Date of Award

January 2024

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)


School of Public Health

First Advisor

Virginia Pitzer

Second Advisor

Anne Wylie


The outbreak of the SARS-CoV-2 pandemic in 2019 has emphasized the need for accurate and timely estimation of infectious disease cases. An important aspect of controlling infectious diseases is the development of robust nowcasting methods, which involve estimating the current and near-future state of an epidemic using real-time data. Google Trends (GT) has emerged as a valuable tool in epidemiology, allowing researchers to track and predict disease outbreaks by analyzing patterns in internet users' searches. While GT provides real-time information on search queries related to infectious disease symptoms and treatment, it has limitations such as data reliability, privacy concerns, sampling bias, and interpretation challenges. To address these limitations, this review proposes combining GT data with wastewater surveillance, which provides concrete measures of community infection rates. By merging the strengths of both datasets, public health officials can gain a more nuanced understanding of disease dynamics and make informed decisions to safeguard public health. The proposed framework for data integration and analysis involves collecting GT data and wastewater samples, merging them at a geographical scale, and analyzing the relationship between public interest and disease prevalence. This integration can significantly improve epidemiological models and guide targeted interventions. By identifying regions with heightened public interest, public health officials can focus on specific needs and optimize resource allocation. In conclusion, Google Trends has proven to be a valuable tool in monitoring disease outbreaks, but its findings should be corroborated with other data sources. Integrating GT with wastewater data offers a promising approach to enhance prediction accuracy and response strategies in public health. This innovative blending of diverse data sources has the potential to revolutionize our understanding and control of epidemics.


This is an Open Access Thesis.

Open Access

This Article is Open Access