diff --git a/Report/create_reports.R b/Report/create_reports.R
index 280f0a3..97419fd 100644
--- a/Report/create_reports.R
+++ b/Report/create_reports.R
@@ -144,7 +144,9 @@ save_score_errors <- list()
 ## Score predictions
 print("Evaluating state forecasts")
 geo_type <- "state"
-state_scores <- evaluate_covid_predictions(state_predictions,
+state_scores <- evaluate_covidcast(
+  state_predictions,
+  signals,
   err_measures,
   geo_type = geo_type
 )
diff --git a/Report/score.R b/Report/score.R
index 364677a..942ff1f 100644
--- a/Report/score.R
+++ b/Report/score.R
@@ -62,6 +62,7 @@ save_score_cards_wrapper <- function(score_card, geo_type, signal_name, output_d
   }
 }
 
+# Fetch national truth data from CovidHubUtils.
 evaluate_chu <- function(predictions, signals, err_measures) {
   allowed_signals <- c(
     "confirmed_incidence_num",
@@ -85,7 +86,7 @@ evaluate_chu <- function(predictions, signals, err_measures) {
     "deaths_incidence_num" = "JHU",
     "confirmed_admissions_covid_1d" = "HealthData"
   )
-  scores <- c()
+  scores <- list()
   for (signal_name in signals) {
     preds_signal <- predictions %>%
       filter(signal == signal_name)
@@ -103,6 +104,8 @@ evaluate_chu <- function(predictions, signals, err_measures) {
         geo_type,
         abbreviation
       ))
+    ## select equivalent to
+    # select(target_end_date, actual, geo_value, full_location_name)
     signal_scores <- evaluate_predictions(preds_signal,
       truth_data = chu_truth,
       err_measures,
@@ -113,7 +116,117 @@ evaluate_chu <- function(predictions, signals, err_measures) {
         "forecaster"
       )
     )
-    scores <- rbind(scores, signal_scores)
+    scores[[signal_name]] <- signal_scores
   }
+  return(bind_rows(scores))
+}
+
+# Fetch truth data from COVIDcast. This function bypasses some of the slow parts
+# of the `evalcast` pipeline by pulling all data from COVIDcast together.
+evaluate_covidcast <- function(predictions, signals, err_measures, geo_type) {
+  allowed_signals <- c(
+    "confirmed_incidence_num",
+    "deaths_incidence_num",
+    "confirmed_admissions_covid_1d"
+  )
+  assert_that(all(signals %in% allowed_signals),
+    msg = paste(
+      "Signal not allowed:",
+      setdiff(signals, allowed_signals)
+    )
+  )
+
+  source_map <- list(
+    "confirmed_incidence_num" = "jhu-csse",
+    "deaths_incidence_num" = "jhu-csse",
+    "confirmed_admissions_covid_1d" = "hhs"
+  )
+  scores <- list()
+  for (signal_name in signals) {
+    preds_signal <- predictions %>%
+      filter(signal == signal_name)
+    source <- source_map[[signal_name]]
+    covidcast_truth <- get_covidcast_period_actuals(preds_signal, source, signal_name, geo_type)
+    signal_scores <- evaluate_predictions(preds_signal,
+      truth_data = covidcast_truth,
+      err_measures,
+      grp_vars = c(
+        "target_end_date",
+        "geo_value",
+        "ahead",
+        "forecaster"
+      )
+    )
+    scores[[signal_name]] <- signal_scores
+  }
+
+  scores <- bind_rows(scores) %>%
+    arrange(ahead, geo_value, forecaster, forecast_date, data_source, signal, target_end_date, incidence_period) %>%
+    select(ahead, geo_value, forecaster, forecast_date, data_source, signal, target_end_date, incidence_period, everything())
   return(scores)
 }
+
+
+get_covidcast_period_actuals <- function(response, source, signal_name, geo_type) {
+  # Get start/end dates of each period we want to sum truth values over.
+  target_periods <- response %>%
+    select(.data$forecast_date, .data$incidence_period, .data$ahead) %>%
+    distinct() %>%
+    purrr::pmap_dfr(get_target_period) %>%
+    distinct()
+
+  # Compute the actual values that the forecaster is trying to
+  # predict. In particular,
+  # - get most recent data available from covidcast for these target periods
+  # - sum up the response over the target incidence period
+  target_periods <- target_periods %>%
+    mutate(available = .data$end <= Sys.Date()) %>%
+    filter(.data$available) %>%
+    select(-.data$available)
+
+  covidcast_truth <- covidcast::covidcast_signal(
+    source,
+    signal_name,
+    geo_type = geo_type,
+    start_day = as.Date(min(target_periods$start)),
+    end_day = as.Date(max(target_periods$end))
+  ) %>%
+    select(data_source, signal, geo_value, time_value, value)
+
+  # Expand out each period by day so easier to join on.
+  target_periods <- target_periods %>% purrr::pmap_dfr(function(start_date, end_date) {
+    tibble(
+      start = start_date,
+      target_end_date = end_date,
+      day = seq.Date(from = start_date, to = end_date, by = 1)
+    )
+  })
+
+  period_truth <- full_join(covidcast_truth, target_periods, by = c("time_value" = "day"))
+
+  if (signal_name != "confirmed_admissions_covid_1d") {
+    # For deaths and cases, expect each truth data period to cover a week
+    check_count <- period_truth %>%
+      group_by(.data$geo_value, .data$start, .data$target_end_date) %>%
+      summarize(num = n(), .groups = "drop") %>%
+      filter(num < 7)
+
+    if (nrow(check_count) != 0) {
+      warning(paste0(
+        "Some or all data missing for the following target periods: ",
+        paste(
+          paste(period_truth$start, period_truth$target_end_date, sep = "-"),
+          collapse = ", "
+        ),
+        "."
+      ))
+    }
+  }
+
+  period_truth <- period_truth %>%
+    group_by(.data$geo_value, .data$target_end_date) %>%
+    summarize(actual = sum(.data$value), .groups = "drop") %>%
+    select(.data$target_end_date, .data$actual, .data$geo_value)
+
+  return(period_truth)
+}