Assessing COVID-19 exposure with weather forecasting methods

Techniques used in weather forecasting can be repurposed to provide individuals with a personalised assessment of their risk of exposure to COVID-19. The technique has the potential to be more effective and less intrusive than blanket lockdowns, according to a recent study led by Tapio Schneider, Theodore Y. Wu Professor of Environmental Science and Engineering at Caltech, and co-authored, among others, by Lucas Böttcher, Assistant Professor of Computational Social Science at Frankfurt School. The paper appeared in PLOS Computational Biology on June 23.

The idea is simple: Weather forecasting models ingest a lot of data to assess what the current state of the atmosphere is, forecast the weather evolution into the future, and then repeat the cycle by blending the forecast atmospheric state with new data. In the same way, disease risk assessment also harnesses various types of available data to make an assessment about an individual's risk of exposure to or infection with disease. 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.

Its result would be a smart phone app similar to existing COVID-19 exposure notification apps but more sophisticated and effective in its use of data. Existing apps provide a binary exposure assessment (yes, you have been exposed, or, in the case of no exposure, radio silence). The new app would provide a more nuanced understanding of continually changing risks of exposure and infection.

FS professor Lucas Böttcher built a computer model of an imaginary city with 100,000 fictional people, and then studied how well the adapted weather-forecasting methods predicted the spread of a disease. The results were encouraging: 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.

However, the implementation of this technology requires suitable levels of smart-device users, and effective testing campaigns to make the risk-assessment software work for managing and controlling epidemics. If approximately 75 percent of the population provide relevant information and self-isolate when they may have been exposed, the risk-assessment software is accurate enough to manage and control the COVID epidemic. A promising scenario is deployment by smaller community user bases—for example a college campus.