NSF Org:	ITE Innovation and Technology Ecosystems
Awardee:	UNIVERSITY OF CALIFORNIA, SAN DIEGO
Initial Amendment Date:	September 3, 2021
Latest Amendment Date:	September 3, 2021
Award Number:	2139740
Award Instrument:	Standard Grant
Program Manager:	Mike Pozmantier mpozmant@nsf.gov  (703)292-4475 ITE  Innovation and Technology Ecosystems TIP  Dir for Tech, Innovation, & Partnerships
Start Date:	October 1, 2021
End Date:	September 30, 2023 (Estimated)
Total Intended Award Amount:	$250,000.00
Total Awarded Amount to Date:	$250,000.00
Funds Obligated to Date:	FY 2021 = $250,000.00
History of Investigator:	Ilya Zaslavsky (Principal Investigator) zaslavsk@sdsc.edu
Awardee Sponsored Research Office:	University of California-San Diego 9500 GILMAN DR LA JOLLA CA  US  92093-5004 (858)534-4896
Sponsor Congressional District:	49
Primary Place of Performance:	University of California-San Diego 9500 Gilman Dr La Jolla CA  US  92093-0934
Primary Place of Performance Congressional District:	49
Unique Entity Identifier (UEI):	UYTTZT6G9DT1
Parent UEI:	NUDGYLBB4S99
NSF Program(s):	Convergence Accelerator Resrch, CYBERINFRASTRUCTURE, ECR-EHR Core Research
Primary Program Source:	040100 NSF RESEARCH & RELATED ACTIVIT 040106 NSF Education & Human Resource
Program Reference Code(s):	096Z
Program Element Code(s):	131Y, 7231, 7980
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.083

ABSTRACT

This project will develop and refine a simulation modeling system to help schools and school districts evaluate the effects of interventions to control COVID-19 infections among students, focusing first on schools in San Diego County, CA. The goal is to help individual schools choose the most effective strategies for their unique circumstances, particularly each school’s population, classroom layouts and ventilation, neighborhood infection rates, and school bus commutes. The interactive model will allow decision makers at the school and district levels to evaluate the potential impacts of both pharmaceutical interventions, like vaccination and testing, and non-pharmaceutical safety measures including social distancing, mask-wearing, limiting interactions between different student cohorts, decreasing classroom occupancy, and improving ventilation. Finding the best combination of such measures is central to safe school re-opening, which is critical for improving child education and development and allowing parents to return to work. As school-aged children, many of whom are not vaccinated, are disproportionately affected by new and more dangerous COVID-19 variants, this simulation service will provide much-needed insights to our partners among decision-makers at the school and district level in San Diego County and elsewhere in the nation.

The school infection model simulates aerosol and droplet transmission as students, teachers, and staff (modeled as human agents) interact during typical school day activities, including classroom instruction, cafeteria lunch, recess, and traveling on a school bus. The likelihood that a healthy agent is exposed to the virus increases as they come near infected, asymptomatic agents or spend significant time in poorly ventilated spaces. The model considers spatial information about room layouts and ventilation, and simulates school day schedules down to 5-minute intervals. Building on a successful prototype for elementary schools, this project will extend the model to middle and high schools and to neighborhoods with different socio-economic and demographic characteristics. Additionally, it will enable simulation of infection dynamics for athletics activities and analysis of infection patterns under different COVID-19 variants. Making the model accessible via a science gateway will let school administrators, researchers, teachers, and students run simulations on a supercomputer, then visualize and compare model outputs under different scenarios. As in the prototype phase, the project will engage undergraduate students in all aspects of model development, and in presenting results to public health and education experts and to the general public – an essential component of their training as data scientists working on societally-impactful topics like education and health equity.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.