diff --git a/02_activities/assignments/COVID-19 Case Rates by Vaccination Status and Age Group.png b/02_activities/assignments/COVID-19 Case Rates by Vaccination Status and Age Group.png
new file mode 100644
index 000000000..f35842e76
Binary files /dev/null and b/02_activities/assignments/COVID-19 Case Rates by Vaccination Status and Age Group.png differ
diff --git a/02_activities/assignments/assignment3.ipynb b/02_activities/assignments/assignment3.ipynb
new file mode 100644
index 000000000..2a87af6a1
--- /dev/null
+++ b/02_activities/assignments/assignment3.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96fcd957",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Social Housing Waiting List Visualization, Zahra Sepahi\n",
+    "# City of Toronto Open Data Portal\n",
+    "# Link to complete dataset: https://open.toronto.ca/dataset/centralized-waiting-list-activity-for-social-housing/\n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Download the dataset\n",
+    "df = pd.read_csv(\"/Users/zahrasepahi/visualization/02_activities/assignments/centralized_waiting_list_activity.csv\")\n",
+    "\n",
+    "# Evaluate only the relevant columns\n",
+    "df = df[[\"Quarter/Year\", \"Total active waiting list\"]]\n",
+    "\n",
+    "# Remove rows with missing values\n",
+    "df = df.dropna()\n",
+    "\n",
+    "# Convert Quarter/Year to string for plotting\n",
+    "df[\"Quarter/Year\"] = df[\"Quarter/Year\"].astype(str)\n",
+    "\n",
+    "# Visualization\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(df[\"Quarter/Year\"], df[\"Total active waiting list\"], marker='o', color='teal', linewidth=2)\n",
+    "plt.xlabel(\"Quarter / Year\")\n",
+    "plt.ylabel(\"Total Active Waiting List (Households)\")\n",
+    "plt.title(\"Toronto Social Housing Waiting List Over Time\")\n",
+    "plt.xticks(df[\"Quarter/Year\"][::2], rotation=45)\n",
+    "plt.grid(True, linestyle='--', alpha=0.5)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save the figure for submission\n",
+    "plt.savefig(\"/Users/zahrasepahi/visualization/02_activities/assignments/toronto_social_housing_waitlist.png\")\n",
+    "plt.show()\n",
+    "\n",
+    "# My apologies for this average looking plot. The trend line doesn't seem to make sense and didn't know how to fix it. :( "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assignment_2.md b/02_activities/assignments/assignment_2.md
index e0244e4b9..cf44a4cc8 100644
--- a/02_activities/assignments/assignment_2.md
+++ b/02_activities/assignments/assignment_2.md
@@ -10,24 +10,24 @@
 - For each visualization (good and bad):  
     - Explain (with reference to material covered up to date, along with readings and other scholarly sources, as needed) why you classified that visualization the way you did.
       ```
-      Your answer...
-
-
-
-
-
-
-
+        Good visualization: "10 Year Trends in Prescription Opioid Use in Commercially Insured by Health Care Cost Institute" (https://public.tableau.com/app/profile/health.care.cost.institute/viz/Opioid10YearTrendsBlogInteractiveTool070919/Figure210YearTrends)
+        The visualization “10 Year Trends in Prescription Opioid Use in the Commercially Insured” by the Health Care Cost Institute is a good example of effective data visualization because it presents complex information in an interpretable manner. One of the main strengths of this visualization is its use of colour. The colours are defined but not too bold, which helps distinguish between different opioid types without distracting the viewer (e.g. not using red or neon colours). This makes it easier to focus on the overall trends rather than being distracted. The use of line graphs is also appropriate, as it clearly shows how prescription opioid use changes over time. Since data covers a ten-year period, the continuous trend of line graphs makes it simple to observe fluctuations and long-term patterns. Another reason this visualization is good is that it incorporates a lot of information without feeling overwhelmed. The interactive features are a major advantage when looking at the different opioid trends plotted by individual states and at a national level. This makes the visualization useful for both broad comparisons and more location-specific analysis. In addition, the visualization considers different ways opioid prescriptions are measured, such as dosage types and terminology, thereby ensuring the data is presented in a more complete and accurate way. Overall, this visualization is effective because it clearly communicates trends, supports comparison, and is accessible to a wide audience. 
+    
+        Bad visualization: "my 2024 health stats by Arjen Groeneveld" (https://public.tableau.com/app/profile/arjen.groeneveld/viz/24healthstatsfeb25/Dashboard1)   
+        The visualization “My 2024 Health Stats” by Arjen Groeneveld is not effective data visualization because it is difficult to understand and does not clearly communicate useful insights. While the dashboard separates data into different health categories such as energy, steps, weight, workouts, and sleep, this structure alone does not make the data easy to interpret. The chart on the right-hand side is most problematic for me because it is very difficult to identify meaningful trends, patterns, or comparisons, thereby limiting the main purpose of data visualization being quick interpretation.  
+        The main issue of the chart is that does not allow the viewer to easily identify trends or patterns. The axes and visual elements are unclear and there is no clear way to compare values over time, making it difficult to understand whether health metrics are improving, declining, or staying the same. The visualization forces the reader to spend time trying to decode what they are looking at and defying the main purpose of data visualization, which is to make information easier to understand.
+        Additionally, the visualization lacks context because there are no reference points or benchmarks to help readers determine whether the metrics presented are good or bad. When the reader does not have adequate context, this makes the data meaningless and hard to draw conclusions for any potential lifestyle changes. Overall, although the idea of tracking personal health data is useful, but this visualization does not present the information in a clear way.
+        
       ```
     - How could this data visualization have been improved?  
       ```
-      Your answer...
-
-
-
-
-
+    Your answer...
+        While the opioid visualization is already good, there are a few ways it could be improved to make it even more effective. One improvement would be adding more context directly to the graph, such as explaining why certain changes happen. For instance, if there were a major policy change or new prescribing guideline, this could have influenced opioid use during this time. Adding short annotations or markers on this line graph can be added to provide context for audiences to understanding what factors could be mediating these fluctuations.  
+        The visualization could also benefit from a short summary of key takeaways at the bottom of the figure. Including a brief takeway section highlighting major findings of this analysis would make the data easier to understand at a glance. These additions would support the viewer in interpreting the data more confidently.
 
+        The “My 2024 Health Stats” visualization could be improved by focusing more on clarity and simplicity. One of the biggest improvements would be changing the chart to a more digestible format, e.g. a line graph, because it can illustrate changes health patterns across time.
+        Clearer labels and explanations would also be useful. Each chart should show what is metric is being plotted and their corresponding units. Legends, colours, and icons should be clear and consistent across the plot (e.g. dashes vs. circles). Including short descriptions explaining what each section means would help viewers understand the data without misinterpretation. Separating each health metric into their own chart instead of combining them all into a single chart would reduce clutter.
+        Including context regarding the rationale for each metric selection via a short description would make the reader understand the importance and purpose of the metrics presented. Also, adding benchmarks or suggestions about recommended sleep ranges, step goals, or workout frequency would help viewers understand how their data compares to healthy standards. By simplifying the design and focusing on what the viewer needs to understand and the rationale for each element, this visualization could become a clearer and more informative visualization of personal health data.
       
       ```
 - Word count should not exceed (as a maximum) 500 words for each visualization (i.e. 
diff --git a/02_activities/assignments/assignment_3_ontario.md b/02_activities/assignments/assignment_3_ontario.md
new file mode 100644
index 000000000..84a82b760
--- /dev/null
+++ b/02_activities/assignments/assignment_3_ontario.md
@@ -0,0 +1,69 @@
+# Data Visualization
+
+## Assignment 3: Final Project
+
+### Requirements:
+- We will finish this class by giving you the chance to use what you have learned in a practical context, by creating data visualizations from raw data. 
+- Choose a dataset of interest from the [City of Toronto’s Open Data Portal](https://www.toronto.ca/city-government/data-research-maps/open-data/) or [Ontario’s Open Data Catalogue](https://data.ontario.ca/). 
+- Using Python and one other data visualization software (Excel or free alternative, Tableau Public, any other tool you prefer), create two distinct visualizations from your dataset of choice.  
+- For each visualization, describe and justify: 
+    > What software did you use to create your data visualization? I used Microsoft Excel to create my data visualization. 
+
+    > Who is your intended audience? My intended audience are public health policy makers, medical professionals, and general public.
+    
+    > What information or message are you trying to convey with your visualization? Significant variations in COVID-19 cases depending on the vaccination status.
+    
+    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? I selected a grouped bar chart to compare the different vaccination statuses side by side. By categorizing the different groups with discernible colours, labelling the axes clearly, and limiting crowding, this allowed for a clear presentation of the data set. 
+    
+    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? Excel is much less reproducible than using a code tool, e.g. Python, because it requires manual updating of the dataset. However, if you manually update the data in the same Excel file, the data visualization will produce an updated verison.
+    
+    > How did you ensure that your data visualization is accessible? I ensured that my data visualization is accessible by preventing overcrowing of the vaccination groups and choosing colours that are clearly different from one another making identification of trends easier to assess.   
+    
+    > Who are the individuals and communities who might be impacted by your visualization? The individuals and communities who might be impacted by my visualization are Ontario citizens considering taking the vaccine and public health policymakers responsible for educating the public on vaccination efficacy.
+    
+    > How did you choose which features of your chosen dataset to include or exclude from your visualization? The features I included in my visualization are age group, vaccination status, and COVID-19 case rate. This provided a bird eye view of age and vaccination status effects COVID-19 transmission. 
+    
+    > What ‘underwater labour’ contributed to your final data visualization product? The ‘underwater labour’ that contributed to my final data visualization product was choosing the best plot type that would illustrate this data set in the most clear, quick to understand, and accessible way.
+
+- This assignment is intentionally open-ended - you are free to create static or dynamic data visualizations, maps, or whatever form of data visualization you think best communicates your information to your audience of choice! 
+- Total word count should not exceed **(as a maximum) 1000 words** 
+ 
+### Why am I doing this assignment?:  
+- This ongoing assignment ensures active participation in the course, and assesses the learning outcomes: 
+* Create and customize data visualizations from start to finish in Python
+* Apply general design principles to create accessible and equitable data visualizations
+* Use data visualization to tell a story  
+- This would be a great project to include in your GitHub Portfolio – put in the effort to make it something worthy of showing prospective employers!
+
+### Rubric:
+
+| Component         | Scoring  | Requirement                                                                 |
+|-------------------|----------|-----------------------------------------------------------------------------|
+| Data Visualizations | Complete/Incomplete | - Data visualizations are distinct from each other<br>- Data visualizations are clearly identified<br>- Different sources/rationales (text with two images of data, if visualizations are labeled)<br>- High-quality visuals (high resolution and clear data)<br>- Data visualizations follow best practices of accessibility |
+| Written Explanations | Complete/Incomplete | - All questions from assignment description are answered for each visualization<br>- Explanations are supported by course content or scholarly sources, where needed |
+| Code              | Complete/Incomplete | - All code is included as an appendix with your final submissions<br>- Code is clearly commented and reproducible |
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `23:59 - 02/02/2026`
+* The branch name for your repo should be: `assignment-3`
+* What to submit for this assignment:
+    * A folder/directory containing:
+        * This file (assignment_3.md)
+        * Two data visualizations 
+        * Two markdown files for each both visualizations with their written descriptions.
+        * Link to your dataset of choice.
+        * Complete and commented code as an appendix (for your visualization made with Python, and for the other, if relevant) 
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/visualization/pull/<pr_id>`
+    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
+
+Checklist:
+- [ ] Create a branch called `assignment-3`.
+- [ ] Ensure that the repository is public.
+- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [ ] Verify that the link is accessible in a private browser window.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.
diff --git a/02_activities/assignments/assignment_3_toronto.md b/02_activities/assignments/assignment_3_toronto.md
new file mode 100644
index 000000000..fcbc65bf2
--- /dev/null
+++ b/02_activities/assignments/assignment_3_toronto.md
@@ -0,0 +1,69 @@
+# Data Visualization
+
+## Assignment 3: Final Project
+
+### Requirements:
+- We will finish this class by giving you the chance to use what you have learned in a practical context, by creating data visualizations from raw data. 
+- Choose a dataset of interest from the [City of Toronto’s Open Data Portal](https://www.toronto.ca/city-government/data-research-maps/open-data/) or [Ontario’s Open Data Catalogue](https://data.ontario.ca/). 
+- Using Python and one other data visualization software (Excel or free alternative, Tableau Public, any other tool you prefer), create two distinct visualizations from your dataset of choice.  
+- For each visualization, describe and justify: 
+    > What software did you use to create your data visualization? I used Python to create my data visualization. I used panda library to clean the data and matplotlib for plotting and presenting the data.
+
+    > Who is your intended audience? My intended audience is for city planners, government policy makers, and general public.
+    
+    > What information or message are you trying to convey with your visualization? Housing demands have been persistent over time. 
+    
+    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? I chose a line graph to show the trend of how housing demands varied across time. I chose teal as a neutral colour. I added circle symbols to identify each date recording. I reduced the number of labels on x-axis to prevent crowding. Axis and graph titles were labelled clearly. 
+    
+    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? I ensured that data visualizations are reproducible because I used a Python script to generate the illustrations via a specific set of instructions. These instructions will continue to output the same design despite the data being updated as long as the same file name is used.
+    
+    > How did you ensure that your data visualization is accessible? I ensured the data visualization is accessible by selecting a clear and identifiable colour that has high contrast with its background. The font size is also large and there is limited clutter ensuring fast and easy data interpretation. 
+    
+    > Who are the individuals and communities who might be impacted by your visualization? The individuals and communities who might be impacted by your visualization are individuals that rely on social housing such as newcomers and low-income households. 
+    
+    > How did you choose which features of your chosen dataset to include or exclude from your visualization? My visualization focuses on the overall macroscopic view of total active waiting list counts across time. This provides high-level overview of the housing crisis without being biased by a specific time point.
+    
+    > What ‘underwater labour’ contributed to your final data visualization product? The 'underwater labour’ that contributed to my final data visualization product is removing unfilled data points, determining which columns to include/remove, and trying different colours and font sizes.
+
+- This assignment is intentionally open-ended - you are free to create static or dynamic data visualizations, maps, or whatever form of data visualization you think best communicates your information to your audience of choice! 
+- Total word count should not exceed **(as a maximum) 1000 words** 
+ 
+### Why am I doing this assignment?:  
+- This ongoing assignment ensures active participation in the course, and assesses the learning outcomes: 
+* Create and customize data visualizations from start to finish in Python
+* Apply general design principles to create accessible and equitable data visualizations
+* Use data visualization to tell a story  
+- This would be a great project to include in your GitHub Portfolio – put in the effort to make it something worthy of showing prospective employers!
+
+### Rubric:
+
+| Component         | Scoring  | Requirement                                                                 |
+|-------------------|----------|-----------------------------------------------------------------------------|
+| Data Visualizations | Complete/Incomplete | - Data visualizations are distinct from each other<br>- Data visualizations are clearly identified<br>- Different sources/rationales (text with two images of data, if visualizations are labeled)<br>- High-quality visuals (high resolution and clear data)<br>- Data visualizations follow best practices of accessibility |
+| Written Explanations | Complete/Incomplete | - All questions from assignment description are answered for each visualization<br>- Explanations are supported by course content or scholarly sources, where needed |
+| Code              | Complete/Incomplete | - All code is included as an appendix with your final submissions<br>- Code is clearly commented and reproducible |
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `23:59 - 02/02/2026`
+* The branch name for your repo should be: `assignment-3`
+* What to submit for this assignment:
+    * A folder/directory containing:
+        * This file (assignment_3.md)
+        * Two data visualizations 
+        * Two markdown files for each both visualizations with their written descriptions.
+        * Link to your dataset of choice.
+        * Complete and commented code as an appendix (for your visualization made with Python, and for the other, if relevant) 
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/visualization/pull/<pr_id>`
+    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
+
+Checklist:
+- [ ] Create a branch called `assignment-3`.
+- [ ] Ensure that the repository is public.
+- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [ ] Verify that the link is accessible in a private browser window.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.
diff --git a/02_activities/assignments/cases_by_age_vac_status.csv b/02_activities/assignments/cases_by_age_vac_status.csv
new file mode 100644
index 000000000..4119fdba3
--- /dev/null
+++ b/02_activities/assignments/cases_by_age_vac_status.csv
@@ -0,0 +1,1029 @@
+date,agegroup,cases_unvac_rate_per100K,cases_partial_vac_rate_per100K,cases_notfull_vac_rate_per100K,cases_full_vac_rate_per100K,cases_boost_vac_rate_per100K,cases_unvac_rate_7ma,cases_partial_vac_rate_7ma,cases_notfull_vac_rate_7ma,cases_full_vac_rate_7ma,cases_boost_vac_rate_7ma
+2021-09-13,0-11yrs,6.24,0,,0,,,,,,
+2021-09-13,12-17yrs,13.11,1.92,,0.8,,,,,,
+2021-09-13,18-39yrs,13.51,6.7,,2,,,,,,
+2021-09-13,40-59yrs,10.91,6.04,,1.41,,,,,,
+2021-09-13,60-79yrs,9.62,0,,0.49,,,,,,
+2021-09-13,80+,13.64,0,,0.64,,,,,,
+2021-09-13,ALL,9.58,5.02,,1.26,,,,,,
+2021-09-14,0-11yrs,4.8,5.58,,0,,,,,,
+2021-09-14,12-17yrs,17.33,5.86,,0.48,,,,,,
+2021-09-14,18-39yrs,11.81,6.75,,2.09,,,,,,
+2021-09-14,40-59yrs,11.26,7.98,,1.24,,,,,,
+2021-09-14,60-79yrs,7.57,1.17,,0.61,,,,,,
+2021-09-14,80+,13.76,0,,0.8,,,,,,
+2021-09-14,ALL,8.64,6.21,,1.26,,,,,,
+2021-09-15,0-11yrs,4.75,0,,0,,,,,,
+2021-09-15,12-17yrs,12.9,1,,0.47,,,,,,
+2021-09-15,18-39yrs,13.39,7.5,,2.25,,,,,,
+2021-09-15,40-59yrs,9.65,4.28,,1.6,,,,,,
+2021-09-15,60-79yrs,11.01,2.38,,0.65,,,,,,
+2021-09-15,80+,5.56,0,,1.45,,,,,,
+2021-09-15,ALL,8.65,5.11,,1.46,,,,,,
+2021-09-16,0-11yrs,7.24,0,,0,,,,,,
+2021-09-16,12-17yrs,13.96,7.06,,0.31,,,,,,
+2021-09-16,18-39yrs,21.77,7.51,,3.59,,,,,,
+2021-09-16,40-59yrs,15.77,6.69,,1.79,,,,,,
+2021-09-16,60-79yrs,14.06,3.61,,1.4,,,,,,
+2021-09-16,80+,16.84,0,,0.96,,,,,,
+2021-09-16,ALL,13.3,6.54,,2.09,,,,,,
+2021-09-17,0-11yrs,6.58,10.47,,0,,,,,,
+2021-09-17,12-17yrs,17.47,3.05,,1.09,,,,,,
+2021-09-17,18-39yrs,19.31,8.67,,2.99,,,,,,
+2021-09-17,40-59yrs,10.85,4.31,,2.34,,,,,,
+2021-09-17,60-79yrs,13.74,2.43,,1.33,,,,,,
+2021-09-17,80+,0,0,,1.28,,,,,,
+2021-09-17,ALL,11.54,6.19,,2.12,,,,,,
+2021-09-18,0-11yrs,7.63,10.31,,0,,,,,,
+2021-09-18,12-17yrs,19.63,4.09,,1.09,,,,,,
+2021-09-18,18-39yrs,19.9,8.69,,3.24,,,,,,
+2021-09-18,40-59yrs,12.17,7.24,,1.56,,,,,,
+2021-09-18,60-79yrs,9.95,3.68,,1.48,,,,,,
+2021-09-18,80+,8.6,0,,1.12,,,,,,
+2021-09-18,ALL,12.34,7.16,,1.99,,,,,,
+2021-09-19,0-11yrs,8.4,0,,0,,6.52,3.77,,0,
+2021-09-19,12-17yrs,12.09,5.13,,1.08,,15.21,4.02,,0.76,
+2021-09-19,18-39yrs,15.16,6.85,,2.19,,16.41,7.52,,2.62,
+2021-09-19,40-59yrs,11.46,3.4,,1.75,,11.72,5.71,,1.67,
+2021-09-19,60-79yrs,13.05,3.71,,1.29,,11.29,2.43,,1.04,
+2021-09-19,80+,11.56,0,,2.08,,9.99,0,,1.19,
+2021-09-19,ALL,11.14,5.19,,1.74,,10.74,5.92,,1.7,
+2021-09-20,0-11yrs,6.58,5.11,,0,,6.57,4.5,,0,
+2021-09-20,12-17yrs,11.19,5.14,,1.08,,14.94,4.48,,0.8,
+2021-09-20,18-39yrs,11.89,5.92,,2.05,,16.18,7.41,,2.63,
+2021-09-20,40-59yrs,11.34,3.41,,1.39,,11.79,5.33,,1.67,
+2021-09-20,60-79yrs,10.04,6.2,,1.13,,11.35,3.31,,1.13,
+2021-09-20,80+,8.73,0,,1.44,,9.29,0,,1.3,
+2021-09-20,ALL,9.19,5.09,,1.51,,10.69,5.93,,1.74,
+2021-09-21,0-11yrs,5.69,0,,0,,6.7,3.7,,0,
+2021-09-21,12-17yrs,12.22,4.12,,0.77,,14.21,4.23,,0.84,
+2021-09-21,18-39yrs,12.73,4.74,,1.82,,16.31,7.13,,2.59,
+2021-09-21,40-59yrs,9.5,2.44,,1.46,,11.53,4.54,,1.7,
+2021-09-21,60-79yrs,13.14,4.97,,0.98,,12.14,3.85,,1.18,
+2021-09-21,80+,8.81,0,,1.28,,8.59,0,,1.37,
+2021-09-21,ALL,8.93,3.91,,1.39,,10.73,5.6,,1.76,
+2021-09-22,0-11yrs,4.7,0,,0,,6.69,3.7,,0,
+2021-09-22,12-17yrs,10.87,2.06,,0.77,,13.92,4.38,,0.88,
+2021-09-22,18-39yrs,9.97,3.09,,1.78,,15.82,6.5,,2.52,
+2021-09-22,40-59yrs,7.52,2.45,,1.07,,11.23,4.28,,1.62,
+2021-09-22,60-79yrs,9.27,1.25,,1.02,,11.89,3.69,,1.23,
+2021-09-22,80+,5.95,0,,1.76,,8.64,0,,1.42,
+2021-09-22,ALL,7.1,2.5,,1.3,,10.51,5.23,,1.73,
+2021-09-23,0-11yrs,7.57,0,,0,,6.74,3.7,,0,
+2021-09-23,12-17yrs,18.45,2.07,,0.31,,14.56,3.67,,0.88,
+2021-09-23,18-39yrs,14.8,5.46,,1.78,,14.82,6.2,,2.26,
+2021-09-23,40-59yrs,12.31,4.94,,1.71,,10.74,4.03,,1.61,
+2021-09-23,60-79yrs,11.57,5.05,,1.21,,11.54,3.9,,1.21,
+2021-09-23,80+,24.08,10.04,,0.96,,9.68,1.43,,1.42,
+2021-09-23,ALL,11.12,4.89,,1.46,,10.19,4.99,,1.64,
+2021-09-24,0-11yrs,7.36,0,,0,,6.85,2.2,,0,
+2021-09-24,12-17yrs,22.05,7.22,,1.22,,15.21,4.26,,0.9,
+2021-09-24,18-39yrs,15.31,6.43,,2.19,,14.25,5.88,,2.15,
+2021-09-24,40-59yrs,14.81,10.94,,1.93,,11.3,4.97,,1.55,
+2021-09-24,60-79yrs,14.92,0,,0.87,,11.71,3.55,,1.14,
+2021-09-24,80+,63.69,0,,1.76,,18.77,1.43,,1.49,
+2021-09-24,ALL,11.93,6.71,,1.67,,10.25,5.06,,1.58,
+2021-09-25,0-11yrs,6.06,5.48,,0,,6.62,1.51,,0,
+2021-09-25,12-17yrs,15,3.12,,1.06,,14.55,4.12,,0.9,
+2021-09-25,18-39yrs,14.54,6.95,,1.83,,13.49,5.63,,1.95,
+2021-09-25,40-59yrs,12.17,5.02,,1.73,,11.3,4.66,,1.58,
+2021-09-25,60-79yrs,9.05,3.86,,1.13,,11.58,3.58,,1.09,
+2021-09-25,80+,22.08,0,,1.12,,20.7,1.43,,1.49,
+2021-09-25,ALL,9.92,5.56,,1.52,,9.9,4.84,,1.51,
+2021-09-26,0-11yrs,7.3,5.68,,0,,6.47,2.32,,0,
+2021-09-26,12-17yrs,14.63,2.1,,1.36,,14.92,3.69,,0.94,
+2021-09-26,18-39yrs,15.35,6.03,,2.17,,13.51,5.52,,1.95,
+2021-09-26,40-59yrs,10.34,4.55,,1.6,,11.14,4.82,,1.56,
+2021-09-26,60-79yrs,14.58,2.59,,0.79,,11.8,3.42,,1.02,
+2021-09-26,80+,45.57,0,,0.96,,25.56,1.43,,1.33,
+2021-09-26,ALL,10.71,4.74,,1.51,,9.84,4.77,,1.48,
+2021-09-27,0-11yrs,7.02,0,,0,,6.53,1.59,,0,
+2021-09-27,12-17yrs,13.21,4.19,,0.91,,15.2,3.55,,0.91,
+2021-09-27,18-39yrs,12.53,4.6,,2.07,,13.6,5.33,,1.95,
+2021-09-27,40-59yrs,8.64,3.55,,1.57,,10.76,4.84,,1.58,
+2021-09-27,60-79yrs,14.65,6.5,,1.09,,12.45,3.46,,1.01,
+2021-09-27,80+,23.39,0,,1.59,,27.65,1.43,,1.35,
+2021-09-27,ALL,9.43,4.27,,1.56,,9.88,4.65,,1.49,
+2021-09-28,0-11yrs,4.15,5.87,,0,,6.31,2.43,,0,
+2021-09-28,12-17yrs,11.32,3.14,,1.21,,15.08,3.41,,0.98,
+2021-09-28,18-39yrs,10.24,3.64,,1.56,,13.25,5.17,,1.91,
+2021-09-28,40-59yrs,9.72,4.08,,1.38,,10.79,5.08,,1.57,
+2021-09-28,60-79yrs,14.17,0,,0.49,,12.6,2.75,,0.94,
+2021-09-28,80+,24.26,5.13,,0.96,,29.86,2.17,,1.3,
+2021-09-28,ALL,7.49,3.31,,1.16,,9.67,4.57,,1.45,
+2021-09-29,0-11yrs,4.32,12,,0,,6.25,4.15,,0,
+2021-09-29,12-17yrs,10.93,7.34,,0.6,,15.08,4.17,,0.95,
+2021-09-29,18-39yrs,11.9,3.66,,1.27,,13.52,5.25,,1.84,
+2021-09-29,40-59yrs,11.89,4.63,,1.6,,11.41,5.39,,1.65,
+2021-09-29,60-79yrs,11.82,3.96,,0.68,,12.97,3.14,,0.89,
+2021-09-29,80+,25.41,0,,0.48,,32.64,2.17,,1.12,
+2021-09-29,ALL,8.2,4.44,,1.12,,9.83,4.85,,1.43,
+2021-09-30,0-11yrs,7.02,0,,0,,6.18,4.15,,0,
+2021-09-30,12-17yrs,15.04,5.26,,0.45,,14.6,4.62,,0.97,
+2021-09-30,18-39yrs,13.9,4.9,,1.93,,13.4,5.17,,1.86,
+2021-09-30,40-59yrs,15.5,6.73,,1.66,,11.87,5.64,,1.64,
+2021-09-30,60-79yrs,18.87,1.33,,1.09,,14.01,2.61,,0.88,
+2021-09-30,80+,26.61,0,,0.95,,33,0.73,,1.12,
+2021-09-30,ALL,11.11,4.84,,1.47,,9.83,4.84,,1.43,
+2021-10-01,0-11yrs,6.12,0,,0,,6,4.15,,0,
+2021-10-01,12-17yrs,10.61,3.17,,1.05,,12.96,4.05,,0.95,
+2021-10-01,18-39yrs,15.72,5.67,,1.8,,13.45,5.06,,1.8,
+2021-10-01,40-59yrs,15.6,4.69,,1.78,,11.98,4.75,,1.62,
