U of T Researchers Find That Coronavirus Has Not Been Controlled as Hoped
by Françoise Makanda, Communications Officer at DLSPH
The coronavirus epidemic started earlier and the disease transmission has not yet been controlled in China, a new U of T disease-transmission model suggests.
“You can’t get up to that level of cases if the epidemic started in December even if you pushed the reproduction really high,” says DLSPH Professor David Fisman, one of the model’s co-creator, available on the Annals of Medicine with the report.
“If you have a reproduction number of 3, the epidemic could not have stated in mid-December because, according to the graph, it is undershooting the cases that were found in December.”
Reproduction in epidemiology refers to the number of secondary cases a single case can infect in a susceptible population.
“It had to be earlier which raises some interesting questions about how this emerged. The plausible start date seems to be mid-November,” says DLSPH Professor David Fisman.
Working with DLSPH Professor Ashleigh Tuite, Fisman points to two green dots on the interactive graph. The Chinese government implemented measures to contain the virus’ spread by mid-January but according to the graph, the disease’s reproduction number has not been reduced below 1—the level needed for control.
“Even with the reproduction being less than 1— one case makes less than one new case before it goes away and the epidemic peaks— using the model, when looking at January 14th, that level control is not happening because the observed cases are exceeding that level.”
Reproduction numbers have not been less than one in the last week since the WHO formally declared the epidemic a public health emergency of international concern.
The model replicates epidemiological scenarios using open-access data that Professors Tuite and Fisman update daily. Fisman says it takes about a week to infect someone and for the person to exhibit the virus’ symptoms. “What you’re seeing today [in the graph] is what happened last week.”
The model also allows users to create plausible epidemic curves or scenarios to observe the outbreak’s trajectory. Fisman says it is a simplified version of reality that can rule out ongoing narratives.
“You can play with this to see how the response is doing. It’s a qualitative tool: there’s also a lot we can’t say with certainty. If the cases take a sharp right turn and stop going up, there are two possibilities: Control has been achieved or they are running out of resources. We can’t distinguish those with the graph alone.”
Fisman and Tuite will be working on the virus’ lethality in an upcoming publication.