data-driven modeling of covid-19

new book on data-driven modeling of covid-19

If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It introduces more than 400 examples, figures, and problems to teache cutting edge tools to model and simulate nonlinear dynamic systems in view of a global pandemic motivated by the curiosity to understand it.

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new course on data-driven modeling of covid-19

This new course explores how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters—in real time—from reported case data and guide political decision making. We critically discuss questions that current models can and cannot answer and highlight controversies around early outbreak dynamics, outbreak control, and exit strategies.

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Linka K, Schafer A, Meng X, Zou Z, Karniadakis GE, Kuhl E. Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems. Comp Meth Appl Mech Eng. 2022; doi:10.1016/j.cma.2022.115346. (download) (cmame)

Linka K, Peirlinck M, Schafer A, Ziya Tikenogullari O, Goriely A, Kuhl E. Effects of B.1.1.7 and B.1.351 on COVID-19 dynamics. A campus reopening study. medRxiv. doi:10.1101/2021.04.22.2125595 (medRxiv)

Lu H, Weintz C, Pace J, Indana D, Linka K, Kuhl E. Are college campuses superspreaders? A data-driven modeling study. Comp Meth Biomech Biomed Eng. 2021; doi:10.1080/10255842.2020.1869221. (download) (cmbbe) (medRxiv) (T&F press release)(scitechdaily)(crumpi)

Linka K, Goriely A, Kuhl E. Global and local mobility as a barometer for COVID-19 dynamics. Biomech Model Mechanobio. 2021; 651-669. (open access) (download)

Bhouri MA, Sahli Costabal F, Wang H, Linka K, Peirlinck M, Kuhl E, Perdikaris P. COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior. Comp Meth Appl Mech Eng. 2021; 382:113891. (download)

Linka K, Peirlinck M, Kuhl E. The reproduction number of COVID-19 and its correlation with public heath interventions. Comp Mech. 2020; 66:1035-1050. (download) (computational mechanics) (medRxiv) (medical life sciences) (the conversation)

Peirlinck M, Linka K, Sahli Costabal F, Bendavid E, Bhattacharya J, Ioannidis J, Kuhl E. Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19. Comp Meth Appl Mech Eng. 2020; 372:113410. (download) (cmame) (medRxiv)

Kuhl E. Data-driven modeling of COVID-19 - Lessons learned. Extr Mech Lett; 2020; 40:100921. (download) (youtube webinar) (EML webinar announcement) (link)

Linka K, Rahman P, Goriely A, Kuhl E. Is it safe to lift COVID-19 travel restrictions? The Newfoundland story. Comp Mech; 2020; 66:1081–1092. (medRxiv) (download) (medical life sciences) (stanford report)

Peirlinck M, Linka K, Sahli Costabal F, Kuhl E. Outbreak dynamics of COVID-19 in China and the United States. Biomech Model Mechanobio; 2020; 19:2179-2193. (medRxiv) (open access) (download) (stanford medicine)

Linka K, Peirlinck M, Sahli Costabal F, Kuhl E. Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions. Comp Meth Biomech Biomed Eng; 2020; 23:710-717. (medRxiv) (download) (science daily) (infosurhoy) (health24) (welt) (mdr wissen)