Fix typo

vignettes/demographic_turnover.Rmd

@@ -100,7 +100,7 @@ final_size_df <- Reduce(rbind, final_size_est)
 
 Having simulated the distribution of initial epidemic sizes based on the distribution of $R_0$, we next implement waning and influx of new susceptibles to calculate the level of immunity over coming years, relative to the level of immunity at the end of the epidemic. Because the effective reproduction number (i.e. $R$ is partially susceptible population) is equal to $R = R_0 \times S$, we can use our prediction about relative immunity to calculate relative susceptibility, i.e. $susceptibility = 1-immunity$, then in turn predict $R$ over the coming years as population susceptibility increases. When $R$ rises above the critical value of 1, there is potential for sustained transmission in the population and hence another large epidemic.
 
-Again, we use the `Map()` function to calculate the level of susceptibility - and hence $R$ - over the distribution of $R_0$ values we have assumed for a re-emerging epidemic. In this example, the distribution is the same as the original epidemic (which is plausible if it is the same pathogen and setting), but we could alter this assumption if we think factors like population density or climate change might change the transmissiblity of the infection in future.
+Again, we use the `Map()` function to calculate the level of susceptibility - and hence $R$ - over the distribution of $R_0$ values we have assumed for a re-emerging epidemic. In this example, the distribution is the same as the original epidemic (which is plausible if it is the same pathogen and setting), but we could alter this assumption if we think factors like population density or climate change might change the transmissibility of the infection in future.
 
 ```{r}
 # define changes in susceptibility over time from waning