Abstract- Jon Fintzi - National Institute of Allergy and Infectious Diseases ( NIAID)
Title: Using multiple data streams to estimate and forecast SARS-CoV-2 transmission dynamics
Title: Using multiple data streams to estimate and forecast SARS-CoV-2 transmission dynamics
Abstract:
Abstract:
Monitoring of outbreak transmission dynamics and evaluation of public health interventions are critical to interrupting the spread of the novel coronavirus (SARS-CoV-2) and mitigating morbidity and mortality caused by coronavirus disease (COVID-19). Avoiding or delaying the implementation of blunt transmission mitigation policies, such as stay-at-home orders and school closures, is only sustainable if policy-makers base decisions of whether to relax or intensify mitigation policies based on careful monitoring of regional and local transmission dynamics. Formulating a regional mechanistic model of SARS-CoV-2 transmission dynamics using streaming surveillance data offers one way to accomplish data-driven decision making. For example, to detect an increase in new SARS-CoV-2 infections due to relaxation of previously implemented mitigation measures one can monitor estimates of the basic and/or effective reproductive number. However, parameter estimation can be imprecise, and sometimes even impossible due to lack of structural identifiability, because surveillance data are noisy and not informative about all aspects of the mechanistic model, even for reasonably parsimonious epidemic models.
Monitoring of outbreak transmission dynamics and evaluation of public health interventions are critical to interrupting the spread of the novel coronavirus (SARS-CoV-2) and mitigating morbidity and mortality caused by coronavirus disease (COVID-19). Avoiding or delaying the implementation of blunt transmission mitigation policies, such as stay-at-home orders and school closures, is only sustainable if policy-makers base decisions of whether to relax or intensify mitigation policies based on careful monitoring of regional and local transmission dynamics. Formulating a regional mechanistic model of SARS-CoV-2 transmission dynamics using streaming surveillance data offers one way to accomplish data-driven decision making. For example, to detect an increase in new SARS-CoV-2 infections due to relaxation of previously implemented mitigation measures one can monitor estimates of the basic and/or effective reproductive number. However, parameter estimation can be imprecise, and sometimes even impossible due to lack of structural identifiability, because surveillance data are noisy and not informative about all aspects of the mechanistic model, even for reasonably parsimonious epidemic models.
To overcome this obstacle, at least partially, we propose a Bayesian modeling framework that integrates multiple surveillance data streams. Our model uses both COVID-19 incidence and mortality time series to estimate our model parameters. Importantly, our model for incidence data takes into account changes in the total number of tests performed. This modeling feature ensures that we do not mistake increases/decreases in cases for increases/decreases in testing. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California. Our results suggest that California Department of Public Health stay-at-home order, issued on March 19, 2020, lowered the SARS-CoV-2 effective reproductive number in Orange County below 1.0, which means that the order was successful in suppressing SARS-CoV-2 infection. However, subsequent ``re-opening'' steps took place when thousands of infectious individuals remained in Orange County, so increased to approximately 1.0 by mid-June and above 1.0 by mid-July.
To overcome this obstacle, at least partially, we propose a Bayesian modeling framework that integrates multiple surveillance data streams. Our model uses both COVID-19 incidence and mortality time series to estimate our model parameters. Importantly, our model for incidence data takes into account changes in the total number of tests performed. This modeling feature ensures that we do not mistake increases/decreases in cases for increases/decreases in testing. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California. Our results suggest that California Department of Public Health stay-at-home order, issued on March 19, 2020, lowered the SARS-CoV-2 effective reproductive number in Orange County below 1.0, which means that the order was successful in suppressing SARS-CoV-2 infection. However, subsequent ``re-opening'' steps took place when thousands of infectious individuals remained in Orange County, so increased to approximately 1.0 by mid-June and above 1.0 by mid-July.