+2021-10-01,60-79yrs,15.87,4.03,,1.42,,14.14,3.18,,0.96,
+2021-10-01,80+,41.92,5.19,,1.27,,29.89,1.47,,1.05,
+2021-10-01,ALL,10.79,4.87,,1.61,,9.66,4.58,,1.42,
+2021-10-02,0-11yrs,7.58,0,,0,,6.22,3.36,,0,
+2021-10-02,12-17yrs,17.32,7.5,,0.74,,13.29,4.67,,0.9,
+2021-10-02,18-39yrs,15.21,7.46,,2.42,,13.55,5.14,,1.89,
+2021-10-02,40-59yrs,12.14,1.58,,2,,11.98,4.26,,1.66,
+2021-10-02,60-79yrs,12.18,5.43,,1.46,,14.59,3.41,,1,
+2021-10-02,80+,44.07,0,,2.22,,33.03,1.47,,1.2,
+2021-10-02,ALL,11.03,5.55,,1.92,,9.82,4.57,,1.48,
+2021-10-03,0-11yrs,5.33,0,,0,,5.93,2.55,,0,
+2021-10-03,12-17yrs,10.26,8.69,,0.74,,12.67,5.61,,0.81,
+2021-10-03,18-39yrs,10.24,4.26,,1.91,,12.82,4.88,,1.85,
+2021-10-03,40-59yrs,11.48,6.37,,2.03,,12.14,4.52,,1.72,
+2021-10-03,60-79yrs,10.97,1.37,,1.24,,14.08,3.23,,1.07,
+2021-10-03,80+,76.76,0,,1.59,,37.49,1.47,,1.29,
+2021-10-03,ALL,8.17,4.84,,1.68,,9.46,4.59,,1.5,
+2021-10-04,0-11yrs,5.61,0,,0,,5.73,2.55,,0,
+2021-10-04,12-17yrs,9.26,2.19,,0.74,,12.11,5.33,,0.79,
+2021-10-04,18-39yrs,10.17,3.02,,1.53,,12.48,4.66,,1.77,
+2021-10-04,40-59yrs,12.04,3.73,,1.36,,12.62,4.54,,1.69,
+2021-10-04,60-79yrs,15.56,0,,1.16,,14.21,2.3,,1.08,
+2021-10-04,80+,111.6,0,,1.74,,50.09,1.47,,1.32,
+2021-10-04,ALL,8.57,2.69,,1.34,,9.34,4.36,,1.47,
+2021-10-05,0-11yrs,4.71,0,,0,,5.81,1.71,,0,
+2021-10-05,12-17yrs,10.35,3.29,,1.48,,11.97,5.35,,0.83,
+2021-10-05,18-39yrs,7.52,4.05,,1.84,,12.09,4.72,,1.81,
+2021-10-05,40-59yrs,7.23,4.3,,1.14,,12.27,4.58,,1.65,
+2021-10-05,60-79yrs,10.47,0,,0.67,,13.68,2.3,,1.1,
+2021-10-05,80+,66.97,0,,0.79,,56.19,0.74,,1.29,
+2021-10-05,ALL,6.46,3.48,,1.24,,9.19,4.39,,1.48,
+2021-10-06,0-11yrs,4.55,0,,0,,5.85,0,,0,
+2021-10-06,12-17yrs,14.09,0,,0.29,,12.42,4.3,,0.78,
+2021-10-06,18-39yrs,10.36,1.28,,1.3,,11.87,4.38,,1.82,
+2021-10-06,40-59yrs,11.29,3.8,,1.9,,12.18,4.46,,1.7,
+2021-10-06,60-79yrs,19.19,1.4,,1.34,,14.73,1.94,,1.2,
+2021-10-06,80+,17.73,0,,0.16,,55.09,0.74,,1.25,
+2021-10-06,ALL,8.2,1.69,,1.36,,9.19,3.99,,1.52,
+2021-10-07,0-11yrs,6.12,0,,0,,5.72,0,,0,
+2021-10-07,12-17yrs,16.32,5.58,,0.44,,12.6,4.35,,0.78,
+2021-10-07,18-39yrs,12.37,3.37,,2.01,,11.66,4.16,,1.83,
+2021-10-07,40-59yrs,11.75,2.75,,1.7,,11.65,3.89,,1.7,
+2021-10-07,60-79yrs,22.77,2.82,,1.31,,15.29,2.15,,1.23,
+2021-10-07,80+,0,10.61,,1.11,,51.29,2.26,,1.27,
+2021-10-07,ALL,9.78,3.56,,1.58,,9,3.81,,1.53,
+2021-10-08,0-11yrs,5.16,9.47,,0,,5.58,1.35,,0,
+2021-10-08,12-17yrs,11.16,3.39,,1.17,,12.68,4.38,,0.8,
+2021-10-08,18-39yrs,10.14,5,,2.18,,10.86,4.06,,1.88,
+2021-10-08,40-59yrs,10.36,4.45,,2.02,,10.9,3.85,,1.74,
+2021-10-08,60-79yrs,25.72,2.85,,1.6,,16.69,1.98,,1.25,
+2021-10-08,80+,61.19,0,,0.95,,54.05,1.52,,1.22,
+2021-10-08,ALL,8.45,4.41,,1.84,,8.67,3.75,,1.57,
+2021-10-09,0-11yrs,7.24,0,,0,,5.53,1.35,,0,
+2021-10-09,12-17yrs,20.39,2.32,,0.44,,13.12,3.64,,0.76,
+2021-10-09,18-39yrs,14.46,4.29,,2.69,,10.75,3.61,,1.92,
+2021-10-09,40-59yrs,10.07,5.66,,1.89,,10.6,4.44,,1.72,
+2021-10-09,60-79yrs,21.2,2.89,,1.19,,17.98,1.62,,1.22,
+2021-10-09,80+,131.8,5.37,,1.11,,66.58,2.28,,1.06,
+2021-10-09,ALL,10.9,4.09,,1.82,,8.65,3.54,,1.55,
+2021-10-10,0-11yrs,5.05,10.98,,0,,5.49,2.92,,0,
+2021-10-10,12-17yrs,24.38,2.37,,1.16,,15.14,2.73,,0.82,
+2021-10-10,18-39yrs,10.68,5.45,,1.49,,10.81,3.78,,1.86,
+2021-10-10,40-59yrs,12.57,3.44,,1.73,,10.76,4.02,,1.68,
+2021-10-10,60-79yrs,14.48,0,,1.12,,18.48,1.42,,1.2,
+2021-10-10,80+,70.24,0,,0.79,,65.65,2.28,,0.95,
+2021-10-10,ALL,9.06,4.01,,1.4,,8.77,3.42,,1.51,
+2021-10-11,0-11yrs,4.32,0,,0,,5.31,2.92,,0,
+2021-10-11,12-17yrs,14.14,3.58,,0.43,,15.83,2.93,,0.77,
+2021-10-11,18-39yrs,7.73,3.57,,1.25,,10.47,3.86,,1.82,
+2021-10-11,40-59yrs,14.3,1.73,,1.32,,11.08,3.73,,1.67,
+2021-10-11,60-79yrs,19.41,2.92,,1.3,,19.03,1.84,,1.22,
+2021-10-11,80+,124.6,0,,0.63,,67.5,2.28,,0.79,
+2021-10-11,ALL,7.94,2.93,,1.19,,8.68,3.45,,1.49,
+2021-10-12,0-11yrs,4.26,0,,0,,5.24,2.92,,0,
+2021-10-12,12-17yrs,10.4,2.4,,0.14,,15.84,2.81,,0.58,
+2021-10-12,18-39yrs,6.85,1.94,,1.27,,10.37,3.56,,1.74,
+2021-10-12,40-59yrs,7.94,2.9,,1.38,,11.18,3.53,,1.71,
+2021-10-12,60-79yrs,11.88,1.47,,1.26,,19.24,2.05,,1.3,
+2021-10-12,80+,53.62,0,,1.42,,65.6,2.28,,0.88,
+2021-10-12,ALL,6.14,2.11,,1.24,,8.64,3.26,,1.49,
+2021-10-13,0-11yrs,3.81,0,,0,,5.14,2.92,,0,
+2021-10-13,12-17yrs,9.38,3.65,,1,,15.17,3.33,,0.68,
+2021-10-13,18-39yrs,5.74,1.4,,1.12,,9.71,3.57,,1.72,
+2021-10-13,40-59yrs,5.52,3.52,,1.13,,10.36,3.49,,1.6,
+2021-10-13,60-79yrs,7.78,0,,0.67,,17.61,1.85,,1.21,
+2021-10-13,80+,89.18,0,,0.95,,75.8,2.28,,0.99,
+2021-10-13,ALL,5.08,1.99,,0.99,,8.19,3.3,,1.44,
+2021-10-14,0-11yrs,3.31,0,,9.09,,4.74,2.92,,1.3,
+2021-10-14,12-17yrs,10.57,2.47,,0.14,,14.35,2.88,,0.64,
+2021-10-14,18-39yrs,8.27,1.14,,1.41,,9.12,3.26,,1.63,
+2021-10-14,40-59yrs,10.15,4.74,,1.47,,10.13,3.78,,1.56,
+2021-10-14,60-79yrs,15.71,2.99,,1.37,,16.6,1.87,,1.22,
+2021-10-14,80+,164.6,0,,2.05,,99.32,0.77,,1.13,
+2021-10-14,ALL,6.56,2.3,,1.38,,7.73,3.12,,1.41,
+2021-10-15,0-11yrs,4.43,0,,0,,4.63,1.57,,1.3,
+2021-10-15,12-17yrs,14,6.25,,0.85,,14.75,3.29,,0.59,
+2021-10-15,18-39yrs,11.9,1.73,,2.04,,9.38,2.79,,1.61,
+2021-10-15,40-59yrs,9.24,4.79,,1.56,,9.97,3.83,,1.5,
+2021-10-15,60-79yrs,19.45,1.51,,1.08,,15.7,1.68,,1.14,
+2021-10-15,80+,36.35,0,,1.42,,95.77,0.77,,1.2,
+2021-10-15,ALL,8.09,2.91,,1.53,,7.68,2.91,,1.36,
+2021-10-16,0-11yrs,4.88,0,,0,,4.29,1.57,,1.3,
+2021-10-16,12-17yrs,13.54,5.12,,0.85,,13.77,3.69,,0.65,
+2021-10-16,18-39yrs,10.31,2.05,,1.91,,8.78,2.47,,1.5,
+2021-10-16,40-59yrs,10.85,5.48,,1.74,,10.08,3.8,,1.48,
+2021-10-16,60-79yrs,17.46,3.04,,1.15,,15.17,1.7,,1.14,
+2021-10-16,80+,207.3,0,,0,,106.6,0,,1.04,
+2021-10-16,ALL,8.17,3.26,,1.48,,7.29,2.79,,1.32,
+2021-10-17,0-11yrs,4.21,0,,0,,4.17,0,,1.3,
+2021-10-17,12-17yrs,12.5,6.53,,0.85,,12.08,4.29,,0.61,
+2021-10-17,18-39yrs,9.16,2.97,,1.31,,8.57,2.11,,1.47,
+2021-10-17,40-59yrs,7.39,1.23,,1.49,,9.34,3.48,,1.44,
+2021-10-17,60-79yrs,10.99,3.07,,1.63,,14.67,2.14,,1.21,
+2021-10-17,80+,231.8,5.49,,2.05,,129.6,0.78,,1.22,
+2021-10-17,ALL,6.71,3.01,,1.46,,6.96,2.64,,1.32,
+2021-10-18,0-11yrs,4.21,0,,0,,4.16,0,,1.3,
+2021-10-18,12-17yrs,10.83,3.96,,0.14,,11.6,4.34,,0.57,
+2021-10-18,18-39yrs,6.53,2.4,,1.13,,8.39,1.95,,1.46,
+2021-10-18,40-59yrs,8.77,2.48,,1.4,,8.55,3.59,,1.45,
+2021-10-18,60-79yrs,15.45,1.54,,0.74,,14.1,1.95,,1.13,
+2021-10-18,80+,0,5.5,,0.63,,111.8,1.57,,1.22,
+2021-10-18,ALL,6.23,2.57,,1.02,,6.71,2.59,,1.3,
+2021-10-19,0-11yrs,3.98,0,,0,,4.12,0,,1.3,
+2021-10-19,12-17yrs,8.02,1.34,,0.28,,11.26,4.19,,0.59,
+2021-10-19,18-39yrs,6.33,1.82,,1.31,,8.32,1.93,,1.46,
