and encourage the development of processes and actions to address this global challenge and answer questions such as: Can we measure and assess our ability to create virtual community through synchronous and asynchronous learning? Can we use data to inform real time risk stratification within campus community? Can we evaluate the effect of social distancing policies enacted on campuses? In doing so, we expect universities to be able to layer the results with data from their campuses to predict retention and graduation rates given emergent events including COVID-19.

While current models of the COVID-19 pandemic have been helpful for initial national and state-level planning, the ability of local government, public health, and health care system leaders to utilize these simpler models to evaluate specific containment, mitigation, and operational strategies is limited. Next-level decision support models need to be representative of the local population and environment, and include interactions between people, resources, and policies. Our multidisciplinary team proposes to develop an integrated simulation model that captures both COVID-19 progression within an individual, within census tracts, and aggregated to counties and the state level. Key elements include socio-demographics such as age, gender, race, and geographic information that locates individuals within households, workplaces, and schools. Example performance measures of the robabilistic forecasts include cases, hospitalizations, deaths over time and by location. Interventions will be incorporated such as school closures, distancing, face coverings, therapeutics, and vaccinations, to result in averted cases, hospitalizations, and deaths. Our proposal builds on established work in influenza and COVID-19, developing and communicating forecasts to key decision makers. The team will conduct interviews with stakeholders to ensure relevance of policies and results. The focus is on rapid development and innovation with communication across multiple platforms.

The CDC Global Health division focuses on control and/or elimination of vaccine-preventable diseases to reduce death and disability globally. Their focus areas include polio eradication, measles elimination, control of rubella, pertussis, Hepatitis B, haemophilia influenza type B, and other diseases in Tier 1 countries such as Afghanistan, Democratic Republic of Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, and the Philippines. Our project goal is to (1) construct a framework to identify the different outputs of our interventions in metrics of health impact, determine how these outputs relate to our selection of interventions, and what other variables affect these results; (2) build disease modeling tools to quantify the effects of vaccination coverage on specific diseases and populations; and (3) develop an Operations Research framework to link the two together to support the selection of effective immunization programs that will maximize the efficiency, effectiveness, and equity of vaccine delivery.

This proposal is for a services agreement between IEEE and NC State University. The agreement is for NC State faculty and other personnel to provide editorial services for the IEEE Transactions on Human-Machine Systems. David Kaber is currently serving as the Editor-in-Chief for this Journal. IEEE Technical Activities Operations will provided funding to NC State for Dr. Kaber to hire a bi-weekly employee to support journal operations, as detailed in the Statement of Work and the Budget Justification statement. Dr. Kaber will continue to manage the journal technical operations, with the help of the journal administrator, through December of 2018.

The Southeastern Regional Medical Center (SRMC) in Lumberton, NC, would like to improve throughput of patients within the Emergency Department (ED). The goal is to have 60% of patients exit the ED within 180 minutes; the current performance is that 40% of patients do so. Ultimately, measures such as this one drive patient satisfaction with the hospital, and thus the measure is a priority of the hospital leadership. Based on benchmarking and discussions with other hospitals, SRMC has asked for analytics and systems engineering approaches to assist in meeting their performance goal. The ISE team proposes engineering analytics and modeling to assist in understanding what actions will improve patient throughput in the ED to meet the performance goal of the hospital.