Workshop Schedule


Instructors	Emails
Micaela E. Martinez (WE ACT for Environmental Justice)	micaela dot martinez at weact dot org
Matt Ferrari (Penn State University)	mjf283 at psu dot edu
TA: Callum Arnold (Penn State University)	cfa5228 at psu dot edu

General workshop times:

July 10, Monday: 8:30 a.m. - 5 p.m. PST

July 12, Tuesday: 8:30 a.m. - 5 p.m. PST

July 13, Wednesday: 8:30 a.m. - 12 p.m. PST

Important

All times are in local time PST

Before class

Please see pre-requisites on the course website

Day 01 – Monday


Time	Section
8:30 - 9:30am	Lecture 1: Intro to Modeling by Micaela Martinez
9:30 - 10am	Lecture 2: Basics of SIR Models by Matt Ferrari (first half)
10 - 10:30pm	coffee break
10:30am - 11am	Lecture 2: Basics of SIR Models by Matt Ferrari (second half)
11 - 12pm	`R` Session 1: Intro to modeling by Callum Arnold
12 - 1:30pm	lunch on your own
1:30pm - 2pm	`R` Session 1: Intro to modeling (continued) by Callum Arnold
2pm - 3pm	Lecture 3: Vaccination & Interventions by Micaela Martinez
3 - 4:15pm	Finish `R` exercises and work on group assignment for `R` Session 1.
4:15pm - 5pm	Group Assignment debrief for `R` Session 1

Day 02 – Tuesday


Time	Section
8:30am - 9:30am	Lecture 4: Heterogeneity in Models by Callum Arnold
9:30 - 10am	Lecture 5: Heterogeneity expanded (age structure and the force of infection) by Matt Ferrari (first half)
10 - 10:30am	Coffee Break
10:30am - 11am	Lecture 5: Heterogeneity expanded (age structure and the force of infection) by Matt Ferrari (second half)
11 - 12am	`R` Session 2: Heterogeneity and age structure by Callum Arnold
12 - 1:30pm	lunch on your own
1:30 - 2:30pm	Lecture 6: Parameter Estimation (estimating $R_0$) by Matt Ferrari
2:30 - 3:30pm	`R` Session 3: Parameter estimation by Callum Arnold
3:30pm - 4:15pm	Finish `R` exercises from the day and work on group assignment for `R` Sessions 2 and 3
4:15pm - 5pm	Group Assignment debrief for `R` Session 2

Day 03 – Wednesday


Time	Section
8:30am - 9:30am	Lecture 7: Confronting models with data by Micaela Martinez
9:30am - 10am	Lecture 8: Stochasticity and Uncertainty lecture by Micaela Martinez and Matt Ferrari (first half)
10am - 10:30am	Coffee break
10:30am - 11am	Lecture 8: Stochasticity and Uncertainty lecture by Micaela Martinez and Matt Ferrari (first half)
11am - 12am	Group Assignment debrief for `R` Session 3 and any other discussion points

Class Ends Noon on Wednesday

Group assignments

Each individual should create a slide deck to address each of the following questions for each of the day’s R exercises.
Each group should develop consensus slides to present when we come together as a class.
Groups will be asked to share their intuition gained.

Questions:

What were the top two take-away messages you gained from this R exercise?
What was the most unexpected/non-intuitive thing you learned from this R exercise?

--- author: "" --- # Workshop Schedule {.unnumbered} |Instructors | Emails | |:--|:--| | Micaela E. Martinez (WE ACT for Environmental Justice) | micaela *dot* martinez *at* weact *dot* org | | Matt Ferrari (Penn State University) | mjf283 *at* psu *dot* edu | | **TA**: Callum Arnold (Penn State University) | cfa5228 *at* psu *dot* edu | : {.striped .hover tbl-colwidths="[60,40]"} **General workshop times**: July 10, Monday: 8:30 a.m. - 5 p.m. PST July 12, Tuesday: 8:30 a.m. - 5 p.m. PST July 13, Wednesday: 8:30 a.m. - 12 p.m. PST ::: {.callout-important} All times are in local time PST ::: ## Before class Please see pre-requisites on the course website ## Day 01 – Monday | Time | Section | |:--|:--| | 8:30 - 9:30am | Lecture 1: Intro to Modeling by Micaela Martinez | | 9:30 - 10am | Lecture 2: Basics of SIR Models by Matt Ferrari (first half) | | 10 - 10:30pm | coffee break | | 10:30am - 11am | Lecture 2: Basics of SIR Models by Matt Ferrari (second half) | | 11 - 12pm | `R` Session 1: Intro to modeling by Callum Arnold | | 12 - 1:30pm | lunch on your own | | 1:30pm - 2pm | `R` Session 1: Intro to modeling (continued) by Callum Arnold | | 2pm - 3pm | Lecture 3: Vaccination & Interventions by Micaela Martinez | | 3 - 4:15pm | Finish `R` exercises and work on [group assignment](#group-assignments) for `R` Session 1. | | 4:15pm - 5pm | Group Assignment debrief for `R` Session 1 | : {.striped .hover tbl-colwidths="[25,75]"} ## Day 02 – Tuesday | Time | Section | |:--|:--| | 8:30am - 9:30am | Lecture 4: Heterogeneity in Models by Callum Arnold | | 9:30 - 10am | Lecture 5: Heterogeneity expanded (age structure and the force of infection) by Matt Ferrari (first half) | | 10 - 10:30am | Coffee Break | | 10:30am - 11am | Lecture 5: Heterogeneity expanded (age structure and the force of infection) by Matt Ferrari (second half) | | 11 - 12am | `R` Session 2: Heterogeneity and age structure by Callum Arnold | | 12 - 1:30pm | lunch on your own | | 1:30 - 2:30pm | Lecture 6: Parameter Estimation (estimating $R_0$) by Matt Ferrari | | 2:30 - 3:30pm | `R` Session 3: Parameter estimation by Callum Arnold | | 3:30pm - 4:15pm | Finish `R` exercises from the day and work on [group assignment](#group-assignments) for `R` Sessions 2 and 3 | | 4:15pm - 5pm | Group Assignment debrief for `R` Session 2 | : {.striped .hover tbl-colwidths="[25,75]"} ## Day 03 – Wednesday | Time | Section | |:--|:--| | 8:30am - 9:30am | Lecture 7: Confronting models with data by Micaela Martinez | | 9:30am - 10am | Lecture 8: Stochasticity and Uncertainty lecture by Micaela Martinez and Matt Ferrari (first half) | | 10am - 10:30am | Coffee break | | 10:30am - 11am | Lecture 8: Stochasticity and Uncertainty lecture by Micaela Martinez and Matt Ferrari (first half) | | 11am - 12am | Group Assignment debrief for `R` Session 3 and any other discussion points | : {.striped .hover tbl-colwidths="[25,75]"} **Class Ends Noon on Wednesday** ## Group assignments - Each individual should create a slide deck to address each of the following questions for each of the day’s `R` exercises. - Each group should develop consensus slides to present when we come together as a class. - Groups will be asked to share their intuition gained. **Questions**: 1. What were the top two take-away messages you gained from this `R` exercise? 2. What was the most unexpected/non-intuitive thing you learned from this `R` exercise?