| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | June 4, 2020 |
| Latest Amendment Date: | June 4, 2020 |
| Award Number: | 2029866 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jacqueline Meszaros
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | June 15, 2020 |
| End Date: | May 31, 2023 (Estimated) |
| Total Intended Award Amount: | $135,593.00 |
| Total Awarded Amount to Date: | $135,593.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
104 AIRPORT DR STE 2200 CHAPEL HILL NC US 27599-5023 (919)966-3411 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Carolina Population Center Chapel Hill NC US 27599-8120 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | COVID-19 Research |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
This Rapid Response Research (RAPID) grant will improve understanding of drivers of disease transmission in rural areas, providing insights for improved decision-making and public health management in rural communities. The majority of current research relevant to modeling COVID-19 spread is focused on urban systems. Given the vast differences in demographic, social mobility, transportation, and built environment characteristics between rural and urban systems, it is expected that rural spread patterns are different from urban. This project will examine whether this is the case; identify key factors that account for any differences; and how models should be adjusted to better fit rural conditions. Based on those findings, the team will build an epidemiological model well suited to rural communities. The project will also begin the process of evaluating how well risk prevention policies and messages could be adjusted for maximum effectiveness in rural communities. The research team will share relevant findings with county- and state-level public health managers and help them incorporate the findings into best practices.
The goal of this research is to conduct an informed process of spatial, geographic, public health, wastewater infrastructure and social data collection and synthesis for improved pandemic management in rural communities. The project team will examine 3 rural and 3 urban counties in North Carolina. Initial data will be collected and synthesized from COVID-19 epidemiological data at the state and county level, as well as other available published information from COVID-19 research as valuable input to a susceptible-exposed-infected-recovered (SEIR) modeling base approach. From there, a guided process of data collection and synthesis will be used to prioritize factors of importance in disease transmission across rural and urban. Available data sources include health surveillance, cell-phone based mobility, land use features, commuting patterns, essential business proximity, public health infrastructure, and medical care availability. The team will simultaneously gather wastewater samples in selected sewage and package treatment systems across selected counties to quantify the prevalence of SARS-CoV-2 in the relevant locations. This will provide an additional non-clinical metric for disease prevalence. Given current inaccuracies of clinical testing data, particularly in rural areas, these disease-presence data will constitute a key measure of disease presence against which to validate insights emerging from the SEIR model as well as to assess other metrics being used in public health models. The final stages of the project will be to rectify the conceptual framework of disease transmission drivers, and initial SEIR/spatial modeling approaches with NC surveillance system data, and work with county and state level public health managers and epidemiological to incorporate the findings into best practices.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Over the duration of this project, we developed valuable partnerships with NC DHHS, and wastewater treatment plant operators across the entire state. At the start of this NSF RAPID project, research efforts to conduct wastewater-based epidemiology existed at only very few NC academic institutions other than our laboratory. Over the course of this funded project, we developed collaborations across NC DHHS, NC DEQ, WWTP stakeholders and multiple academic institutions. We trained more than 15 different people across three laboratories to conduct digital PCR based quantification of SARS CoV-2 targets, including variants of concern, using appropriate quantitative controls and quality control elements. This collaborative effort built the foundation and capacity for the State of NC to not only conduct, but to lead the development of, wastewater-based surveillance on behalf of the CDC and the newly developed National Wastewater Surveillance System.
We established new relationships with wastewater utilities, municipal stakeholders, sanitation districts, joint powers agencies, and others interested in developing improved surveillance systems using wastewater surveillance approaches, and we bridged the gap of developing relationships with rural and small scale system operators that to this point in the process of wastewater surveillance were largely being ignored. We also succeeded in securing additional funding and analytical capabilities that allowed us to improve analytical throughput.
We collected, downloaded, compiled and analyzed COVID-19 case, hospitalization and death data in the context of running three different types of models that captured, Emerging Hot Spot Analysis (EHSA) model, Local Outlier Analysis (LOA) model, and Change Point Detection models. The EHSA and LOA models were run for the Alpha variant period, the Beta variant period, the Delta variant period, and the full period across all three variants. Each model was run using two different neighborhood definitions for determining statistical significance. The "Neighborhood Time Step" assessed significance using only the case data from adjacent zip codes for the current week plus one week on either side. The "Global Time Step" used all data from all zip codes and all weeks in the time period. The Change Point Detection model was only run on the full time period that encompassed all three variants.
Last Modified: 12/06/2023
Modified by: Rachel T Noble
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