UA Professor: Perfect forecast not possible, but models, experts find a way
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UA Professor: Perfect forecast not possible, but models, experts find a way

The following is the opinion and analysis of the writer:

High-d efinition — hi-fi, Blu-ray Disc, HDTV and laptops, iPads and Chromebooks, Androids and iPhones — those dots-per-inch and pixels make sounds and pictures clear. We want high-definition weather, climate and economic forecasts. We want promise of where and when, in detail, it is safe to return to society from pandemic isolation. As of this writing, in less than four months, COVID-19 has claimed nearly 90,000 American lives.

Remember the scene in the first “Jurassic Park” movie, where Jeff Goldblum’s character is describing chaos and cites Ed Lorenz’s research on the flap of a butterfly’s wings in Brazil setting off a tornado in Texas? Lorenz, the MIT meteorologist, used that metaphor to depict chaos, the unfortunate promise when real world initial environmental conditions are not well known. Forecasts made in chaos rarely end well.

Lorenz verified what science had already established: skillful forecasts begin with reliable observations for model calculations. This is true for weather as well as for an ongoing pandemic. Which leads to my frustration over others’ frustration that the country’s lockdown over COVID-19 is too punishing.

I work to forecast risk of valley fever. When inhaled, fragments from the fungi C. posadasii and C. immitis are responsible for the disease, coccidioidomycosis, that experts say may afflict 100,000 people in the U.S. every year. The fungi live in unmapped patches across the American West, Mexico and Central and South America. Fungi fragments hitchhike in the wind, hidden among fine dust particles kicked up from dry exposed land.

NASA satellites map and monitor in detail dust sources, which they see, and which become initial conditions for a dust forecast model. But the fungus is invisible, and with few data on fungi sources it is anyone’s guess whether all, some, or none of the dust will contain the fungus. The same is true about COVID-19. Without widespread testing of where it is, model predictions and promises are unlikely to end well.

Today, hope for my research rests on a pilot study led by George Mason University. Small, inexpensive air samplers are being deployed to capture windblown dirt and fungi spores for analysis at Northern Arizona University, the Centers for Disease Control and Prevention, and George Mason. Through this project, a clearer picture of the fungi sources may emerge.

For health professionals, hope rests on public understanding and public demands for COVID-19 testing on a scale also defined by science. I knew Ed Lorenz, and these tests are the initial environmental conditions that he would want to know before conscientious people are sent, trusting, back into the coronavirus unknown.

According to the National Bureau of Labor Statistics, 7,410 epidemiologists and 18,270 microbiologists and virologists work in the United States today. Moms, dads, students, philanthropists and taxpayers very likely ponied up between $2 trillion and $5 trillion in college fees and tuition for these experts, the folks on whom we pin our hopes for a safe return to schools, stadiums, restaurants, friends and family. We depend on their scientific research to find cures and vaccines. When representatives of science, like Dr. Anthony Fauci of the National Institutes of Health, ask for more virus testing and more patience in social distancing, we should give it.

The cost of incomplete initial data, as Lorenz shows, is chaos. In the case of COVID-19, chaos could mean a viral resurgence that burrows into the community, more deaths, incapacitated workers, a deeper weakened economy, slower recovery and, since pandemics share wrong decisions with the rest of the world, less confidence in the decision maker.

William Sprigg is the principal for Science Policy Consultants, LLC, an Emeritus Research Professor at the University of Arizona and a former board director, National Academies of Science, Engineering and Medicine.

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