+2021-10-19,40-59yrs,5.68,3.75,,1.12,,8.23,3.71,,1.42,
+2021-10-19,60-79yrs,8.16,4.64,,0.78,,13.57,2.4,,1.06,
+2021-10-19,80+,0,0,,0.31,,104.2,1.57,,1.06,
+2021-10-19,ALL,5.16,2.45,,0.99,,6.57,2.64,,1.26,
+2021-10-20,0-11yrs,3.2,0,,0,,4.03,0,,1.3,
+2021-10-20,12-17yrs,6.92,2.71,,0.56,,10.91,4.05,,0.52,
+2021-10-20,18-39yrs,8.34,2.46,,1.04,,8.69,2.08,,1.45,
+2021-10-20,40-59yrs,9.06,1.27,,1.02,,8.73,3.39,,1.4,
+2021-10-20,60-79yrs,9,0,,0.48,,13.75,2.4,,1.03,
+2021-10-20,80+,224.2,5.54,,0,,123.5,2.36,,0.92,
+2021-10-20,ALL,5.8,1.55,,0.79,,6.67,2.58,,1.24,
+2021-10-21,0-11yrs,4.15,17.23,,0,,4.15,2.46,,0,
+2021-10-21,12-17yrs,11.61,5.5,,0.7,,11.06,4.49,,0.6,
+2021-10-21,18-39yrs,8.52,1.56,,1.42,,8.73,2.14,,1.45,
+2021-10-21,40-59yrs,8.51,2.56,,1.76,,8.5,3.08,,1.44,
+2021-10-21,60-79yrs,12.12,1.58,,1,,13.23,2.2,,0.98,
+2021-10-21,80+,388.7,0,,0.47,,155.5,2.36,,0.7,
+2021-10-21,ALL,6.64,2.35,,1.31,,6.69,2.59,,1.23,
+2021-10-22,0-11yrs,4.93,0,,0,,4.22,2.46,,0,
+2021-10-22,12-17yrs,7.01,2.79,,0.97,,10.06,3.99,,0.62,
+2021-10-22,18-39yrs,8.46,4.41,,1.94,,8.24,2.52,,1.44,
+2021-10-22,40-59yrs,11.36,1.94,,1.79,,8.8,2.67,,1.47,
+2021-10-22,60-79yrs,15.32,1.59,,1.22,,12.64,2.21,,1,
+2021-10-22,80+,447.8,5.61,,0.47,,214.3,3.16,,0.56,
+2021-10-22,ALL,7.31,3.33,,1.56,,6.57,2.65,,1.23,
+2021-10-23,0-11yrs,3.98,0,,0,,4.09,2.46,,0,
+2021-10-23,12-17yrs,10,5.71,,0.55,,9.56,4.08,,0.58,
+2021-10-23,18-39yrs,7.9,2.88,,1.44,,7.89,2.64,,1.37,
+2021-10-23,40-59yrs,9.83,1.96,,1.29,,8.66,2.17,,1.41,
+2021-10-23,60-79yrs,6.2,1.61,,0.41,,11.03,2,,0.89,
+2021-10-23,80+,533.3,5.63,,0.47,,260.8,3.97,,0.63,
+2021-10-23,ALL,6.21,2.9,,1.02,,6.29,2.59,,1.16,
+2021-10-24,0-11yrs,4.31,0,,0,,4.11,2.46,,0,
+2021-10-24,12-17yrs,8.29,4.36,,0,,8.95,3.77,,0.46,
+2021-10-24,18-39yrs,6.3,3.24,,1.26,,7.48,2.68,,1.36,
+2021-10-24,40-59yrs,6.85,5.94,,1.45,,8.58,2.84,,1.4,
+2021-10-24,60+,11.69,0,,0.99,,11.69,0,,0.99,
+2021-10-24,ALL,5.64,3.58,,1.14,,6.14,2.68,,1.12,
+2022-03-11,0-4yrs,,,9.27,0,0,,,,,
+2022-03-11,12-17yrs,,,3.14,16.45,8.92,,,,,
+2022-03-11,18-39yrs,,,10.59,15.35,19.48,,,,,
+2022-03-11,40-59yrs,,,10.11,13.35,18.48,,,,,
+2022-03-11,5-11yrs,,,7.58,19.4,0,,,,,
+2022-03-11,60+,,,26.2,8.79,6.62,,,,,
+2022-03-11,ALL,,,9.58,14.5,13.76,,,,,
+2022-03-12,0-4yrs,,,6.5,0,0,,,,,
+2022-03-12,12-17yrs,,,3.14,14.93,17.53,,,,,
+2022-03-12,18-39yrs,,,10.45,14.1,23.05,,,,,
+2022-03-12,40-59yrs,,,7.42,11.63,15.49,,,,,
+2022-03-12,5-11yrs,,,7.76,11.92,0,,,,,
+2022-03-12,60+,,,29.08,11.18,7.39,,,,,
+2022-03-12,ALL,,,8.57,13.09,14.18,,,,,
+2022-03-13,0-4yrs,,,7.19,0,0,,,,,
+2022-03-13,12-17yrs,,,6.29,11.87,8.45,,,,,
+2022-03-13,18-39yrs,,,5.38,10.3,17.43,,,,,
+2022-03-13,40-59yrs,,,5.18,9.1,13.51,,,,,
+2022-03-13,5-11yrs,,,5.63,12.59,0,,,,,
+2022-03-13,60+,,,20.12,6.45,6.4,,,,,
+2022-03-13,ALL,,,6.5,9.92,11.56,,,,,
+2022-03-14,0-4yrs,,,6.78,0,0,,,,,
+2022-03-14,12-17yrs,,,3.77,10.08,11.22,,,,,
+2022-03-14,18-39yrs,,,5.08,5.69,12.23,,,,,
+2022-03-14,40-59yrs,,,4.5,6.39,10.1,,,,,
+2022-03-14,5-11yrs,,,5.22,12.24,0,,,,,
+2022-03-14,60+,,,12.87,4.25,4.09,,,,,
+2022-03-14,ALL,,,5.69,6.79,8.22,,,,,
+2022-03-15,0-4yrs,,,6.36,0,0,,,,,
+2022-03-15,12-17yrs,,,4.41,6.98,3.98,,,,,
+2022-03-15,18-39yrs,,,5.08,5.69,11.67,,,,,
+2022-03-15,40-59yrs,,,4.05,5.76,9.04,,,,,
+2022-03-15,5-11yrs,,,4.96,9.64,0,,,,,
+2022-03-15,60+,,,12.02,5.45,3.85,,,,,
+2022-03-15,ALL,,,5.45,6.15,7.53,,,,,
+2022-03-16,0-4yrs,,,7.33,0,0,,,,,
+2022-03-16,12-17yrs,,,2.52,11.74,8.67,,,,,
+2022-03-16,18-39yrs,,,9.1,12.33,22.71,,,,,
+2022-03-16,40-59yrs,,,6.31,13.93,17.89,,,,,
+2022-03-16,5-11yrs,,,5.66,14.1,0,,,,,
+2022-03-16,60+,,,27.96,9.38,7.83,,,,,
+2022-03-16,ALL,,,7.66,12.43,14.9,,,,,
+2022-03-17,0-4yrs,,,10.1,0,0,,,7.65,0,0
+2022-03-17,12-17yrs,,,12.6,12.21,9.39,,,5.12,12.04,9.74
+2022-03-17,18-39yrs,,,12.51,20.6,24.91,,,8.31,12.01,18.78
+2022-03-17,40-59yrs,,,8.34,19.23,17.56,,,6.56,11.34,14.58
+2022-03-17,5-11yrs,,,8.86,12.91,0,,,6.52,13.26,0
+2022-03-17,60+,,,29.92,11.8,8,,,22.6,8.19,6.31
+2022-03-17,ALL,,,10.96,17.46,15.46,,,7.77,11.48,12.23
+2022-03-18,0-4yrs,,,5.81,0,0,,,7.15,0,0
+2022-03-18,12-17yrs,,,6.93,9.37,10.08,,,5.67,11.03,9.9
+2022-03-18,18-39yrs,,,13.3,20.28,27.81,,,8.7,12.71,19.97
+2022-03-18,40-59yrs,,,10.15,14.12,19.32,,,6.56,11.45,14.7
+2022-03-18,5-11yrs,,,8.47,13.39,0,,,6.65,12.4,0
+2022-03-18,60+,,,38.47,12.69,9.11,,,24.35,8.74,6.67
+2022-03-18,ALL,,,10.22,15.77,17.23,,,7.86,11.66,12.73
+2022-03-19,0-4yrs,,,8.02,0,0,,,7.37,0,0
+2022-03-19,12-17yrs,,,8.84,9.42,6.2,,,6.48,10.24,8.28
+2022-03-19,18-39yrs,,,12.85,12.66,23.75,,,9.04,12.51,20.07
+2022-03-19,40-59yrs,,,9.26,10.69,15.22,,,6.83,11.32,14.66
+2022-03-19,5-11yrs,,,5.86,7.19,0,,,6.38,11.72,0
+2022-03-19,60+,,,28.25,7.91,7.99,,,24.23,8.28,6.75
+2022-03-19,ALL,,,9.59,10.74,14.36,,,8.01,11.32,12.75
+2022-03-20,0-4yrs,,,5.81,0,0,,,7.17,0,0
+2022-03-20,12-17yrs,,,9.48,8.89,10.24,,,6.94,9.81,8.54
+2022-03-20,18-39yrs,,,8.06,9.42,19.98,,,9.43,12.38,20.44
+2022-03-20,40-59yrs,,,7.45,9.49,15.89,,,7.15,11.37,15
+2022-03-20,5-11yrs,,,4.63,10.66,0,,,6.24,11.45,0
+2022-03-20,60+,,,23.61,7.4,7.46,,,24.73,8.41,6.9
+2022-03-20,ALL,,,7.17,9.22,13.43,,,8.11,11.22,13.02
+2022-03-21,0-4yrs,,,5.95,0,0,,,7.05,0,0
+2022-03-21,12-17yrs,,,2.53,5.84,4.22,,,6.76,9.21,7.54
+2022-03-21,18-39yrs,,,4.65,6.91,14.89,,,9.36,12.56,20.82
+2022-03-21,40-59yrs,,,5.65,5.71,10.09,,,7.32,11.28,15
+2022-03-21,5-11yrs,,,5.34,6.81,0,,,6.25,10.67,0
+2022-03-21,60+,,,33.18,3.27,4.5,,,27.63,8.27,6.96
+2022-03-21,ALL,,,6.28,6.02,8.97,,,8.19,11.11,13.13
+2022-03-22,0-4yrs,,,4.56,0,0,,,6.8,0,0
+2022-03-22,12-17yrs,,,5.05,6.58,10.05,,,6.85,9.15,8.41
+2022-03-22,18-39yrs,,,8.53,8.93,17.53,,,9.86,13.02,21.65
+2022-03-22,40-59yrs,,,7.45,7.97,10.49,,,7.8,11.59,15.21
+2022-03-22,5-11yrs,,,6.05,9.79,0,,,6.41,10.69,0
+2022-03-22,60+,,,37.25,5.69,5.64,,,31.23,8.31,7.22
+2022-03-22,ALL,,,7.58,8.04,10.36,,,8.49,11.38,13.53
+2022-03-23,0-4yrs,,,7.47,0,0,,,6.82,0,0
+2022-03-23,12-17yrs,,,6.32,10.56,16.5,,,7.39,8.98,9.53
+2022-03-23,18-39yrs,,,10.25,11.95,22.97,,,10.02,12.96,21.69
+2022-03-23,40-59yrs,,,7.91,12.65,19.8,,,8.03,11.41,15.48
+2022-03-23,5-11yrs,,,6.2,12.19,0,,,6.49,10.42,0
+2022-03-23,60+,,,24.03,7.77,8.84,,,30.67,8.08,7.36
+2022-03-23,ALL,,,8.41,11.46,16.11,,,8.6,11.24,13.7
+2022-03-24,0-4yrs,,,8.02,0,0,,,6.52,0,0
+2022-03-24,12-17yrs,,,6.32,9.41,15.46,,,6.5,8.58,10.39
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+2022-03-24,ALL,,,9.83,13.41,19.94,,,8.44,10.67,14.34
+2022-03-25,0-4yrs,,,9.13,0,0,,,6.99,0,0
+2022-03-25,12-17yrs,,,6.32,14.15,11.23,,,6.41,9.26,10.56
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+2022-03-26,0-4yrs,,,10.37,0,0,,,7.33,0,0
