MS08 - MEPI-1
Cartoon Room 2 (#3147) in The Ohio Union

Integrating Data with Epidemic Models: Challenges and Opportunities

Friday, July 21 at 10:30am

SMB2023 Follow Friday during the "MS08" time block.
Room assignment: Cartoon Room 2 (#3147) in The Ohio Union.

Bruce Pell, Fuqing Wu

Description:

The integration of data with epidemic models is critical for gaining insights into the transmission of infectious diseases and for developing effective public health interventions. However, the process can be impeded by various challenges, including the availability and quality of data, as well as the complexity of models. To address these issues, we propose a mini-symposium that will bring together experts in mathematical modeling, epidemiology and environmental science. The symposium will provide a platform for sharing and discussing innovative approaches to data integration, such as the use of wastewater data. Additionally, the symposium will address the challenges of incorporating such data into dynamic models.

Tin Phan

Los Alamos National Laboraty (Theoretical Biology and Biophysics)

"Integrating wastewater surveillance data with dynamic models to track and predict viral infections and beyond"

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. Yet, wastewater surveillance data is not independent of transmission dynamics and its integration with dynamic within-host and between-host models is necessary to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.

Matthew D. Johnston

Lawrence Technological University (Department of Mathematics + Computer Science)

"Integrating Virus Variant Data into a Two-Strain SIR Model with Cross-Immunity"

We consider a dimensionally-reduced infectious disease model involving two competing virus strains with asymmetric temporary immunity periods and partial cross-immunity. In the utilized reduction method, we assume that the original strain remains at its endemic steady state as the emerging strain enters the population. We are then able to derive explicit conditions for competitive exclusion and coexistence of the two strains depending on the relative basic reproduction numbers, temporary immunity periods, and degree of cross-immunity. We are also able to fit to COVID-19 variant data to estimate the changes in a variant's transmissibility and the degree of cross-immunity.
Additional authors: Bruce Pell, Lawrence Technological University; David Rubel, Lawrence Technological University

Fuqing Wu

The University of Texas Health Science Center at Houston (Department of Epidemiology, Human Genetics, and Environmental Sciences)

"A Wastewater-based dynamic model for epidemiological inferrence"

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020–Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6–16 days and 8.3–10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.
Additional authors: Tin Phan: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, NM, USA; Samantha Brozak: School of Mathematical and Statistical Sciences, Arizona State University, AZ, USA; Bruce Pell: Department of Mathematics and Computer Science, Lawrence Technological University, MI, USA; Anna Gitter: The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030; Amy Xiao: Center for Microbiome Informatics and Therapeutics; Department of Biological Engineering, Massachusetts Institute of Technology; Kristina D Mena: The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030; Yang Kuang: School of Mathematical and Statistical Sciences, Arizona State University, AZ, USA;

Shokoofeh Nourbakhsh

Public Health Agency of Canada (PHAC) (National Microbiology Lab / Public Health Risk Sciences / Infectious Disease Modelling)

"A Wastewater-based Epidemic Model for SARS-CoV-2"

Wastewater-based epidemiology has proven to be a reliable indicator of community incidence. It provided valuable ongoing information on the state of the COVID-19 pandemic, mainly when the Omicron variant emerged and overwhelmed clinical surveillance. We present a mathematical model coupled with wastewater and clinical data from Canadian cities to estimate disease prevalence in the sampled communities and provide short-term epidemic forecasts to support public-health decision-making. Our endeavour highlighted the lack of a quantitative framework on viral pathogen fates within the urban sewer system hamper the epidemiological interpretation and the calibration of wastewater-based epidemic models due to the significant variance in measured viral concentration downstream at the wastewater treatment plants.
Additional authors: David Champredon, Public Health Risk Sciences, National Microbiology Laboratory, Public Health Agency of Canada

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