By Nileena Velappan
In December of 2021, biomedical scientist Alina Deshpande had the idea of searching in historical disease records for potential “black swan” anomalous disease outbreaks. Pulling our research team at Los Alamos National Laboratory together, she hypothesized that there might be common features among exceptionally large outbreaks that, if identified, could serve as warning signs of future pandemics.
We know that if a newly mutated coronavirus germ, or pathogen, infects a person who already has antibodies that can recognize and neutralize it, the pathogen might stop spreading. Or if a new pathogen infects a person who is isolated and dies before they interact with another person, well then, the disease stops there. Pathogen fitness, host susceptibility and environmental conditions are three major considerations to disease spread.
Seeking to understand if each of these factors is equally important, we tackled this question using a visual analytics tool Deshpande had developed in 2012 called AIDO (Analytics for Investigation of Disease Outbreaks). We designed AIDO to help researchers understand and respond to new disease outbreaks by comparing them to historical ones.
The tool includes a database of detailed information about more than 600 outbreaks of 40 distinct diseases—measles, cholera, Ebola and more. We narrowed the black swan collection to a subset of 32 potential diseases in the database and we excluded SARS-CoV-2 data, the virus that causes COVID-19, so that we could better understand any clues from the past that might have foretold the current pandemic.
Read the rest of the story as it appeared in STAT.