Median incubation period for COVID-19
March 17, 2020
Science Daily/University of Massachusetts Amherst
A new study calculates that the median incubation period for COVID-19 is just over 5 days and that 97.5% of people who develop symptoms will do so within 11.5 days of infection.
A University of Massachusetts Amherst biostatistician who directs the UMass-based Flu Forecasting Center of Excellence was invited by the White House Coronavirus Task Force to participate Wednesday morning in a coronavirus modeling webinar.
The four-hour, virtual gathering will include 20 of the world's leading infectious disease and pandemic forecasting modelers, from researchers at Harvard, Johns Hopkins and the Centers for Disease Control and Prevention (CDC) in the U.S. to those based at institutions in England, Hong Kong, South Africa and the Netherlands.
According to the White House Coronavirus Task Force coordinator Dr. Charles Vitek, "This webinar is designed to highlight for the Task Force what modeling can tell us regarding the potential effects of mitigation measures on the coronavirus outbreak. The unprecedented speed and impact of the nCoV-19 epidemic requires the best-informed public health decision-making we can produce."
Nicholas Reich, associate professor in the School of Public Health and Health Sciences, heads a flu forecasting collaborative that has produced some of the world's most accurate models in recent years. He and postdoctoral researcher Thomas McAndrew have been conducting weekly surveys of more than 20 infectious disease modeling researchers to assess their collective expert opinion on the trajectory of the COVID-19 outbreak in the U.S. The researchers and modeling experts design, build and interpret models to explain and understand infectious disease dynamics and the associated policy implications in human populations.
Reich is co-author of a new study in the Annals of Internal Medicine that calculates that the median incubation period for COVID-19 is just over five days and that 97.5 percent of people who develop symptoms will do so within 11.5 days of infection. The incubation period refers to the time between exposure to the virus and the appearance of the first symptoms.
The study's lead author is UMass Amherst biostatistics doctoral alumnus Stephen Lauer, a former member of the Reich Lab and current postdoctoral researcher at the Johns Hopkins Bloomberg School of Public Health.
The researchers examined 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. They conclude that "the current period of active monitoring recommended by the U.S. Centers for Disease Control and Prevention [14 days] is well supported by the evidence."
https://www.sciencedaily.com/releases/2020/03/200317175438.htm
To predict an epidemic, evolution can't be ignored
March 2, 2020
Science Daily/College of Engineering, Carnegie Mellon University
Whether it's coronavirus or misinformation, scientists can use mathematical models to predict how something will spread across populations. But what happens if a pathogen mutates, or information becomes modified, changing the speed at which it spreads? Researchers now show for the first time how important these considerations are.
When scientists try to predict the spread of something across populations -- anything from a coronavirus to misinformation -- they use complex mathematical models to do so. Typically, they'll study the first few steps in which the subject spreads, and use that rate to project how far and wide the spread will go.
But what happens if a pathogen mutates, or information becomes modified, changing the speed at which it spreads? In a new study appearing in this week's issue of Proceedings of the National Academy of Sciences (PNAS), a team of Carnegie Mellon University researchers show for the first time how important these considerations are.
"These evolutionary changes have a huge impact," says CyLab faculty member Osman Yagan, an associate research professor in Electrical and Computer Engineering (ECE) and corresponding author of the study. "If you don't consider the potential changes over time, you will be wrong in predicting the number of people that will get sick or the number of people who are exposed to a piece of information."
Most people are familiar with epidemics of disease, but information itself -- nowadays traveling at lightning speeds over social media -- can experience its own kind of epidemic and "go viral." Whether a piece of information goes viral or not can depend on how the original message is tweaked.
"Some pieces of misinformation are intentional, but some may develop organically when many people sequentially make small changes like a game of 'telephone,'" says Yagan. "A seemingly boring piece of information can evolve into a viral Tweet, and we need to be able to predict how these things spread."
In their study, the researchers developed a mathematical theory that takes these evolutionary changes into consideration. They then tested their theory against thousands of computer-simulated epidemics in real-world networks, such as Twitter for the spread of information or a hospital for the spread of disease.
In the context of spreading of infectious disease, the team ran thousands of simulations using data from two real-world networks: a contact network among students, teachers, and staff at a US high school, and a contact network among staff and patients in a hospital in Lyon, France.
These simulations served as a test bed: the theory that matches what is observed in the simulations would prove to be the more accurate one.
"We showed that our theory works over real-world networks," says the study's first author, Rashad Eletreby, who was a Carnegie Mellon Ph.D. student when he wrote the paper. "Traditional models that don't consider evolutionary adaptations fail at predicting the probability of the emergence of an epidemic."
While the study isn't a silver bullet for predicting the spread of today's coronavirus or the spread of fake news in today's volatile political environment with 100% accuracy -- one would need real-time data tracking the evolution of the pathogen or information to do that -- the authors say it's a big step.
"We're one step closer to reality," says Eletreby.
https://www.sciencedaily.com/releases/2020/03/200302153551.htm