Yongtao Cao, a statistician in the Department of Mathematical and Computer Sciences, has written a paper on monitoring the SARS-CoV-2 virus in wastewater, and has written an app to predict the number of COVID-19 cases for a provided SARS-CoV-2 concentration
level in the wastewater.
As the COVID-19 pandemic continues, clinical testing for COVID-19 at individual levels does not by itself provide a holistic indicator of community health risk. The main reasons are (1) most coronavirus infections in the US are caused by asymptomatic
and pre-symptomatic people; and (2) a considerable percentage of patients recovered from COVID-19 could still carry and transmit the virus. Because of this, a growing practice and research area for improving community health is using wastewater-based
epidemiology (WBE) to monitor the SARS-CoV-2 virus in wastewater, in conjunction with the clinical testing data.
A group of IUP faculty and administrators started meeting with Indiana Borough during summer 2020 with the aim of building an effective wastewater surveillance system within the borough as well as on the IUP campus. Cao, a member of this group, has written
a research paper titled “On Forecasting the Community-wide COVID-19 Cases from the Concentration of SARS-CoV-2 in Wastewater.”
Along with this paper, Cao is also maintaining a web app. Currently, this app can (1) predict the number of COVID-19 cases for a provided SARS-CoV-2 concentration level in the wastewater,
and (2) forecast the number of COVID-19 cases within Indiana Borough and IUP for three weeks in the future.
In addition to this work, Professor Cao is also working on measuring the impact of COVID-19 on college students and on building a support system within the campus environment for college student patients with COVID-19. Those interested in this research
can contact Cao directly at email@example.com.
Yongtao Cao is an associate professor and statistician in the Department of Mathematical and Computer Sciences. He teaches statistics for business,
health science, and social sciences at the undergraduate level, and survey methodology, experimental design, and time series data analysis at the graduate level. His research interests include design of experiments, multi-objective optimization,
and application of machine learning on medical and health science, environmental science, and education. His research activities in “data + science” has resulted in publications in Statistics and Computing, Journal of Statistical Planning and Inference, Journal of Quality Technology,
Quality Engineering, Archives of Physical Medicine and Rehabilitation, Aging Cell, Science of the Total Environment, and others.