[presentation] polish

.gitignore

@@ -9,3 +9,6 @@ compile_commands.json
 *.html
 *.ipynb
 .nocheck/
+
+*_files
+!README_files

interactives/Presentation.qmd

@@ -1,16 +1,16 @@
 ---
 title: "Frameworks for Epidemiologic Agent-Based Modeling"
-subtitle: "Epiworld and it's various wrappers"
+subtitle: "Epiworld and its various wrappers"
 format:
-  revealjs: 
+  revealjs:
     slide-number: true
-    footer: "??? - <https://milobanks.com/whatever-goes-here>"
+    footer: "Epiworld and its various wrappers - <https://milobanks.com/slides/2024-10-14>"
     code-annotations: below
-    chalkboard: 
+    chalkboard:
       buttons: false
     preview-links: auto
     incremental: true
-    include-in-header: 
+    include-in-header:
       text: |
         <style>
         .v-center-container {
@@ -24,7 +24,7 @@ format:
 # author: # Unsure
 institute:
   - The University of Utah
-date: 2024-09-03
+date: 2024-10-14
 aspectratio: 169
 fig-format: svg
 ---
@@ -75,11 +75,21 @@ fig-format: svg
 
 - **EpiworldR**: R wrapper that makes it easy to run simulations in the R environment, favored by data scientistss.
 - **EpiworldPy *(In Progress)***: Python wrapper, enabling the use of Epiworld in Python, which is also favored in data science and machine learning spheres.
-- **EpiworldShiny**: A non-programatic of interacting with Epiworld.
+- **EpiworldRShiny**: A non-programatic of interacting with Epiworld.
 - **EpiworldWeb *(In Progress)*** : A port of Epiworld to WebAssembly, allowing serverless simulations.
 
 ---
 
+## A Look at EpiworldRShiny
+
+::: {.v-center-container}
+
+![](fig/epiworldrshiny.png){width="100%" fig-align="center"}
+
+:::
+
+---
+
 ## Preview of EpiworldWeb
 
 ::: {.v-center-container}
@@ -93,9 +103,7 @@ fig-format: svg
 ## Comparison of Wrappers
 
 - What does **EpiworldPy** exist over **EpiworldR**?
-- **EpiworldShiny** must be run locally on a computer despite being packaged as a website, whereas **EpiworldWeb** needs no prerequisites.
-
----
+- **EpiworldRShiny** must be run locally on a computer despite being packaged as a website, whereas **EpiworldWeb** needs no prerequisites.
 
 # Example {background-color="black"}
 
@@ -107,7 +115,7 @@ fig-format: svg
 
 ```c++
 /* Creating the model. */ // <1>
-epimodels::ModelSEIRCONN<> model( 
+epimodels::ModelSEIRCONN<> model(
   "Influenza", /* Name of the virus. */
   50000,       /* Population. */
   0.01,        /* Initial prevalence. */
@@ -205,7 +213,7 @@ model.print()
 
 ---
 
-## Demo: Influenza Outbreak in SLC (EpiworldShiny)
+<!-- ## Demo: Influenza Outbreak in SLC (EpiworldRShiny)
 
 ::: {.v-center-container}
 
@@ -223,7 +231,7 @@ model.print()
 
 :::
 
----
+--- -->
 
 ## Demo: Graphing Model Compartments
 
@@ -304,14 +312,14 @@ plt.figure(figsize=(10, 6))
 
 for virus_id, virus_data in enumerate(reproductive_data):
     average_rts = list()
-    
+
     for date_data in virus_data:
         if not date_data:
             continue
 
         keys_array = np.array(list(date_data.values()), dtype=np.float64)
         average_rts.append(np.mean(keys_array))
-        
+
     plt.plot(range(0, len(average_rts)), average_rts, label=f"Virus {virus_id}")
 
 plt.xlabel('Date')
@@ -427,10 +435,10 @@ fig, ax = plt.subplots(figsize=(6,4))
 to_track = { source[0] }
 def update(frame):
     ax.clear()
-    
+
     agents_involved_today = set()
     agents_relationships_we_care_about = []
-    
+
     # Get only the agents involved in the current frame.
     for i in range(len(start)):
         if start[i] <= frame <= end[i]:
@@ -545,7 +553,7 @@ vaccine = epiworld.Tool(
     as_proportion            = True,
     susceptibility_reduction = 0.9,
     transmission_reduction   = 0.5,
-    recovery_enhancer        = 0.5, 
+    recovery_enhancer        = 0.5,
     death_reduction          = 0.9
 )
 
@@ -602,14 +610,14 @@ plt.figure(figsize=(10, 6))
 
 for virus_id, virus_data in enumerate(reproductive_data):
     average_rts = list()
-    
+
     for date_data in virus_data:
         if not date_data:
             continue
 
         keys_array = np.array(list(date_data.values()), dtype=np.float64)
         average_rts.append(np.mean(keys_array))
-        
+
     plt.plot(range(0, len(average_rts)), average_rts, label=f"Virus {virus_id}")
 
 plt.xlabel('Date')
@@ -620,16 +628,25 @@ plt.grid(True)
 plt.show()
 ```
 
----
-
 # Closing Thoughts {background-color="black"}
 
 ---
 
 ## Closing Thoughts
 
-- Self, what do I say here so it's not a knockoff of `ccs2024`?
+- `epiworld` is a high-performance C++ library designed for building agent-based epidemiological models.
+- A flexible framework, allowing callers to define custom states and update dynamics for their models.
+- It has as been utilized in numerous epidemiological agent-based modeling research projects and studies.
+- Check it out at [github.com/UofUEpiBio/epiworld](https://github.com/UofUEpiBio/epiworld)!
 
 ---
 
+## Closing Thoughts
+
+::: {.v-center-container}
+
+We are looking for **collaborators** and **contributors**!
+
+:::
+
 # Questions {background-color="black"}