Friday, June 19, 2020 at 11:00am to 12:00pm
Register here: https://usc.zoom.us/webinar/register
This week our webinar will feature a talk by Prof. Dave Conti and Dr. Abigail Horn. Prof. Conti is in the Division of Biostatistics in the Department of Preventive Medicine and the Norris Comprehensive Cancer Center (NCCC) at USC. Dr. Horn is a Postdoctoral Fellow in the Department of Preventive Medicine at USC.
Incorporation of risk factors in a stochastic epidemiological COVID-19 model for Los Angeles County – by Dave Conti & Abigail Horn
Abstract: We have developed an epidemic compartmental model to address the key questions of (1) When and how the epidemic dynamics will impact health care capacity? (2) What happens to the dynamics of the epidemic when social distancing changes? And (3) How will the epidemic affect different at-risk groups? Our model uses stochastic differential equations and approximate Bayes calculation techniques for parameter estimation. We incorporate external information to inform prior distributions for parameter specification. This includes previous studies on risk factors for COVID-19 to inform differences in illness severity (e.g., advanced age, existing health conditions) and the prevalence of these risk factors in Los Angeles County; mobility data to inform time-varying changes in contact rate; (3) seroprevalence data to estimate the fraction of unobserved illnesses. Our modeling framework enables modifying parameters at different time points, enabling the specification of interventions, e.g., social distancing scenarios. Our model's key contribution over existing models estimating hospital resource demand is that it accounts for combinations of risk factors (age, unhealthy behaviors, existing comorbidities, and combinations of comorbidities), including area-level differences in the prevalence of these risk factors, in model dynamics. Accounting for differential risk for specific populations allows us to estimate the impact of COVID-19 on these populations in Los Angeles, including analyzing findings by race/ethnicity groups, to inform the prioritization of these populations for protection. For example, this allows us to compare our projections to the observed disparities in death rates across race/ethnicity groups and answer the question of whether these differences can be explained by the prevalence of risk factors alone, or if other factors (e.g. disparities in exposure, differences in contact rates) must be involved. We find that death rates are higher for these populations than can be explained by the risk factors alone.
Model-estimated parameters, projections, and probabilities of severe illness for different combinations of risk factors are provided online on our project website: uscbiostats.github.io/COVID19
Bio: Dave Conti, PhD
Dr. Conti is a Professor in the Division of Biostatistics in the Department of Preventive Medicine and the Norris Comprehensive Cancer Center (NCCC) at USC. He is Associate Director for Data Science Integration for the NCCC at USC and the Kenneth T. Norris, Jr. Chair in Cancer Prevention. His research focuses on study design and statistical methods for genetic and environmental epidemiology. His methodological research aims to integrate biological knowledge in statistical modeling. He has several past R01s to develop and investigate the use of Bayesian hierarchical models for genomic studies and to develop statistical methods to integrate genetic and omics data. He has developed statistical and bioinformatics software for analysis. Most recently, this includes LUCIDus (https://cran.r-project.org/web/packages/LUCIDus/index.html) and hJAM (https://cran.r-project.org/web/packages/hJAM/index.html). He is currently Director of the Data Science Core for a U19 project investigating aggressive prostate cancer in African-American men integrating the built environment, germline and somatic genetic profiles, gene expression, and tumor microenvironment data. He is also Co-Investigator on a P01 focusing on developing statistical methods for integrated genomics analysis and numerous applied epidemiology studies.
Abigail Horn, PhD
Dr. Horn is a Postdoctoral Fellow in the Department of Preventive Medicine at the University of Southern California and a member of the Center for Applied Network Analysis (CANA). Her research interests involve network epidemiology, probabilistic modeling, and data science in the context of public health, with a focus on foodborne diseases and diseases of diet. She received a Ph.D. from the Institute for Data, Systems, and Society at MIT, and a Bachelor's in Physics from the College of Creative Studies at UCSB. Before joining USC, she led a research project at the German federal-level food protection agency to develop, implement, and evaluate algorithms and decision support systems for modeling food supply networks to identify the source of large-scale outbreaks of foodborne disease. Her work has been funded by the NIH, the Robert Wood Johnson Foundation, the Bayer Foundation, the German Research Foundation, and the Santa Fe Institute.