+2022-03-26,12-17yrs,,,5.06,14.2,14.14,,,5.87,9.95,11.69
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+2022-04-23,60+,,,117.1,22.21,32.85,,,75.99,17.82,23.56
+2022-04-23,ALL,,,17.1,14.88,33.54,,,12.76,12.35,25.37
+2022-04-24,0-4yrs,,,15.35,0,0,,,11.34,0,0
+2022-04-24,12-17yrs,,,9.49,10.32,15.87,,,8.41,8.1,9.52
+2022-04-24,18-39yrs,,,20.37,16.97,38.57,,,14.74,12.79,29.75
+2022-04-24,40-59yrs,,,17.78,19.53,38.02,,,13.11,15.5,29.32
+2022-04-24,5-11yrs,,,8.62,13.18,0,,,7.51,11.5,85.54
+2022-04-24,60+,,,116.1,21.71,34.35,,,82.31,18.77,25.47
+2022-04-24,ALL,,,18.34,16.95,36.18,,,13.72,13.43,27.44
+2022-04-25,0-4yrs,,,7.05,0,0,,,10.73,0,0
+2022-04-25,12-17yrs,,,5.06,4.54,7.3,,,8.32,7.78,9.85
+2022-04-25,18-39yrs,,,8.69,7.99,17.78,,,14.63,12.68,29.41
+2022-04-25,40-59yrs,,,7.98,11.18,18.45,,,12.79,15.7,28.91
+2022-04-25,5-11yrs,,,4.39,6.23,0,,,7.28,10.97,85.54
+2022-04-25,60+,,,85.7,12.67,17,,,88.94,18.74,25.29
+2022-04-25,ALL,,,9.47,8.71,17.43,,,13.64,13.33,27.15
+2022-04-26,0-4yrs,,,11.2,0,0,,,11.66,0,0
+2022-04-26,12-17yrs,,,4.43,2.35,7.9,,,8.05,7.69,10.53
+2022-04-26,18-39yrs,,,8.69,5.98,14.6,,,14.9,12.87,30.09
+2022-04-26,40-59yrs,,,9.8,6.38,15.02,,,13.7,15.84,29.59
+2022-04-26,5-11yrs,,,4.85,7.18,0,,,7.39,11.72,85.54
+2022-04-26,60+,,,63.08,11.97,17.06,,,92.59,19.04,26.36
+2022-04-26,ALL,,,10.17,6.39,15.57,,,14.23,13.54,27.98
+2022-04-27,0-4yrs,,,9.96,0,0,,,12.62,0,0
+2022-04-27,12-17yrs,,,8.22,6.27,7.86,,,8.77,8.1,11.29
+2022-04-27,18-39yrs,,,12.81,11.51,25.76,,,16.22,13.7,32.48
+2022-04-27,40-59yrs,,,11.17,13.43,27.9,,,14.78,16.96,32.38
+2022-04-27,5-11yrs,,,4.55,8.36,0,,,7.76,12.5,85.54
+2022-04-27,60+,,,79.19,20.73,27.88,,,100.2,21.21,29.17
+2022-04-27,ALL,,,11.7,12.08,26.87,,,15.35,14.53,30.63
+2022-04-28,0-4yrs,,,9.68,0,0,,,11.46,0,0
+2022-04-28,12-17yrs,,,8.21,6.44,7.23,,,8.31,7.26,10.47
+2022-04-28,18-39yrs,,,13.76,14.43,32.17,,,14.37,12.85,30.04
+2022-04-28,40-59yrs,,,14.37,14.86,32.45,,,13.87,15.41,30.28
+2022-04-28,5-11yrs,,,8.19,10.97,0,,,7.17,11.24,85.54
+2022-04-28,60+,,,90.66,22.24,33.07,,,93.76,20.33,28.17
+2022-04-28,ALL,,,13.63,14.07,32.05,,,14.07,13.47,28.92
+2022-04-29,0-4yrs,,,8.99,0,0,,,10.73,0,0
+2022-04-29,12-17yrs,,,4.43,4.56,7.78,,,7.23,6.26,9.81
+2022-04-29,18-39yrs,,,9.97,10.22,25.88,,,13.05,11.42,27.11
+2022-04-29,40-59yrs,,,10.27,11.3,23.65,,,12.83,13.56,27.15
+2022-04-29,5-11yrs,,,4.56,9.76,0,,,6.25,9.88,85.54
+2022-04-29,60+,,,65.27,24.68,25.24,,,88.16,19.46,26.78
+2022-04-29,ALL,,,9.93,11.35,24.51,,,12.91,12.06,26.59
+2022-04-30,0-4yrs,,,9.82,0,0,,,10.29,0,0
+2022-04-30,12-17yrs,,,4.42,5.03,7.77,,,6.32,5.64,8.82
+2022-04-30,18-39yrs,,,11.24,11.48,25.56,,,12.22,11.23,25.76
+2022-04-30,40-59yrs,,,10.73,13.96,23.33,,,11.73,12.95,25.55
+2022-04-30,5-11yrs,,,6.38,8.56,0,,,5.93,9.18,0
+2022-04-30,60+,,,81.24,15.37,25.27,,,83.03,18.48,25.7
+2022-04-30,ALL,,,11.49,11.43,24.34,,,12.1,11.57,25.28
+2022-05-01,0-4yrs,,,8.71,0,0,,,9.34,0,0
+2022-05-01,12-17yrs,,,5.68,5.51,9.52,,,5.78,4.96,7.91
+2022-05-01,18-39yrs,,,9.82,8.41,20.13,,,10.71,10,23.13
+2022-05-01,40-59yrs,,,6.62,10.07,21.06,,,10.13,11.6,23.12
+2022-05-01,5-11yrs,,,7,4.98,0,,,5.7,8.01,0
+2022-05-01,60+,,,68.03,15.94,19.41,,,76.17,17.66,23.56
+2022-05-01,ALL,,,9.98,9,19.88,,,10.91,10.43,22.95
+2022-05-02,0-4yrs,,,6.92,0,0,,,9.33,0,0
+2022-05-02,12-17yrs,,,3.79,2.36,5.35,,,5.6,4.65,7.63
+2022-05-02,18-39yrs,,,4.59,3.93,10.51,,,10.13,9.42,22.09
+2022-05-02,40-59yrs,,,6.62,5.24,10.55,,,9.94,10.75,21.99
+2022-05-02,5-11yrs,,,3.05,2.37,0,,,5.51,7.45,0
+2022-05-02,60+,,,29.63,11,12.97,,,68.16,17.42,22.99
+2022-05-02,ALL,,,5.94,4.72,11.39,,,10.41,9.86,22.09
+2022-05-03,0-4yrs,,,8.3,0,0,,,8.91,0,0
+2022-05-03,12-17yrs,,,3.15,3,1.19,,,5.41,4.74,6.67
+2022-05-03,18-39yrs,,,6.81,5.09,12.4,,,9.86,9.3,21.77
+2022-05-03,40-59yrs,,,6.39,4.99,12.99,,,9.45,10.55,21.7
+2022-05-03,5-11yrs,,,2.59,3.08,0,,,5.19,6.87,0
+2022-05-03,60+,,,52.84,10.65,14.4,,,66.69,17.23,22.61
+2022-05-03,ALL,,,7.39,5.24,13.13,,,10.01,9.7,21.74
+2022-05-04,0-4yrs,,,9.41,0,0,,,8.83,0,0
+2022-05-04,12-17yrs,,,5.68,3.63,4.14,,,5.05,4.36,6.14
+2022-05-04,18-39yrs,,,9.67,8.22,22.52,,,9.41,8.83,21.31
+2022-05-04,40-59yrs,,,9.59,10.91,21,,,9.23,10.19,20.72
+2022-05-04,5-11yrs,,,5.79,5.21,0,,,5.37,6.42,0
+2022-05-04,60+,,,68.3,16.56,24.47,,,65.14,16.63,22.12
+2022-05-04,ALL,,,10.29,8.97,22.4,,,9.81,9.25,21.1
+2022-05-05,0-4yrs,,,8.02,0,0,,,8.6,0,0
+2022-05-05,12-17yrs,,,5.68,5.05,8.25,,,4.69,4.16,6.29
+2022-05-05,18-39yrs,,,9.68,8.77,22.71,,,8.83,8.02,19.96
+2022-05-05,40-59yrs,,,8,10.09,23.87,,,8.32,9.51,19.49
+2022-05-05,5-11yrs,,,4.27,6.39,0,,,4.81,5.76,0
+2022-05-05,60+,,,107.2,17.9,26.68,,,67.5,16.01,21.21
+2022-05-05,ALL,,,10.52,9.43,24.33,,,9.36,8.59,20
+2022-05-06,0-4yrs,,,6.78,0,0,,,8.28,0,0
+2022-05-06,12-17yrs,,,8.83,4.43,5.88,,,5.32,4.14,6.01
+2022-05-06,18-39yrs,,,10.16,8.78,22.33,,,8.85,7.81,19.45
+2022-05-06,40-59yrs,,,7.77,11.01,21.38,,,7.96,9.47,19.17
+2022-05-06,5-11yrs,,,3.36,5.67,0,,,4.63,5.18,0
+2022-05-06,60+,,,77.03,15.18,21.96,,,69.18,14.66,20.74
+2022-05-06,ALL,,,9.29,9.21,21.5,,,9.27,8.29,19.57
+2022-05-07,0-4yrs,,,7.47,0,0,,,7.94,0,0
+2022-05-07,12-17yrs,,,3.78,3.32,7.03,,,5.23,3.9,5.91
+2022-05-07,18-39yrs,,,7.46,8.18,19.43,,,8.31,7.34,18.58
+2022-05-07,40-59yrs,,,8.46,8.68,18.98,,,7.64,8.71,18.55
+2022-05-07,5-11yrs,,,5.04,5.43,0,,,4.44,4.73,0
+2022-05-07,60+,,,59.94,15.78,19.28,,,66.14,14.72,19.88
+2022-05-07,ALL,,,8.51,8.27,18.94,,,8.85,7.83,18.8
+2022-05-08,0-4yrs,,,6.36,0,0,,,7.61,0,0
+2022-05-08,12-17yrs,,,5.67,2.69,4.09,,,5.23,3.5,5.13
+2022-05-08,18-39yrs,,,8.42,6.67,17.85,,,8.11,7.09,18.25
+2022-05-08,40-59yrs,,,8,8.11,16.05,,,7.83,8.43,17.83
+2022-05-08,5-11yrs,,,3.98,5.65,0,,,4.01,4.83,0
+2022-05-08,60+,,,58.99,12.82,19.58,,,64.85,14.27,19.91
+2022-05-08,ALL,,,8.18,7.1,17.67,,,8.59,7.56,18.48
+2022-05-09,0-4yrs,,,7.61,0,0,,,7.71,0,0
+2022-05-09,12-17yrs,,,2.52,3.33,1.75,,,5.04,3.64,4.62
+2022-05-09,18-39yrs,,,5.08,3.38,9.26,,,8.18,7.01,18.07
+2022-05-09,40-59yrs,,,3.2,4.85,9.6,,,7.34,8.38,17.7
+2022-05-09,5-11yrs,,,3.06,3.76,0,,,4.01,5.03,0
+2022-05-09,60+,,,48.76,10.6,12.96,,,67.58,14.21,19.9
+2022-05-09,ALL,,,6.21,4.59,10.68,,,8.63,7.54,18.38
+2022-05-10,0-4yrs,,,6.09,0,0,,,7.39,0,0
+2022-05-10,12-17yrs,,,1.89,2.22,2.91,,,4.86,3.52,4.86
+2022-05-10,18-39yrs,,,3.18,4.34,8.11,,,7.66,6.91,17.46
+2022-05-10,40-59yrs,,,5.94,4.26,7.82,,,7.28,8.27,16.96
+2022-05-10,5-11yrs,,,4.44,3.29,0,,,4.28,5.06,0
+2022-05-10,60+,,,45.6,8.38,9.75,,,66.55,13.89,19.24
+2022-05-10,ALL,,,5.99,4.4,8.56,,,8.43,7.42,17.73
+2022-05-11,0-4yrs,,,9.13,0,0,,,7.35,0,0
