living matter lab
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* 15 % homework 02 - early outbreak dynamics of covid-19 <br>
 
* 15 % homework 02 - early outbreak dynamics of covid-19 <br>
 
* 15 % homework 03 - outbreak control of covid-19 <br>
 
* 15 % homework 03 - outbreak control of covid-19 <br>
* 25 % project presentation - presentation graded by class <br>
+
* 25 % final project - presentation graded by class <br>
* 30 % project report - report graded by instructors
+
* 30 % final project - report graded by instructors
  
 
==previous class projects==
 
==previous class projects==

Revision as of 12:38, 20 August 2020

Corona.jpg

Contents

fall 21 - me233 - data-driven modeling of covid-19

ME233aa.jpg
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me 233 - data-driven modeling of covid-19

ellen kuhl kevin linka
amelie schafer oguz tikenogullari

announcement, syllabus

fall 2021
tue thu 8:30-9:50

 

me234 youtube channel

Brain youtube2017.jpg

dissecting brains

objectives

understanding the outbreak dynamics of COVID-19 through the lens of mathematical models is an elusive but significant goal. within only half a year, the COVID-19 pandemic has resulted in more than 20 million reported cases across 188 countries with more than 700,000 deaths worldwide. this has generated an unprecedented volume of data; yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. this course discusses how to design computational tools to understand the COVID-19 outbreak. we focus on mathematical epidemiology, infectious disease models, concepts of the effective reproduction number and herd immunity, network modeling, outbreak dynamics and outbreak control, bayesian methods, model calibration and validation, prediction and uncertainty quantification. we highlight the early success of classical models for infectious diseases and show why these models fail to predict the current outbreak dynamics of COVID-19. we illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters—in real time—from reported case data to make informed predictions and guide political decision making. We critically discuss questions that current models can and cannot answer and showcase controversial decisions around the early outbreak dynamics, outbreak control, and exit strategies.

grading

  • 15 % homework 01 - mathematical epidemiology of covid-19
  • 15 % homework 02 - early outbreak dynamics of covid-19
  • 15 % homework 03 - outbreak control of covid-19
  • 25 % final project - presentation graded by class
  • 30 % final project - report graded by instructors

previous class projects

harris tc, de rooij r, kuhl e. the shrinking brain: cerebral atrophy following traumatic brain injury. ann biomed eng. 2019; 47:1941-1959. (download)

weickenmeier j, kurt m, ozkaya e, wintermark m, butts pauly k, kuhl e. magnetic resonance elastography of the brain: a comparison between pigs and humans. j mech beh biomed mat. 2018; 77:702-710. (download)

wu lc, ye pp, kuo c, laksari k, camarillo d, kuhl e. pilot findings of brain displacements and deformations during roller coaster rides. j neurotrauma. 2017; 34:3198-3205. (download)

lejeune e, javili a, weickenmeier j, kuhl e, linder c. tri-layer wrinkling as a mechanism for anchoring center initiation in the developing cerebellum. soft matter. 2016;12:5613-5620. (download)

ploch cc, mansi cssa, jayamohan j, kuhl e. using 3D printing to create personalized brain models for neurosurgical training and preoperative planning. world neurosurg. 2016;90:668-674. (download), (perspectives)

syllabus

day date topic slides homework
mon jan 06 introduction to brain anatomy s01
wed jan 08 introduction to brain mechanics s02
fri jan 10 dissecting brains - uytengsu 130/132 s03
mon jan 13 brain anatomy - student presentations s03
wed jan 15 brain anatomy - student presentations s04
wed jan 22 brain mechanics in 1d – elasticity of neurons s05
fri jan 24 brain mechanics in 3d – elasticity of the brain s06
mon jan 27 brain mechanics in 3d - probing the living brain s07
wed jan 29 brain growth in 1d – axonal growth s08
mon feb 03 brain growth in 2d – morphogenesis s09
wed feb 05 brain growth in 3d - physiology and pathologies s10
mon feb 10 brain damage in 1d – diffuse axonal injury s11
wed feb 12 brain damage in 3d – traumatic brain injury s12
wed feb 19 brain damage in 3d – neurodegeneration s13
fri feb 21 brain damage in 3d - brain atrophy s14
mon feb 24 brain surgery - brain doctors at john radcliffe s15
wed feb 26 brain surgery - craniosynostosis s16
mon mar 02 brain surgery – decompressive craniectomy s17
wed mar 04 brain regeneration - spinal cord injury s18
mon mar 09 final projects - discussion, presentation, evaluation s19
wed mar 11 final projects - discussion, presentation, evaluation s20
fri mar 13 final project reports due

matlab files

here's the matlab code for brain folding

additional reading

bayly pv, taber la, kroenke cd. mechanical forces in cerebral cortical folding: a review of measurements and models. j mech beh biomed mat. 2014;29:568-581. (download)

budday s, steinmann p, kuhl e. the role of mechanics during brain development. j mech phys solids. 2014:72:75-92. (download)

budday s, nay r, steinmann p, wyrobek t, ovaert tc, kuhl e. mechanical properties of gray and white matter brain tissue by indentation. j mech behavior biomed mat. 2015;46:318-330. (download)

budday s, steinmann p, kuhl e. physical biology of human brain development. front cell neurosci. 2015;9:257. (download)

budday s, sommer g, birkl c, langkammer c, hayback j, kohnert j, bauer m, paulsen f, steinmann p, kuhl e, holzapfel ga. mechanical characterization of human brain tissue. acta biomat. 2017;48:319-340. (download)

dennerll tj, lamoureux p, buxbaum re, heidemann sr. the cytomechanics of axonal elongation and retraction. j cell bio. 1989;109:3073-3083. (download)

franceschini g, bigoni d, regitnig p, holzapfel ga. brain tissue deforms similar to filled elastomers and follows consolidation theory. j mech phys solids. 2006;54:2592-2620. (download)

goriely a, geers mgd, holzapfel ga, jayamohan j, jerusalem a, sivaloganathan s, squier w, van dommelen jaw, waters s, kuhl e. mechanics of the brain: perspectives, challenges, and opportunities. biomech mod mechanobio. 2015;14:931-965. (download)

hardan ay, libove ra, keshavan ms, melhem nm, minshew nj. a preliminary longitudinal magnetic resonance imaging study of brain volume and cortical thickness in autism. biol psych. 2009;66:320-326. (download)

kruse sa, rose gh, glaser kj, manduca a, felmlee jp, jack cr, ehman rl. magentic resonance elastography of the brain. neuroimage. 2008;39:231-237. (download)

miller k, chinzei k. constitutive modelling of brain tissue: experiment and theory. j biomech. 1997;30:1115-1121. (download)

raybaud c, widjaja e. development and dysgenesis of the cerebral cortex: malformations of cortical development. neuroimag clin n am. 2011;21:483–543. (download)

richman dp, stewart rm, hutchinson jw, caviness vs. mechanical model of brain convolutional development. science. 1975;189:18-21. (download)

sun t, hevner rf. growth and folding of the mammalian cerebral cortex: from molecules to malformations. nature neurosci. 2014;15:217-231. (download)

van essen dc. a tension-based theory of morphogenesis and compact wiring in the central nervous system. nature. 1997;385:313-318. (download)

weickenmeier j, kuhl e, goriely a. the multiphysics of prion-like disease: progression and atrophy. phys rev lett. 2018;121:158101. (download)