Berry, Scott M

Monitoring Population Health through Wastewater Surveillance for Infectious Diseases

Wastewater surveillance is an epidemiological tool that tests wastewater samples for disease biomarkers released in human waste, such as pharmaceutical metabolites and viral RNA, to track disease trends in a community. This approach, in contrast to clinical testing, allows for more cost-effective and scalable disease monitoring from the facility level (hospitals and nursing homes) to community and state levels (wastewater treatment plants). During the COVID-19 pandemic, researchers demonstrated the utility of wastewater surveillance on university campuses and various metropolitan areas across the world and established its potential for early detection of infectious diseases in a community, which could translate to lives and healthcare expenses saved. Our lab aims to increase the accessibility and implementation of WBE to enable data-driven community health management in resource-rich and resource-limited settings. Our approach is to reduce the technical, logistic, and financial investments required to establish a wastewater surveillance system through technical innovations while exploring the full potential of wastewater surveillance in supporting healthcare. As such, our research activities include: workflow and device development to simplify viral extraction and detection from wastewater; agent-based epidemiological modeling to inform resource allocation and assess wastewater testing approaches; and third-generation sequencing using Oxford Nanopore technology (ONT) to track antimicrobial resistance (AMR) in various health populations.

PI: Scott Berry (smbe264)

Oxford Nanopore Sequencing for Anti-Microbial Resistant Genes

This project aims to identify antimicrobial resistant genes present in wastewater samples in order to guide responsible drug administration at hospitals and in the larger community.  To accomplish this, we use ONT long-read sequencing to enable metagenomic analysis on wastewater samples. EPI2ME, an open-source analysis software provided by ONT, is used to basecall, align, and identify AMR genes of interest. With the high biodiversity in wastewater samples, secondary and tertiary metagenomic analyses for a single sample can take a single laptop multiple days to complete. Access to additional computing resources can speed up analyses considerably and accelerate grant writing efforts.

Computational Methods:

basecalling, read assembly, read mapping

Software:

EPI2ME (free, open-source)

Personnel:

Dalton Strike (Graduate, wdst234), Added on MCC cluster, 06/08/2023 

Agent-based Modeling of Wastewater Surveillance

This project aims to develop an agent-based model that enhances understanding of the effectiveness of centralized versus local mobile-based wastewater surveillance to the community spread of coronavirus such as SARS-CoV-2. The model incorporates an SEIR (susceptible, exposed, infected, and recovered) epidemic model, the county-level population geographic distribution information, and the realistic demographic information and transmission networks across different social layers, such as households, schools, workplaces, and communities to simulate community interactions and monitor the coronavirus RNA concentrations in sewer systems and wastewater treatment plants for revealing the epidemic progression. The model will be tested using the public COVID-19 case surveillance data since 2020 and empirical data gathered through local mobile-based wastewater surveillance.

Computational Methods:

Monte Carlo algorithm

Software:

Netlogo (installation required), Python

Personnel:

Dr. Lin Xiang (Faculty),

Alexus Rockward (Graduate, alro257), Added on MCC cluster, 06/08/2023