diff --git a/R/predict_future_infections.R b/R/predict_future_infections.R
index 4f22fd1f86b3caea3723414523a43b846fac9753..8bee90f763197312407c41be8fd3e6759b214bbc 100644
--- a/R/predict_future_infections.R
+++ b/R/predict_future_infections.R
@@ -6,9 +6,9 @@
 #' meanlog and sdlog are the log-normal distribution parameters derived from
 #' the incubation period characteristics described in Xin et al. (2021).
 #'
-#' @param last_day_reported_infection Number of days the last infection was reported after the event (1 = event day)
-#' @param total_reported_infections Number of reported symptomatic infections so far
-#' @param total_expected_infections Number of expected symptomatic infections in total
+#' @param last_day_reported_infection Number of days the last infection was reported after the event (0 = event day).
+#' @param total_reported_infections Number of reported symptomatic infections so far.
+#' @param total_expected_infections Number of expected symptomatic infections in total.
 #' @param meanlog Number, the parameter of mean from the log-normal distribution.
 #' @param sdlog Number, the parameter of sd from the log-normal distribution.
 #' @return Vector with expected future infections per day after the event.
diff --git a/vignettes/future_infections.Rmd b/vignettes/future_infections.Rmd
index 2837240e67295a94d2d6d0c8cd3e53bb5a83fd9a..b87b1253a4d5e62aefe89852388cf6b50fc2fd26 100644
--- a/vignettes/future_infections.Rmd
+++ b/vignettes/future_infections.Rmd
@@ -119,7 +119,7 @@ When at some point the updated `total_reported_infections` is not smaller than `
 ### Output
 
 ```{r predict_future_infections}
-last_day_reported_infections = 4 # day 1 = event day
+last_day_reported_infections = 3 # day 0 = event day
 total_reported_infections = 4
 total_expected_infections = get_expected_total_infections(25, "mixed", "high risk")
 
@@ -137,7 +137,7 @@ The function `predict_future_infections()` creates a vector with values represen
 ```{r libraries, foldcode = TRUE, message = FALSE}
 data <- data.frame("Erkrankungsdatum" = as.Date("2022-03-15") + 0:3,
                    "Neue_Faelle" = c(0, 0, 1, 3))
-expected <- data.frame("Erkrankungsdatum" = as.Date("2022-03-15") + 0:(length(predicted_daily_infections) - 1),
+expected <- data.frame("Erkrankungsdatum" = as.Date("2022-03-15") + 1:(length(predicted_daily_infections)),
                        "ErwarteteWeitereFaelle" = predicted_daily_infections)
 g <- ggplot(expected) +
     geom_bar(
@@ -155,8 +155,8 @@ g <- ggplot(expected) +
       ),
       stat = 'identity'
     ) +
-    geom_vline(xintercept = expected$Erkrankungsdatum[1]) +
-    geom_label(aes(x = expected$Erkrankungsdatum[1], y = data$Neue_Faelle[1] + 4, label = "Event"), 
+    geom_vline(xintercept = data$Erkrankungsdatum[1]) +
+    geom_label(aes(x = data$Erkrankungsdatum[1], y = data$Neue_Faelle[1] + 4, label = "Event"), 
                colour = "black", fill = "white", vjust = 1, size = 7) +
     scale_y_continuous(breaks = function(x) unique(
       floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +