In an essay published by the Carnegie Mellon University College of Engineering, “To predict an epidemic, evolution can’t be ignored,” Daniel Tkacik (inadvertently) makes an excellent point about the importance of the intelligent design perspective for the study of pandemics.
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.
The researchers emphasized the role of evolution in understanding the spread of viruses:
“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.”
The Spread of Information
To model the impact of evolution on the spread of viruses, the researchers correlated viral spread to the spread of information in designed systems:
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.
The researchers noted the correlation between viral spread and the spread of information in intelligently designed networks. They attributed this to evolution, which is true, but the relevant evolution is in intelligently designed systems.
The Antithesis of Darwinian Evolution
The researchers conclude:
“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.
These researchers actually correlate viral evolution with evolution in designed systems, and they (bizarrely) imply that this demonstrates the importance of Darwinian evolution to medical research. Yet this research uses models that are the antithesis of unintelligent Darwinian natural selection. The reality to which these scientists are “one step closer” is the fact that ID research — research on the correlation between biological and designed systems — is indispensable to medicine and biology.
Nothing in biology makes sense except in light of design.