+2022-05-11,12-17yrs,,,3.15,4.6,3.49,,,4.5,3.66,4.77
+2022-05-11,18-39yrs,,,6.68,7.33,18.98,,,7.24,6.78,16.95
+2022-05-11,40-59yrs,,,9.37,8.87,19.22,,,7.25,7.98,16.7
+2022-05-11,5-11yrs,,,3.06,5.64,0,,,3.89,5.12,0
+2022-05-11,60+,,,55.45,9.52,16.52,,,64.71,12.88,18.1
+2022-05-11,ALL,,,8.23,7.45,17.7,,,8.13,7.21,17.05
+2022-05-12,0-4yrs,,,8.3,0,0,,,7.39,0,0
+2022-05-12,12-17yrs,,,5.67,4.92,6.39,,,4.5,3.64,4.51
+2022-05-12,18-39yrs,,,7.48,7.08,19.75,,,6.92,6.54,16.53
+2022-05-12,40-59yrs,,,10.06,9.88,20.63,,,7.54,7.95,16.24
+2022-05-12,5-11yrs,,,4.45,8.21,0,,,3.91,5.38,0
+2022-05-12,60+,,,46.19,14.77,18.39,,,55.99,12.44,16.92
+2022-05-12,ALL,,,8.49,8.46,19.16,,,7.84,7.07,16.32
+2022-05-13,0-4yrs,,,6.78,0,0,,,7.39,0,0
+2022-05-13,12-17yrs,,,5.04,3.33,5.22,,,3.96,3.49,4.41
+2022-05-13,18-39yrs,,,7.96,7.79,18.64,,,6.61,6.4,16
+2022-05-13,40-59yrs,,,7.32,8.21,16.25,,,7.48,7.55,15.51
+2022-05-13,5-11yrs,,,5.22,4.69,0,,,4.18,5.24,0
+2022-05-13,60+,,,46.39,14.43,18.04,,,51.62,12.33,16.36
+2022-05-13,ALL,,,7.9,7.77,17.34,,,7.64,6.86,15.72
+2022-05-14,0-4yrs,,,5.39,0,0,,,7.09,0,0
+2022-05-14,12-17yrs,,,3.15,3.65,3.47,,,3.87,3.53,3.9
+2022-05-14,18-39yrs,,,5.58,6.07,15.03,,,6.34,6.09,15.37
+2022-05-14,40-59yrs,,,7.1,4.78,14.43,,,7.28,6.99,14.86
+2022-05-14,5-11yrs,,,2.3,5.39,0,,,3.79,5.23,0
+2022-05-14,60+,,,47.78,9.01,14.97,,,49.88,11.36,15.74
+2022-05-14,ALL,,,6.15,5.7,14.54,,,7.31,6.5,15.09
+2022-05-15,0-4yrs,,,6.64,0,0,,,7.13,0,0
+2022-05-15,12-17yrs,,,6.29,3.34,5.19,,,3.96,3.63,4.06
+2022-05-15,18-39yrs,,,6.53,5.57,13.93,,,6.07,5.94,14.81
+2022-05-15,40-59yrs,,,6.64,6.54,12.8,,,7.09,6.77,14.39
+2022-05-15,5-11yrs,,,2.46,4.44,0,,,3.57,5.06,0
+2022-05-15,60+,,,33.54,11.28,16.26,,,46.24,11.14,15.27
+2022-05-15,ALL,,,6.42,6.05,14.31,,,7.06,6.35,14.61
+2022-05-16,0-4yrs,,,6.36,0,0,,,6.96,0,0
+2022-05-16,12-17yrs,,,0.63,1.59,2.31,,,3.69,3.38,4.14
+2022-05-16,18-39yrs,,,5.26,3.09,8.45,,,6.1,5.9,14.7
+2022-05-16,40-59yrs,,,2.98,4.19,9.18,,,7.06,6.68,14.33
+2022-05-16,5-11yrs,,,1.85,3.04,0,,,3.4,4.96,0
+2022-05-16,60+,,,33.73,9.02,11.46,,,44.1,10.92,15.06
+2022-05-16,ALL,,,4.96,3.83,9.74,,,6.88,6.24,14.48
+2022-05-17,0-4yrs,,,3.87,0,0,,,6.64,0,0
+2022-05-17,12-17yrs,,,2.51,2.07,4.03,,,3.78,3.36,4.3
+2022-05-17,18-39yrs,,,3.99,3.7,7.72,,,6.21,5.8,14.64
+2022-05-17,40-59yrs,,,2.75,4.28,8.03,,,6.6,6.68,14.36
+2022-05-17,5-11yrs,,,2.77,2.57,0,,,3.16,4.85,0
+2022-05-17,60+,,,58.29,6.96,10.2,,,45.91,10.71,15.12
+2022-05-17,ALL,,,5.04,3.89,8.73,,,6.74,6.16,14.5
+2022-05-18,0-4yrs,,,6.5,0,0,,,6.26,0,0
+2022-05-18,12-17yrs,,,4.4,4.3,6.33,,,3.96,3.31,4.71
+2022-05-18,18-39yrs,,,6.38,5.12,16.52,,,6.17,5.49,14.29
+2022-05-18,40-59yrs,,,7.33,5.71,15.92,,,6.31,6.23,13.89
+2022-05-18,5-11yrs,,,3.23,7,0,,,3.18,5.05,0
+2022-05-18,60+,,,66.06,8.3,13.52,,,47.43,10.54,14.69
+2022-05-18,ALL,,,7.51,5.68,14.88,,,6.64,5.91,14.1
+2022-05-19,0-4yrs,,,5.95,0,0,,,5.93,0,0
+2022-05-19,12-17yrs,,,0,2.71,5.17,,,3.15,3,4.53
+2022-05-19,18-39yrs,,,6.86,5.83,13.23,,,6.08,5.31,13.36
+2022-05-19,40-59yrs,,,7.56,6.38,15.08,,,5.95,5.73,13.1
+2022-05-19,5-11yrs,,,2.62,3.97,0,,,2.92,4.44,0
+2022-05-19,60+,,,44.2,10.01,15.11,,,47.14,9.86,14.22
+2022-05-19,ALL,,,6.43,5.85,14.37,,,6.34,5.54,13.42
+2022-05-20,0-4yrs,,,4.7,0,0,,,5.63,0,0
+2022-05-20,12-17yrs,,,3.77,2.39,3.44,,,2.96,2.86,4.28
+2022-05-20,18-39yrs,,,3.83,5.47,13.59,,,5.49,4.98,12.64
+2022-05-20,40-59yrs,,,4.81,6.55,13.22,,,5.6,5.49,12.67
+2022-05-20,5-11yrs,,,1.69,3.97,0,,,2.42,4.34,0
+2022-05-20,60+,,,44.48,10.98,12.35,,,46.87,9.37,13.41
+2022-05-20,ALL,,,4.93,5.81,12.74,,,5.92,5.26,12.76
+2022-05-21,0-4yrs,,,4.98,0,0,,,5.57,0,0
+2022-05-21,12-17yrs,,,3.77,1.75,2.87,,,3.05,2.59,4.19
+2022-05-21,18-39yrs,,,4.63,4.21,11.86,,,5.35,4.71,12.19
+2022-05-21,40-59yrs,,,6.19,5.55,10.27,,,5.47,5.6,12.07
+2022-05-21,5-11yrs,,,1.39,2.56,0,,,2.29,3.94,0
+2022-05-21,60+,,,42.08,10.8,11.23,,,46.05,9.62,12.88
+2022-05-21,ALL,,,5.27,4.8,10.9,,,5.79,5.13,12.24
+2022-05-22,0-4yrs,,,4.29,0,0,,,5.24,0,0
+2022-05-22,12-17yrs,,,1.88,2.07,0.57,,,2.42,2.41,3.53
+2022-05-22,18-39yrs,,,3.04,3.85,9,,,4.86,4.47,11.48
+2022-05-22,40-59yrs,,,6.42,4.79,8.81,,,5.43,5.35,11.5
+2022-05-22,5-11yrs,,,2.93,1.63,0,,,2.35,3.53,0
+2022-05-22,60+,,,43.45,8.15,10.21,,,47.47,9.17,12.01
+2022-05-22,ALL,,,5.05,4.13,9.23,,,5.6,4.86,11.51
+2022-05-23,0-4yrs,,,3.87,0,0,,,4.88,0,0
+2022-05-23,12-17yrs,,,1.25,0.48,1.72,,,2.51,2.25,3.45
+2022-05-23,18-39yrs,,,2.56,1.98,4.26,,,4.47,4.31,10.88
+2022-05-23,40-59yrs,,,2.29,2.19,6.26,,,5.34,5.06,11.08
+2022-05-23,5-11yrs,,,1.7,3.26,0,,,2.33,3.57,0
+2022-05-23,60+,,,26.18,5.12,7.36,,,46.39,8.62,11.43
+2022-05-23,ALL,,,3.29,2.3,6.07,,,5.36,4.64,10.99
+2022-05-24,0-4yrs,,,2.77,0,0,,,4.72,0,0
+2022-05-24,12-17yrs,,,1.25,0.96,0.57,,,2.33,2.09,2.95
+2022-05-24,18-39yrs,,,2.24,2.03,5.1,,,4.22,4.07,10.51
+2022-05-24,40-59yrs,,,2.75,1.85,6.08,,,5.34,4.72,10.81
+2022-05-24,5-11yrs,,,1.23,1.86,0,,,2.11,3.46,0
+2022-05-24,60+,,,26.36,6.08,5.53,,,41.83,8.49,10.76
+2022-05-24,ALL,,,2.88,2.28,5.47,,,5.05,4.41,10.52
+2022-05-25,0-4yrs,,,4.01,0,0,,,4.37,0,0
+2022-05-25,12-17yrs,,,0,1.6,0.57,,,1.7,1.71,2.13
+2022-05-25,18-39yrs,,,2.4,2.79,6.24,,,3.65,3.74,9.04
+2022-05-25,40-59yrs,,,4.13,3.45,5.15,,,4.88,4.39,9.27
+2022-05-25,5-11yrs,,,1.39,0.93,0,,,1.85,2.6,0
+2022-05-25,60+,,,24.04,4.94,7.42,,,35.83,8.01,9.89
+2022-05-25,ALL,,,3.37,2.87,6.24,,,4.46,4.01,9.29
+2022-05-26,0-4yrs,,,4.29,0,0,,,4.13,0,0
+2022-05-26,12-17yrs,,,1.25,1.44,5.14,,,1.88,1.53,2.13
+2022-05-26,18-39yrs,,,4.96,4.21,14.03,,,3.38,3.51,9.15
+2022-05-26,40-59yrs,,,2.98,4.71,11.98,,,4.22,4.16,8.82
+2022-05-26,5-11yrs,,,0.93,6.05,0,,,1.61,2.89,0
+2022-05-26,60+,,,44.69,4.95,9.45,,,35.9,7.29,9.08
+2022-05-26,ALL,,,4.42,4.22,11.33,,,4.17,3.77,8.85
+2022-05-27,0-4yrs,,,3.46,0,0,,,3.95,0,0
+2022-05-27,12-17yrs,,,3.13,2.39,3.42,,,1.79,1.53,2.12
+2022-05-27,18-39yrs,,,4.64,3.1,10.18,,,3.5,3.17,8.67
+2022-05-27,40-59yrs,,,4.36,3.96,9.16,,,4.16,3.79,8.24
+2022-05-27,5-11yrs,,,2.01,2.79,0,,,1.65,2.73,0
+2022-05-27,60+,,,37.1,6.1,10.91,,,34.84,6.59,8.87
+2022-05-27,ALL,,,4.5,3.52,9.99,,,4.11,3.45,8.46
+2022-05-28,0-4yrs,,,2.9,0,0,,,3.66,0,0
+2022-05-28,12-17yrs,,,3.13,1.76,6.26,,,1.7,1.53,2.61
+2022-05-28,18-39yrs,,,4.33,3.76,10.33,,,3.45,3.1,8.45
+2022-05-28,40-59yrs,,,3.67,4.04,8.54,,,3.8,3.57,8
+2022-05-28,5-11yrs,,,2.62,3.49,0,,,1.83,2.86,0
+2022-05-28,60+,,,21.84,8.39,8.57,,,31.95,6.25,8.49
+2022-05-28,ALL,,,3.86,4.05,8.97,,,3.91,3.34,8.19
+2022-05-29,0-4yrs,,,2.35,0,0,,,3.38,0,0
+2022-05-29,12-17yrs,,,1.88,1.6,1.14,,,1.7,1.46,2.69
+2022-05-29,18-39yrs,,,3.21,3,8.25,,,3.48,2.98,8.34
