With the death toll in the novel coronavirus touching 637 in China, a new study by the University of Toronto has said that the epidemic had started earlier and that the disease transmission has not yet been controlled in the country.
“You can’t get up to that level of cases if the epidemic started in December even if you pushed the reproduction really high,” said DLSPH Professor David Fisman, one of the disease-transmission model’s co-creator, available on the Annals of Medicine with the report.
Reproduction in epidemiology essentially means the number of secondary cases a single case can infect in a susceptible population. “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,” he said.
“It had to be earlier which raises some interesting questions about how this emerged. The plausible start date seems to be mid-November,” Fisman said.
The Chinese government implemented measures to contain the virus’ spread mid-January, however, the graph pointed out that the disease’s reproduction number had 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,” Professors David Fisman and Ashleigh Tuite said.
Reproduction numbers have not been less than one in the last week since the World Health Organization 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 every day. Fisman said 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 said it is a simplified version of the 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.
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