diff --git a/vignettes/other-data-sources.Rmd b/vignettes/other-data-sources.Rmd
index 51b92503..17d063d6 100644
--- a/vignettes/other-data-sources.Rmd
+++ b/vignettes/other-data-sources.Rmd
@@ -20,21 +20,23 @@ library(conmat)
 
 The primary goal of the conmat package is to be able to get a contact matrix for a given age population. It was initially written for work done in Australia, and so the initial focus was on cleaning and extracting data from the Australian Bureau of Statistics. 
 
-This vignette focusses on using other data sources with conmat.
+This vignette focuses on using other data sources with conmat.
 
-We can use some other functions from `socialmixr` to extract similar estimates for different populations in different countries.
+# Using `socialmixr`
 
-We could extract some data from Italy using the [`socialmixr`](https://epiforecasts.io/socialmixr/) R package
+We can use some functions from `socialmixr` to extract similar estimates for different populations in different countries.
+
+We could extract some data from Italy using the [`socialmixr`](https://epiforecasts.io/socialmixr/) R package:
 
 ```{r}
 library(socialmixr)
 
-italy_2005 <- wpp_age("Italy", "2005")
+italy_2005 <- socialmixr::wpp_age("Italy", "2005")
 
 head(italy_2005)
 ```
 
-We can then convert this data into a `conmat_population` object and use it in `extrapolate_polymod`
+We can then convert this data into a `conmat_population` object and use it in `extrapolate_polymod`:
 
 ```{r}
 italy_2005_pop <- as_conmat_population(
@@ -54,6 +56,47 @@ italy_contact <- extrapolate_polymod(
 italy_contact
 ```
 
+# Fitting to other population demographics
+
+It is important to consider the contact survey you are using to fit to your country or population of interest.
+
+Models built on contact patterns (and thus contact surveys) different to your population of interest may give different results. For example, if a model was fitted using the POLYMOD contact survey (which is based in Europe, and thus generally does not have people from multiple generations living within the same household) for a population that has multi-generational households such as China, the results will likely be different in comparison to using a contact survey from China itself.
+
+For further discussion on this problem, refer to the paper "Apparent structural changes in contact patterns during COVID-19 were driven by survey design and long-term demographic trends" by Harris et al. (2024). https://arxiv.org/abs/2406.01639
+
+We will walk through model creation using other contact surveys in this example. Here we use China. 
+
+```{r}
+# Another way of downloading the contact survey from socialmixr
+socialmixr::list_surveys()
+
+# Once we know which survey we want, we download it from Zenodo
+china_survey <- socialmixr::get_survey("https://doi.org/10.5281/zenodo.3878754")
+
+china_imputed <- impupte_contact_data(china_survey)
+
+china_filtered <- china_imputed %>%
+    dplyr::group_by(part_id) %>%
+    dplyr::mutate(
+      missing_any_contact_age = any(is.na(cnt_age_exact)),
+      missing_any_contact_setting = any(
+        is.na(cnt_home) | 
+          is.na(cnt_work) | 
+          is.na(cnt_school) | 
+          is.na(cnt_transport) | 
+          is.na(cnt_otherplace) | 
+          is.na(cnt_otherpublicplace)
+      )
+    ) %>%
+    dplyr::ungroup() %>%
+    dplyr::filter(
+      !is.na(part_age),
+      !missing_any_contact_age,
+      !missing_any_contact_setting
+    )
+```
+
+
 # Creating a next generation matrix (NGM)
 
 To create a next generation matrix, you can use either a conmat population