Methods from Weather Forecasting Can Be Adapted to Assess COVID Risk
A more granular understanding of risk could reduce the need for widespread lockdowns during an epidemic
Techniques used in weather forecasting can be repurposed to provide individuals with a personalized assessment of their risk of exposure to COVID-19 or other viruses. The technique has the potential to be more effective and less intrusive than blanket lockdowns for combatting the spread of disease.
The study by scientists at Caltech and partners, including Columbia University Mailman School of Public Health, is published in PLOS Computational Biology.
“For this pandemic, it may be too late. But this is not going to be the last epidemic that we will face. This is useful for tracking other infectious diseases too,” says lead author Tapio Schneider, the Theodore Y. Wu Professor of Environmental Science and Engineering and senior research scientist at JPL, which Caltech manages for NASA.
The research presented in PLOS Computational Biology is proof of concept. However, its end result would be a smartphone app that would provide an individual with a frequently updated numerical assessment (i.e., a percentage) that reflects their likelihood of having been exposed to or infected with a particular infectious disease agent, such as COVID-19.
The app might harness information from sensors, infection data, and proximity tracking that people could use to adjust their behavior to mitigate individual risks. The app would be similar to existing COVID-19 exposure notification apps but more sophisticated and effective in its use of data. Those apps only tell you if/when you've been exposed; the new app would provide a more nuanced understanding of continually changing risks as individuals come close to others and as data about infections is propagated across a continually evolving contact network.
Similar to techniques used in weather forecasting, disease risk assessment harnesses various types of available data to make an assessment about an individual's risk of exposure to or infection with disease, forecasts the spread of disease across a network of human contacts using an epidemiological model, and then repeats the cycle by blending the forecast with new data. Such assessments might use the results of an institution’s surveillance testing, data from wearable sensors, self-reported symptoms and close contacts as recorded by smartphones, and municipalities' disease-reporting dashboards.
“Over the last decade, the field of infectious-disease modeling, and forecasting, in particular, has exploded. Many disease-forecasting approaches leverage ensemble and inference methods commonly used in weather prediction,” says study co-author Jeffrey Shaman, professor of environmental health sciences at Columbia University Mailman School of Public Health and a leading researcher on infectious disease forecasting.
To test the new technique, researchers built a computer model of an imaginary city—a downscaled and idealized version of New York City—with 100,000 “nodes,” or fictional people, and then studied how well the adapted weather-forecasting methods predicted the spread of a disease through the population. The results were encouraging: in the simulations, the model identified up to twice as many potential exposures than would be caught by traditional contact tracing or exposure-notification apps when both use the same data.
Despite these promising results, the implementation of this technology in the real world requires suitable levels of smart-device users, and effective testing campaigns to make the risk-assessment software work for managing and controlling epidemics. Nevertheless, a promising scenario is a deployment by smaller community user bases—for example, the population of a college campus— that can readily provide the software with more than enough data to provide accurate risk assessments that will locally reduce the spread of disease.
“The challenge in making this system a reality is managing privacy concerns, for example, about transferring data about close contacts to a central data-processing facility,” Schneider says. “That being said, only anonymized information is needed. Location information is already routinely collected for commercial use, and we envision ways to harden the system against exploitation by bad actors.”
A complete list of co-authors is available on a journal article page. This research was supported by Eric and Wendy Schmidt and Schmidt Futures; the Swiss National Science Foundation; the National Institutes of Health; the Army Research Office; the National Science Foundation; the National Institute of Allergy and Infectious Diseases; and the Morris-Singer Foundation.
(This article is adapted from a longer article published by CalTech.)