- Supervisor
- Alexander Viguerie - Emory University and GSSI
- Date and time
- Wednesday, October 28, 2020 at 4:15 PM - 12 hours - online - live streaming
- Contact person
- Giandomenico Orlandi
- Publication date
- September 22, 2020
- Department
- Computer Science

The outbreak of COVID-19 in 2020 has led to a surge in interest of the mathematical modeling of

epidemics. Many of the introduced models are so-called compartmental models, in which the total quan-

tities characterizing a certain system may be decomposed into two (or more) species distributed

into two (or more) homogeneous units called compartments. This short course will introduce the notion

of a compartment model and the basics of their development, beginning with the standard SIR

(susceptible-infected-recovered) model and gradually introducing more realistic models that account for

factors such as age-structured populations, asymptomatic patients, and interventions such as lockdowns,

mandatory mask-wearing etc. The course will also address how one may incorporate spatial variation

via a partial differential equation (PDE) model or additional compartments in an ordinary differential

equation (ODE) model. Some sample python code will be provided for numerical examples. The course is

open to all students; however previous exposure to differential equations and basic programming concepts

is recommended.

(Zoom, link in the e-learning course platform)

Lesson 2: Compartment modelling in general: derivation of a predator-prey type model and differences/similarities with SIR. Using graphs for intuition, discussion of various types of compartment models. More sophisticated SIR-type models and R0 generalizations with next-generation matrices.

Lesson 3: Incorporating spatial information. ODE spatial incorporation via spatial-compartment structure. Introduction of PDEs and diffusion. Parallels with computational mechanics and interpretation in terms of constitutive relation and balance equations. Brief discussion of data fitting/machine learning techniques

Lesson 4: Interactive session: extension of basic SIR code. Students will be provided the base code in python and asked to extend the model via a provided flow-chart. Then they will answer several questions based on the new model

contact e-mail: giandomenico.orlandi@univr.it

1. 28/10 16.15 - 17.45

2. 29/10 09.30 - 11.45

3. 04/11 16.15 - 17.45

4. 05/11 09/30 - 11.45

5. Project assignments

Lesson 2: Compartment modelling in general: derivation of a predator-prey type model and differences/similarities with SIR. Using graphs for intuition, discussion of various types of compartment models. More sophisticated SIR-type models and R0 generalizations with next-generation matrices.

Lesson 3: Incorporating spatial information. ODE spatial incorporation via spatial-compartment structure. Introduction of PDEs and diffusion. Parallels with computational mechanics and interpretation in terms of constitutive relation and balance equations. Brief discussion of data fitting/machine learning techniques

Lesson 4: Interactive session: extension of basic SIR code. Students will be provided the base code in python and asked to extend the model via a provided flow-chart. Then they will answer several questions based on the new model

contact e-mail: giandomenico.orlandi@univr.it

Title | Format (Language, Size, Publication date) |
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poster - locandina corso | pdf (it, 511 KB, 22/09/20) |

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