ArticleHeliyon2024
Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Universal differential equations for quantifying NF-κB-p53 signaling crosstalk.Bioinformatics (Oxford, England) · 2026Article
- A Hybrid Machine Learning Framework for Interpretable Kinetics of α-Tocopherol and Myricetin Synergism.Journal of food science · 2026Article
- Current state and open problems in universal differential equations for systems biology.NPJ systems biology and applications · 2025Article
- Physiology-informed regularisation enables training of universal differential equation systems for biological applications.PLoS computational biology · 2025Article
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Authors and funding
2 authors.
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Abstract
During the COVID-19 pandemic, it became clear that pandemic waves and population responses were locked in a mutual feedback loop in a classic example of a coupled behavior-disease system. We demonstrate for the first time that universal differential equation (UDE) models are able to extract this interplay from data. We develop a UDE model for COVID-19 and test its ability to make predictions of second pandemic waves. We find that UDEs are capable of learning coupled behavior-disease dynamics and predicting second waves in a variety of populations, provided they are supplied with learning biases describing simple assumptions about disease transmission and population response. Though not yet suitable for deployment as a policy-guiding tool, our results demonstrate potential benefits, drawbacks, and useful techniques when applying universal differential equations to coupled systems.
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