ArticleScientific reports2020
Algorithmic discovery of dynamic models from infectious disease data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Modeling Moose-Wolf interactions in Isle Royale National Park using sparseidentification of nonlinear dynamics.Scientific reports · 2025Article
- Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations.Heliyon · 2024Article
- The Experimentalist's Guide to Machine Learning for Small Molecule Design.ACS applied bio materials · 2024Review
- A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Application to COVID-19.Viruses · 2022Article
- Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics.PLoS computational biology · 2022Article
- Modelling coupled human-environment complexity for the future of the biosphere: strengths, gaps and promising directions.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Discovering dynamic models of COVID-19 transmission.Transboundary and emerging diseases · 2022Article
- Revisiting the standard for modeling the spread of infectious diseases.Scientific reports · 2022Article
- Article
- Sparsifying priors for Bayesian uncertainty quantification in model discovery.Royal Society open science · 2022Article
- SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis.Nonlinear dynamics · 2022Article
- Ziegler and Nichols meet Kermack and McKendrick: Parsimony in dynamic models for epidemiology.Computers & chemical engineering · 2022Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Theoretical models are typically developed through a deductive process where a researcher formulates a system of dynamic equations from hypothesized mechanisms. Recent advances in algorithmic methods can discover dynamic models inductively-directly from data. Most previous research has tested these methods by rediscovering models from synthetic data generated by the already known model. Here we apply Sparse Identification of Nonlinear Dynamics (SINDy) to discover mechanistic equations for disease dynamics from case notification data for measles, chickenpox, and rubella. The discovered models provide a good qualitative fit to the observed dynamics for all three diseases, However, the SINDy chickenpox model appears to overfit the empirical data, and recovering qualitatively correct rubella dynamics requires using power spectral density in the goodness-of-fit criterion. When SINDy uses a library of second-order functions, the discovered models tend to include mass action incidence and a seasonally varying transmission rate-a common feature of existing epidemiological models for childhood infectious diseases. We also find that the SINDy measles model is capable of out-of-sample prediction of a dynamical regime shift in measles case notification data. These results demonstrate the potential for algorithmic model discovery to enrich scientific understanding by providing a complementary approach to developing theoretical models.
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