Public Health – Frost Institute for Data Science & Computing

COVID-19 Transmission Model

An innovative University of Miami (UM) COVID-19 study has demonstrated the power of applying advanced computer resources to understanding viral transmission rates.

The collaborative UM research team conducted a granular study of classroom interactions and the likelihood of transmitting the SARS-CoV-2 virus in an indoor setting.

“Unlike current models of COVID-19 transmission from reported or assumed interactions, we leveraged high-resolution observations to simulate infectious processes,” said Chaoming Song, Associate Professor of Physics and a Member of the Institute for Data Science and Computing (IDSC).

“To our knowledge, this is the first study to apply Susceptible, Exposure, Infected, Recovered (SEIR) models to physically defined interactions in these contexts.” Song added that the likelihood of transmission increases when individuals are closer to each other, especially when face-to-face. “But we can’t tell which individuals will get the virus or not, because the answers are probabilistic,” he added.

Professor Song was the senior lead author of a study, “Simulating COVID19 Transmission From Observed Movement: An Agent-Based Model of Classroom Dispersion,” recently posted at arXiv.org and under review at Scientific Reports. UM coauthors included Zhang Yi (above right), a Physics Department doctoral candidate who shared first authorship, and Yudong Tao (at left), Research Assistant.

They were joined by Mei-Ling Shyu, Ph.D., Associate Chair and Professor at in the Department of Electrical and Computer Engineering; and Lynn K. Perry, Ph.D., Associate Professor of Psychology, as well as and Prem R. Warde, MSIE, former Director, Care Transformation, University of Miami Health System (L to R, below: Shyu, Perry, and Warde)

“With data science studies, it’s important to have people from different disciplines working together share their ideas and approaches to meeting global challenges”

“With data science studies, it’s important to have people from different disciplines working together share their ideas and approaches to meeting global challenges,” said Song.

Coauthor Daniel Messinger, Associate Professor in the Child Division of the Department of Psychology, Coordinator of the UM Developmental Psychology Program, and Director of Social System Informatics at IDSC Data Ethics + Society, said the leading-edge study drew on data from prior studies used ultrawideing radio frequency identification (RFID) sensors to track the real-time movements and directional orientation of children and their teachers in four preschool classes over a total of 34 observations.

“Decreased classroom density and teacher vaccinations had highly beneficial impacts on transmission rates”

To develop their model, the UM team used risk guidelines published by the U.S. Centers for Disease Control and Prevention (CDC) to estimate the transmission impact of one infected student attending class on the proportion of overall infections, the average transmission rate, and the time lag to the appearance of symptomatic individuals.

“Using these metrics, we found that decreased classroom density and teacher vaccinations had highly beneficial impacts on transmission rates,” said Messinger. A classroom with only half capacity was associated with an 18.2% drop in overall infection rates, while teacher vaccinations were associated with a 25.3% drop.

“These simulation results of classroom transmission dynamics may inform public policy in the face of COVID-19 and similar infectious threats,” added Messinger. “Our model can be applied to other airborne viruses, including influenza and the Omicron COVID variant. Recognizing the global importance of viral transmission concerns, we have also made our model available for clinicians and researchers conducting their own simulations.”

Zhang, Yi, et al. “Simulating COVID19 Transmission From Observed Movement: An Agent-Based Model of Classroom Dispersion.” arXiv preprint arXiv:2108.07808 (2021).

Covid-19 Wastewater Testing

South Florida RAD: Using Wastewater Measurements to Predict COVID-19 in the Community

The University of Miami (UM) and Weill Cornell Medicine (WCM) established a collaborative study for human and environmental surveillance of SARS-CoV-2, the virus that causes COVID-19 disease, including surface, air, and wastewater based surveillance efforts.

Five shared resources at UM helped establish and are currently providing coordinated support for ongoing surveillance and research, providing a case study of how a diverse array of shared resources can work together to facilitate human and environmental surveillance for SARS-CoV-2. The shared resources involved in this effort include a group of Sylvester Comprehensive Cancer Center (SCCC) Shared Resources, including the Behavioral and Community Based Research Shared Resource, Biospecimen Shared Resource, and Onco-Genomics Shared Resource, along with the Miami Center for AIDS Research (CFAR) Laboratory Sciences Core, and the Miami Clinical and Translational Science Institute (CTSI) Biostatistics Collaboration and Consulting Core.

UM has deployed an extensive human surveillance testing, tracking, and tracing system to monitor students, faculty, and staff, and widespread wastewater surveillance of SARS-CoV-2 from buildings on all campuses. This surveillance includes a major hospital that is part of UM and that treats COVID-19 patients.

WCM has established an international consortium for SARS-CoV-2 environmental surveillance. The goals of this UM/WCM study are to generate, optimize, standardize, and compare SARS-CoV-2 human and wastewater surveillance with various sampling, processing, detection, and analysis approaches, and to integrate wastewater data with community and hospital COVID-19 prevalence, with the aim of developing predictive models of local and community level spread of COVID-19 and also the spread of other existing and emerging pathogens.

A significant impact of this research will be that it compares SARS-CoV-2 human surveillance data to WBT measurements of SARS-CoV-2, non-COVID microbes, and physical chemical parameters, to improve sensitivity and specificity of wastewater measurements. Results will be used to standardize sample collection and concentration procedures. Innovative methods of sample concentration and detection will be explored to provide for more rapid processing and lower limits of detection, while at the same time providing for simplified detection procedures. Results of measurements will be used to develop a model that predicts local and community spread of COVID-19 based upon WBT. Thus, the results of the proposed research will broaden the scope of communities able to implement WBT to augment human-disease surveillance programs. Moreover, the results will inform and improve public health and safety strategies on the local community and municipal levels and may also impact public health strategies at the state, national, and international levels.

The covidsfrad.org website presents lessons learned, current results, and future directions, with a focus on the role and impact of the shared resources.

Shared Resources

SYLVESTER COMPREHENSIVE CANCER CENTER Behavioral and Community Based Research Shared Resource Biospecimen Shared ResourceOnco-Genomics Shared ResourceMIAMI CENTER FOR AIDS RESEARCH (CFAR)
Laboratory Sciences CoreMIAMI CLINICAL AND TRANSLATIONAL INSTITUTE (CTSI)
Biostatistics Collaboration and Consulting Core

Participating Institutions

xSAT Covid-19 Dashboard

IDSC has developed a first-of-its-kind early-detection platform for COVID-19 xsat.idsc.miami.edu that enables local residents to self-report symptoms, and researchers and decision makers to visualize data and identify hot spots. By tracking social media posts, analyzing public health data, and creating an interactive online dashboard, University of Miami researchers, and others, will have the ability to identify new clusters of COVID-19 cases in real time, rather than days or weeks after an outbreak. Read more “xSAT Covid-19 Dashboard” →

ype: Public Health