Update

Author: PhilChodrow

_quarto-preview.yml

@@ -25,6 +25,7 @@ book:
       chapters:
         - source/50-random-walks.qmd
         - source/51-agent-based-modeling.qmd
+        - source/53-epidemiology-computational.qmd
         - source/52-epidemiology.qmd
   appendices:
     - source/92-probability.qmd

_quarto-publish.yml

@@ -25,6 +25,7 @@ book:
       chapters:
         - chapters/50-random-walks.qmd
         - chapters/51-agent-based-modeling.qmd
+        - chapters/53-epidemiology-computational.qmd
         - chapters/52-epidemiology.qmd
     - part: "Appendices"
       appendices: 

docs/chapters/01-networkrepresentations.html

@@ -318,8 +318,14 @@
 </li>
 <li class="sidebar-item">
 <div class="sidebar-item-container">
+<a class="sidebar-item-text sidebar-link" href="../chapters/53-epidemiology-computational.html">
+<span class="menu-text"><span class="chapter-number">17</span>  <span class="chapter-title">Models of Disease-Spread</span></span></a>
+</div>
+</li>
+<li class="sidebar-item">
+<div class="sidebar-item-container">
 <a class="sidebar-item-text sidebar-link" href="../chapters/52-epidemiology.html">
-<span class="menu-text"><span class="chapter-number">17</span>  <span class="chapter-title">Epidemic Models on Networks</span></span></a>
+<span class="menu-text"><span class="chapter-number">18</span>  <span class="chapter-title">Epidemic Models on Networks</span></span></a>
 </div>
 </li>
 </ul>
@@ -398,7 +404,7 @@ <h3 class="anchored" data-anchor-id="networks-as-graphs">Networks as Graphs</h3>
 </div>
 </div>
 <div class="page-columns page-full"><p>Here’s an example of a famous graph: the Zachary Karate Club. Each node represents a member of a university karate club, and each edge represents a friendly social relationship between them. This graph is supplied by <code>networkx</code>, the Python software package that we’ll use throughout these notes for network analysis. </p><div class="no-row-height column-margin column-container"><span class="margin-aside">The code in this collapsed cell imports packages and sets up various plotting options</span></div></div>
-<div class="cell" data-execution_count="1" id="854434be">
+<div class="cell" data-execution_count="1" id="94512ce7">
 <details class="code-fold">
 <summary>Show code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a aria-hidden="true" href="#cb1-1" tabindex="-1"></a><span class="im">from</span> matplotlib <span class="im">import</span> pyplot <span class="im">as</span> plt</span>
@@ -698,7 +704,7 @@ <h4 class="anchored" data-anchor-id="creating-a-graph-dictionary">Creating a gra
 </div>
 </div></div></div>
 <p>It’s also possible to “go backwards” and obtain the dictionary representation from the graph object using</p>
-<div class="cell" data-execution_count="8" id="c782c7ed">
+<div class="cell" data-execution_count="8" id="0ae5e1b9">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a aria-hidden="true" href="#cb9-1" tabindex="-1"></a>nx.to_dict_of_lists(G)</span></code><button class="code-copy-button" title="Copy to Clipboard"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
 <pre><code>{1: [2], 2: [1, 3, 4], 3: [2, 4], 4: [2, 3]}</code></pre>