Layers

I find this approach, described at IEEE Spectrum for modeling the spread of SARS-CoV-2, very appealing:

Current models that track epidemics make predictions about how a disease advances among individuals but don’t take into consideration things that change as it is passed along, such as how a virus mutated or what is being done to control its spread, Poor says. A common model being used for the coronavirus, known as SIR (susceptible, infected, and recovered), is a very basic one for viral spread. He says it doesn’t take into account factors such as underlying health conditions of patients or that some people spread more of COVID-19 droplets than others.

“Previous models for epidemics assumed a pathogen or a piece of information is transferred across network nodes without going through any modifications or evolutionary adaptions,” Poor and his colleagues wrote in that 2019 research article. “[But] in real-life spreading processes, pathogens often evolve in response to changing environments and medical interventions, and the information is often modified by individuals before being forwarded.”

Once the pandemic hit, Poor realized his model could be applied to COVID-19.

“Contact tracing and social distancing are known by epidemiologists to work so we’re not going to try to prove they do work,” Poor says. “What we’re going to bring to the table is a more refined understanding of when to apply these and other measures and what to expect of them.”

When people are physically connected, they form what’s called a multi-layered network, Poor says. The network could include family, coworkers, classmates, or social media friends. They, plus their acquaintances, form various degrees of connections, which the researchers call layers.

The researchers will first model these networks using data about the pandemic from a comprehensive data source, known as a data lake, maintained by C3.ai. The database includes the current number of COVID-19 cases, deaths, hospitalizations, and recoveries for countries, including by city, state, province, or county.

“It’s quite extensive, and includes details about COVID-19 victims and spread from a variety of sources globally,” Poor says.

The model can incorporate properties of the spread of the novel coronavirus that have been learned by public health and medical personnel over the past few months, Poor says. That includes the existence of “super spreaders,” asymptomatic spreaders, and delays between when a person becomes contagious and begins showing symptoms. Differences in responses to the virus between populations can also be incorporated into the model, he says, as well as some of the evidence for mutations that has emerged.

I hope they avoid the data from China since I question its veracity. I will look forward to the results of using this approach and don’t wish to prejudge them.