Date of Award
Fall 2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Chemical and Environmental Engineering (ENAS)
First Advisor
Peccia, Jordan
Abstract
The detection and quantification of viral infectious disease outbreaks is necessary for theeffective implementation of public health interventions that protect human health. Communicable viral diseases are a leading cause of morbidity and mortality worldwide and prior pandemics have had acute economic and social impacts. However, the identification and tracking of community wide outbreaks for most human viruses across the developed and developing world does not occur due to resource constraints. The goal of this dissertation’s research is to develop the science and methodology that underpin the environmental surveillance of viruses, focusing specifically on wastewater monitoring for identifying and tracking viral infectious disease outbreaks within a community. In this dissertation, the viability of quantifying viral concentrations in environmental samples and associating these concentrations to epidemiological indicators is first explored. These analyses were performed using quantitative (q) PCR and droplet digital (dd) PCR techniques to quantify the number of gene copies of SARS-CoV-2, rhinoviruses, human coronavirus OC43, Mastadenovirus, and Norovirus in environmental samples. Results demonstrated that an individual student’s exposure to viruses could be estimated using concentrations measured on school desks and showed that SARS-CoV-2 concentrations in wastewater are related to clinical epidemiological indicators including case rates and hospitalizations and are a leading indicator over these measures. Mathematical models were then created and applied to rigorously predict case rates and other epidemiological indicators from environmental virus concentrations. A simple binomial model allowed for the prediction of exposure from fomites for school children rotating through classrooms. Using wastewater indicators, multi-variate weighted linear models were built and tested that accurately predicted SARS-CoV-2 cases for a variety of cities in Connecticut, USA. This model was then generalized to be location agnostic to demonstrate its broad applicability. Based on the success of SARS-CoV-2 wastewater surveillance, a variety of human respiratory viral pathogens were studied, including Influenza A, Influenza B, Human metapneumovirus (HMPV), and Respiratory syncytial virus (RSV). We showed that these agents could be successfully monitored in wastewater, that they are highly correlated to clinical case rates, and that the lead time identified by wastewater surveillance is correlated to the incubation period of the viruses. The final portion of this dissertation focuses on applying DNA sequencing and analysis approaches to broaden our target selection without the need for developing and performing individual ddPCR assays. We demonstrate that amplicon-based sequencing of wastewater samples can provide important lineage estimations of viral variants that are comparable to clinical sequencing results. Probe-based hybridization was also used to select for and amplify 66 viral pathogens and showed that the abundances of these viral sequences are reflective of the underlying viral concentrations as measured by ddPCR. Overall, this work demonstrates that wastewater surveillance is an effective and timely indicator of viral disease outbreaks in a community and is broadly applicable to a diversity of human viruses and locations.
Recommended Citation
Zulli, Alessandro, "Environmental Surveillance of Viral Pathogens for Identifying Infectious Disease Outbreaks" (2023). Yale Graduate School of Arts and Sciences Dissertations. 1147.
https://elischolar.library.yale.edu/gsas_dissertations/1147
