diff --git a/dashboard.Rmd b/dashboard.Rmd
index 8d1463d..cd04428 100644
--- a/dashboard.Rmd
+++ b/dashboard.Rmd
@@ -167,7 +167,7 @@ fluidRow(
       '
       <h5>Select a Storm</h5>
       
-      We are currently tracking 201 CONUS storms with over $3.6T in losses spanning from 1900 to 2024
+      We are currently tracking 201 CONUS storms with over $3T in losses spanning from 1900 to 2024
       
       <hr>
       
@@ -1000,14 +1000,6 @@ output$great_miami_dt <- renderDT({
 DTOutput("great_miami_dt")
 ```
 
-Storm Fatalities {data-navmenu="Storm Details"}
-===
-
-### {}
-```{r}
-# TODO: add storm specific fatalities
-```
-
 Normalization Calculator {data-navmenu="Compute"}
 ===
 
@@ -1741,154 +1733,5 @@ output$all_storms_map <- renderLeaflet({
 leafletOutput("all_storms_map", height = "100%")
 ```
 
-Fatalities {data-navmenu="All Storms"}
-===
-
-### {data-height=500}
-```{r eval=FALSE, include=FALSE}
-fatality_years <- seq(1900, 2010, by = 10)
-direct_deaths <- c(6000, 275, 0, 408, 26, 654, 466, 213, 104, 228, 1136, 321)
-indirect_deaths <- c(0, 0, 0, 0, 0, 1, 8, 15, 40, 54, 1171, 368)
-
-yearly_fatalities <- data.frame(
-  fatality_years,
-  direct_deaths,
-  indirect_deaths
-) %>%
-  mutate(
-    fatality_years = as.Date(paste0(fatality_years, "-01-01"))
-  )
-
-yearly_fatalities_ts <- yearly_fatalities %>%
-  select(-fatality_years) %>%
-  xts(order.by = yearly_fatalities$fatality_years)
-
-output$decade_fatalities <- renderDygraph(
-  dygraph(yearly_fatalities_ts, main = "Fatalities By Decade") %>%
-    dySeries("direct_deaths", label = "Direct Deaths") %>%
-    dySeries("indirect_deaths", label = "Indirect Deaths") %>%
-    dyRangeSelector()
-)
-
-dygraphOutput("decade_fatalities")
-```
-
-### {data-height=500}
-```{r eval=FALSE, include=FALSE}
-surge_yearly <- c(0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 410, 107)
-surf_yearly <- c(0, 0, 0, 0, 0, 0, 0, 14, 2, 12, 12, 17)
-rough_seas_yearly <- c(0, 0, 0, 0, 16, 0, 2, 0, 24, 17, 0, 14)
-rip_current_yearly <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 14, 3)
-freshwater_floods_yearly <- c(0, 0, 0, 0, 0, 200, 12, 151, 0, 117, 50, 284)
-wind_yearly <- c(0, 0, 0, 0, 0, 0, 0, 8, 14, 23, 11, 82)
-tree_fall_yearly <- c(0, 0, 0, 0, 1, 0, 0, 1, 0, 9, 24, 56)
-tornado_yearly <- c(0, 0, 0, 0, 1, 12, 43, 7, 0, 7, 11, 7)
-traffic_yearly <- c(0, 0, 0, 0, 0, 0, 0, 4, 0, 3, 2, 1)
-traffic_accident_yearly <- c(0, 0, 0, 0, 0, 0, 5, 0, 0, 8, 26, 11)
-electrocution_yearly <- c(0, 0, 0, 0, 0, 0, 2, 0, 0, 5, 2, 7)
-other_yearly <- c(0, 0, 0, 0, 5, 0, 5, 11, 15, 13, 37, 7)
-
-yearly_fatalities_type <- data.frame(
-  fatality_years,
-  surge_yearly,
-  surf_yearly,
-  rough_seas_yearly,
-  rip_current_yearly,
-  freshwater_floods_yearly,
-  wind_yearly,
-  tree_fall_yearly,
-  tornado_yearly,
-  traffic_yearly,
-  traffic_accident_yearly,
-  electrocution_yearly,
-  other_yearly
-) %>%
-  mutate(
-    fatality_years = as.Date(paste0(fatality_years, "-01-01"))
-  )
-
-yearly_fatalities_type_ts <- yearly_fatalities_type %>%
-  select(-fatality_years) %>%
-  xts(order.by = yearly_fatalities_type$fatality_years)
-
-output$decade_fatalities_type <- renderDygraph(
-  dygraph(yearly_fatalities_type_ts, main = "Fatality Types By Decade") %>%
-    dySeries("surge_yearly", label = "Surge") %>%
-    dySeries("surf_yearly", label = "Surf") %>%
-    dySeries("rough_seas_yearly", label = "Rough Seas") %>%
-    dySeries("rip_current_yearly", label = "Rip Current") %>%
-    dySeries("freshwater_floods_yearly", label = "Freshwater Floods") %>%
-    dySeries("wind_yearly", label = "Wind") %>%
-    dySeries("tree_fall_yearly", label = "Tree Fall") %>%
-    dySeries("tornado_yearly", label = "Tornado") %>%
-    dySeries("traffic_yearly", label = "Traffic") %>%
-    dySeries("traffic_accident_yearly", label = "Traffic Accident") %>%
-    dySeries("electrocution_yearly", label = "Electrocution") %>%
-    dySeries("other_yearly", label = "Other") %>%
-    dyRangeSelector()
-)
-
-dygraphOutput("decade_fatalities_type")
-```
-
-### {data-height=500}
-```{r eval=FALSE, include=FALSE}
-fatality_type <- c(
-  "Surge",
-  "Surf",
-  "Rough Seas",
-  "Rip Current",
-  "Floods",
-  "Wind",
-  "Tree Fall",
-  "Tornado",
-  "Traffic",
-  "Traffic Accident",
-  "Electrocution",
-  "Other"
-)
-fatality_totals <- c(520, 56, 77, 23, 826, 131, 91, 88, 10, 45, 16, 56)
-
-aggregate_fatality_types <- data.frame(fatality_type, fatality_totals)
-
-output$aggregate_fatalities <- renderBillboarder(
-  billboarder() %>%
-    bb_piechart(aggregate_fatality_types)
-  #%>% bb_legend(position = "right")
-)
-
-billboarderOutput("aggregate_fatalities")
-```
-
-### {data-height=500}
-```{r eval=FALSE, include=FALSE}
-```
-
-Column {data-width=500 .tabset}
----
-
-### Fatality Map {}
-```{r}
-
-```
-
-### Fatality Geo Data {}
-```{r}
-
-```
-
-Column {data-width=500}
----
-
-### Perils {data-height=500}
-```{r}
-
-```
-
-### Trends {data-height}
-```{r}
-
-```
-
 About 
 ===