By Lon Wagner,
With COVID-19, what we know can be frightening: more than 180,000 deaths in the United States as of late June stretched demand on healthcare workers and supplies, and development of a vaccine is at least months or years away.
To Lauren Childs, assistant professor of mathematics, it’s what we don’t know that makes it difficult for communities to prevent the spread of the coronavirus. One big unknown is that evidence indicates that people who do not have symptoms can transmit the virus.
“If symptoms are associated with transmission, one can avoid anyone with symptoms and be nearly risk-free,” Childs said. “If symptoms are not a good way to identify who is transmitting, then testing becomes much more important.”
Childs realized that communities would need more information about who was being tested, who was not, and how many could be carriers of the virus — if we are to contain the pandemic. The National Science Foundation (NSF) awarded Childs with a $180,000 Rapid Response Research (RAPID) grant to try to fill in the gaps.
“A key aspect of all the interventions we are using — whether it’s contact tracing, quarantine, travel restrictions, or social distancing — is knowing who is infected,” Childs said, “and that’s something that can only be determined by testing.”
Childs, the Cliff and Agnes Lilly Faculty Fellow, has extensive experience building computational models to analyze infectious disease, and will work with mathematics doctoral student Melody Walker to develop, analyze, and simulate the COVID-19 models. Initially, they will work with a publicly available dataset from Iceland, which is updated daily.
Childs’ aim is to help countries, states, and other jurisdictions deter-mine the most effective ways to staunch the spread of the virus based upon their particular testing strategy and rate of testing. For instance, if a community is testing only individuals with symptoms, what actions should it take in terms of school closings, social distancing, and usage of masks? If it takes five days to get back test results, as opposed to one or two days, how should a community adapt its strategy?
With the Iceland data she will combine data from Johns Hopkins University, which includes which includes sources such as the World Health Organization and Centers for Disease Control.
A new source of information about who may be infected with the COVID-19 virus without knowing it has recently emerged: people undergoing medical procedures that were previously determined to be “non-essential” and postponed.
“Now that non-essential medical procedures are being rescheduled, and in most cases a COVID test is required beforehand,” Childs said, “we are seeing more positive results from asymptomatic people.”
One end result of the NSF RAPID grant will be to create a publicly accessible dashboard where you can piece together the types of testing, sensitivity and specificity, results, turnaround time, and other factors. One region may have tests that are 99 percent accurate, yet take five days for results. Another may have a test that is 80 percent accurate, but gets results in 15 minutes. How should their mitigation strategies differ? A health district could look at Childs’ model and “use that to make informed decisions.”