Surveillance surveys give clearer picture of COVID’s spread

Door-to-door surveillance surveys, which collect information from households or individuals in a specific geographical area, can often provide more precise estimates of how many people are infected with COVID-19 or have immunity to COVID-19 at any given point in time than relying on self-reporting and self-testing, a Cornell-led research group has found.

Dr. Casey Cazer, DVM ’16, Ph.D. ’20, an assistant professor in the Department of Clinical Sciences in the College of Veterinary Medicine, was lead author of the study, which published Jan. 29 in AJPM Focus. Cazer’s lab partnered with the Tompkins County, New York, public health department and Cayuga Health System.

Early in the pandemic, the prevalence of COVID-19 cases was determined by how many people were testing positive every day, using PCR tests. While the tests themselves are considered highly accurate, there were limitations to this method: Not everybody had the same access to testing, and those with asymptomatic infections were unlikely to seek out a test.

In February, April and October of 2022, the research team surveyed three municipalities in Tompkins County, using a two-stage cluster sampling model developed by researchers at Oregon State University. While surveillance studies are often considered the gold standard for collecting reliable data, they can be labor- and resource-intensive.

Using census blocks, the teams randomly chose different areas to sample, and surveyed one municipality at a time. Cazer said student volunteers would knock on approximately every 10th door and ask people if they wanted to participate in the study and give a sample.

Participants provided self-administered nasal swabs, which were tested for the virus as well as for antibodies. They also provided demographic information, symptom history and vaccination status, and answered questions on COVID-19 prevention behaviors and attitudes. In all, 233 individuals were sampled.

Cazer said the results showed a high level of asymptomatic infections.

“We found that some people had antibodies from a previous infection, but they did not report ever having a positive COVID test,” Cazer said. “It’s not surprising, but it confirms that there was a large amount of asymptomatic spread.”

The researchers emphasized the importance of leveraging existing relationships with institutions, thoroughly and consistently training volunteers, and creating new partnerships to make this method more efficient and sustainable.

“Some of the things that contributed to our success were the fact that we had really strong partnerships with Tompkins County [Whole Health] and Cayuga Health System,” said Cazer, who’s also an assistant professor in the Department of Public and Ecosystem Health and associate hospital director of the Small Animal Community Practice Service in the College of Veterinary Medicine.

“Because of those partnerships,” Cazer said, “we were able to use existing infrastructure and tools. So, we were able to run our COVID tests through the Cornell COVID-19 Testing Lab, which was a partnership with Cayuga Health System, and we were able to use existing technology systems from the Cayuga Health System to de-identify our samples and enable us to collect anonymous information.”

The researchers built off the work by the Oregon State teams, combining that with Cornell’s training for handling infectious samples. “Putting together a combination of borrowed and new training helped our team members be successful,” Cazer said.

The team hopes that the surveillance surveys provided timely and reliable evidence to inform local public health officials’ decision-making efforts in response to the COVID-19 pandemic. They encourage other universities or health departments that might want to do this kind of work to increase their odds of success by leveraging existing partnerships and tools.

Cazer’s team is currently collaborating with members of the School of Operations Research and Information Engineering, in Cornell Engineering, to understand how uncertainty impacts epidemiologic models of pandemics.

“There were a lot of different models and forecasts available of what was going to happen with COVID-19,” Cazer said. “Because there were limited amounts of information available, sometimes those models were not very accurate. We’re interested in trying to understand how that sort of uncertainty about what’s happening in real time impacts our ability to forecast those diseases and will help us to develop better forecasts for the next pandemic.”

Christina Frank is a freelance writer for the College of Veterinary Medicine.

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