Update data documentation

Author: actions-user

docs/index.html

@@ -74,6 +74,9 @@ <h6 class="sidebar-heading px-3 mt-1 mb-1 text-muted">Data Sources</h6>
               <li class="nav-item">
                 <a class="nav-link" href="#medicaid-quality">Medicaid Quality</a>
               </li>
+              <li class="nav-item">
+                <a class="nav-link" href="#mmr-epic">MMR Epic</a>
+              </li>
               <li class="nav-item">
                 <a class="nav-link" href="#mmr-healthmap">MMR Healthmap</a>
               </li>
@@ -4347,6 +4350,10 @@ <h5 class="mt-3">
               <h2 class="border-bottom pb-2">Medicaid Quality</h2>
               <p class="text-muted">No standard data files found.</p>
             </section>
+            <section id="mmr-epic" class="mb-5">
+              <h2 class="border-bottom pb-2">MMR Epic</h2>
+              <p class="text-muted">No standard data files found.</p>
+            </section>
             <section id="mmr-healthmap" class="mb-5">
               <h2 class="border-bottom pb-2">MMR Healthmap</h2>
               <p class="lead">County, ZIP code, and state-level estimates of MMR (measles, mumps, and rubella) vaccine coverage among US children, developed using small area estimation with multilevel regression and post-stratification (MRP). The methodology integrates participatory surveillance data from digital health platforms with demographic and contextual covariates to produce granular geographic estimates of vaccination coverage gaps. Estimates include posterior mean coverage percentages, risk classifications for under-vaccination, and spatial autocorrelation measures (Local Moran's I) to identify geographic clustering of under-vaccinated areas. This research was conducted in response to the 2025 measles outbreak to support targeted public health interventions.</p>