+2022-05-29,40-59yrs,,,4.82,4.8,7.57,,,3.57,3.57,7.82
+2022-05-29,5-11yrs,,,1.7,2.55,0,,,1.65,2.99,0
+2022-05-29,60+,,,29.71,4.2,8.43,,,29.99,5.68,8.24
+2022-05-29,ALL,,,3.56,3.36,7.94,,,3.7,3.23,8
+2022-05-30,0-4yrs,,,2.35,0,0,,,3.16,0,0
+2022-05-30,12-17yrs,,,1.88,0.8,2.84,,,1.79,1.51,2.85
+2022-05-30,18-39yrs,,,2.08,1.22,3.94,,,3.41,2.87,8.3
+2022-05-30,40-59yrs,,,2.53,2.86,4.97,,,3.61,3.67,7.64
+2022-05-30,5-11yrs,,,0.46,2.32,0,,,1.48,2.86,0
+2022-05-30,60+,,,19.44,3.82,5.31,,,29.03,5.5,7.95
+2022-05-30,ALL,,,2.33,1.96,4.79,,,3.56,3.18,7.82
+2022-05-31,0-4yrs,,,3.46,0,0,,,3.26,0,0
+2022-05-31,12-17yrs,,,1.25,1.12,1.14,,,1.79,1.53,2.93
+2022-05-31,18-39yrs,,,2.25,2.29,5.4,,,3.41,2.91,8.34
+2022-05-31,40-59yrs,,,3.45,2.7,3.7,,,3.71,3.79,7.3
+2022-05-31,5-11yrs,,,1.54,0.93,0,,,1.52,2.72,0
+2022-05-31,60+,,,20.9,4.2,5.31,,,28.25,5.23,7.91
+2022-05-31,ALL,,,3.08,2.32,4.73,,,3.59,3.19,7.71
+2022-06-01,0-4yrs,,,5.26,0,0,,,3.44,0,0
+2022-06-01,12-17yrs,,,4.38,1.76,1.13,,,2.41,1.55,3.01
+2022-06-01,18-39yrs,,,5.62,4.47,10.73,,,3.87,3.15,8.98
+2022-06-01,40-59yrs,,,4.37,4.47,7.96,,,3.74,3.93,7.7
+2022-06-01,5-11yrs,,,2.01,2.78,409.8,,,1.61,2.99,58.54
+2022-06-01,60+,,,27.67,5.55,7.71,,,28.76,5.32,7.96
+2022-06-01,ALL,,,4.99,4.08,8.44,,,3.82,3.36,8.03
+2022-06-02,0-4yrs,,,4.29,0,0,,,3.44,0,0
+2022-06-02,12-17yrs,,,2.5,2.4,2.27,,,2.59,1.69,2.6
+2022-06-02,18-39yrs,,,4.01,3.51,8.4,,,3.73,3.05,8.18
+2022-06-02,40-59yrs,,,4.14,2.95,9.5,,,3.91,3.68,7.34
+2022-06-02,5-11yrs,,,1.24,2.09,0,,,1.65,2.42,58.54
+2022-06-02,60+,,,35.9,7.46,9.84,,,27.51,5.67,8.01
+2022-06-02,ALL,,,4.24,3.53,9.17,,,3.79,3.26,7.72
+2022-06-03,0-4yrs,,,2.9,0,0,,,3.36,0,0
+2022-06-03,12-17yrs,,,3.13,1.12,3.4,,,2.59,1.51,2.6
+2022-06-03,18-39yrs,,,4.66,3.61,8.29,,,3.74,3.12,7.91
+2022-06-03,40-59yrs,,,2.53,3.63,7.43,,,3.64,3.64,7.1
+2022-06-03,5-11yrs,,,2.47,2.32,0,,,1.72,2.35,58.54
+2022-06-03,60+,,,25.45,3.45,7.81,,,25.84,5.3,7.57
+2022-06-03,ALL,,,3.79,3.15,7.71,,,3.69,3.21,7.39
+2022-06-04,0-4yrs,,,4.29,0,0,,,3.56,0,0
+2022-06-04,12-17yrs,,,2.5,1.6,3.39,,,2.5,1.49,2.19
+2022-06-04,18-39yrs,,,3.7,3.35,7.56,,,3.65,3.06,7.51
+2022-06-04,40-59yrs,,,2.99,2.87,7.38,,,3.55,3.47,6.93
+2022-06-04,5-11yrs,,,2.01,2.55,0,,,1.63,2.22,58.54
+2022-06-04,60+,,,17.49,8.06,6.25,,,25.22,5.25,7.24
+2022-06-04,ALL,,,3.65,3.45,6.88,,,3.66,3.12,7.09
+2022-06-05,0-4yrs,,,1.94,0,0,,,3.5,0,0
+2022-06-05,12-17yrs,,,0.62,1.6,1.69,,,2.32,1.49,2.27
+2022-06-05,18-39yrs,,,2.25,2.95,7.04,,,3.51,3.06,7.34
+2022-06-05,40-59yrs,,,2.99,2.7,6.11,,,3.29,3.17,6.72
+2022-06-05,5-11yrs,,,0.77,0.93,0,,,1.5,1.99,58.54
+2022-06-05,60+,,,17.55,4.41,5.57,,,23.49,5.28,6.83
+2022-06-05,ALL,,,2.26,2.68,6.03,,,3.48,3.02,6.82
+2022-06-06,0-4yrs,,,2.77,0,0,,,3.56,0,0
+2022-06-06,12-17yrs,,,1.87,0.64,1.13,,,2.32,1.46,2.02
+2022-06-06,18-39yrs,,,1.93,1.47,3.63,,,3.49,3.09,7.29
+2022-06-06,40-59yrs,,,2.3,1.69,3.65,,,3.25,3,6.53
+2022-06-06,5-11yrs,,,1.39,0.69,0,,,1.63,1.76,58.54
+2022-06-06,60+,,,19.04,2.3,4.8,,,23.43,5.06,6.76
+2022-06-06,ALL,,,2.56,1.44,4.04,,,3.51,2.95,6.71
+2022-06-07,0-4yrs,,,3.6,0,0,,,3.58,0,0
+2022-06-07,12-17yrs,,,0.62,1.12,4.52,,,2.23,1.46,2.5
+2022-06-07,18-39yrs,,,2.41,1.27,3.94,,,3.51,2.95,7.08
+2022-06-07,40-59yrs,,,2.99,2.19,3.56,,,3.19,2.93,6.51
+2022-06-07,5-11yrs,,,1.7,1.85,0,,,1.66,1.89,58.54
+2022-06-07,60+,,,16.46,2.69,4.15,,,22.79,4.85,6.59
+2022-06-07,ALL,,,2.93,1.69,3.92,,,3.49,2.86,6.6
+2022-06-08,0-4yrs,,,5.12,0,0,,,3.56,0,0
+2022-06-08,12-17yrs,,,2.5,2.4,3.39,,,1.96,1.55,2.83
+2022-06-08,18-39yrs,,,4.34,3.1,9.37,,,3.33,2.75,6.89
+2022-06-08,40-59yrs,,,4.37,3.97,8.92,,,3.19,2.86,6.65
+2022-06-08,5-11yrs,,,2.17,5.09,0,,,1.68,2.22,0
+2022-06-08,60+,,,19.26,5.18,7.02,,,21.59,4.79,6.49
+2022-06-08,ALL,,,4.33,3.64,8.14,,,3.39,2.8,6.56
+2022-06-09,0-4yrs,,,2.21,0,0,,,3.26,0,0
+2022-06-09,12-17yrs,,,2.5,1.12,1.69,,,1.96,1.37,2.74
+2022-06-09,18-39yrs,,,4.02,3,7.71,,,3.33,2.68,6.79
+2022-06-09,40-59yrs,,,1.84,3.71,7.73,,,2.86,2.97,6.4
+2022-06-09,5-11yrs,,,0.46,1.62,0,,,1.57,2.15,0
+2022-06-09,60+,,,42.9,4.61,7.19,,,22.59,4.39,6.11
+2022-06-09,ALL,,,3.27,2.98,7.36,,,3.26,2.72,6.3
+2022-06-10,0-4yrs,,,4.29,0,0,,,3.46,0,0
+2022-06-10,12-17yrs,,,1.25,2.08,3.94,,,1.69,1.51,2.82
+2022-06-10,18-39yrs,,,2.58,3.05,8.07,,,3.03,2.6,6.76
+2022-06-10,40-59yrs,,,1.84,2.62,7.16,,,2.76,2.82,6.36
+2022-06-10,5-11yrs,,,1.39,2.32,0,,,1.41,2.15,0
+2022-06-10,60+,,,18.97,5.77,5.98,,,21.67,4.72,5.85
+2022-06-10,ALL,,,3.01,3.05,6.84,,,3.14,2.7,6.17
+2022-06-11,0-4yrs,,,2.49,0,0,,,3.2,0,0
+2022-06-11,12-17yrs,,,1.25,1.28,2.25,,,1.52,1.46,2.66
+2022-06-11,18-39yrs,,,3.7,3.1,8.33,,,3.03,2.56,6.87
+2022-06-11,40-59yrs,,,2.99,3.46,6.24,,,2.76,2.91,6.2
+2022-06-11,5-11yrs,,,1.86,1.16,0,,,1.39,1.95,0
+2022-06-11,60+,,,29.96,3.85,6.88,,,23.45,4.12,5.94
+2022-06-11,ALL,,,3.39,2.86,6.95,,,3.11,2.62,6.18
+2022-06-12,0-4yrs,,,3.04,0,0,,,3.36,0,0
+2022-06-12,12-17yrs,,,2.49,1.44,1.69,,,1.78,1.44,2.66
+2022-06-12,18-39yrs,,,1.61,2.7,5.9,,,2.94,2.53,6.71
+2022-06-12,40-59yrs,,,1.61,1.86,5.89,,,2.56,2.79,6.16
+2022-06-12,5-11yrs,,,0.62,1.39,0,,,1.37,2.02,0
+2022-06-12,60+,,,23.2,3.08,6.21,,,24.26,3.93,6.03
+2022-06-12,ALL,,,2.41,2.24,5.92,,,3.13,2.56,6.17
+2022-06-13,0-4yrs,,,1.24,0,0,,,3.14,0,0
+2022-06-13,12-17yrs,,,0.62,0.32,0,,,1.6,1.39,2.5
+2022-06-13,18-39yrs,,,1.77,1.48,3.67,,,2.92,2.53,6.71
+2022-06-13,40-59yrs,,,1.84,1.18,3.07,,,2.5,2.71,6.08
+2022-06-13,5-11yrs,,,0.31,1.16,0,,,1.22,2.08,0
+2022-06-13,60+,,,20.59,3.47,3.04,,,24.48,4.09,5.78
+2022-06-13,ALL,,,1.73,1.44,3.14,,,3.01,2.56,6.04
+2022-06-14,0-4yrs,,,2.63,0,0,,,3,0,0
+2022-06-14,12-17yrs,,,0,0.64,1.69,,,1.52,1.33,2.09
+2022-06-14,18-39yrs,,,2.26,1.17,4.03,,,2.9,2.51,6.73
+2022-06-14,40-59yrs,,,1.61,1.77,3.69,,,2.3,2.65,6.1
+2022-06-14,5-11yrs,,,0.77,1.39,0,,,1.08,2.02,0
+2022-06-14,60+,,,23.53,2.7,3.51,,,25.49,4.09,5.69
+2022-06-14,ALL,,,2.33,1.44,3.66,,,2.92,2.52,6
+2022-06-15,0-4yrs,,,2.21,0,0,,,2.59,0,0
+2022-06-15,12-17yrs,,,0.62,1.12,5.05,,,1.25,1.14,2.33
+2022-06-15,18-39yrs,,,3.22,2.44,8.84,,,2.74,2.42,6.65
+2022-06-15,40-59yrs,,,2.99,1.52,6.89,,,2.1,2.3,5.81
+2022-06-15,5-11yrs,,,1.55,2.77,0,,,0.99,1.69,0
+2022-06-15,60+,,,29.31,3.66,6.58,,,26.92,3.88,5.63
+2022-06-15,ALL,,,3.05,2.2,7.23,,,2.74,2.32,5.87
+2022-06-16,0-4yrs,,,2.49,0,0,,,2.63,0,0
+2022-06-16,12-17yrs,,,1.87,0.48,3.93,,,1.16,1.05,2.65
+2022-06-16,18-39yrs,,,2.1,2.6,7.91,,,2.46,2.36,6.68
+2022-06-16,40-59yrs,,,3.22,3.89,7.16,,,2.3,2.33,5.73
+2022-06-16,5-11yrs,,,0.93,3.7,0,,,1.06,1.98,0
+2022-06-16,60+,,,21.06,3.47,6.17,,,23.8,3.71,5.48
+2022-06-16,ALL,,,2.6,2.84,6.88,,,2.65,2.3,5.8
+2022-06-17,0-4yrs,,,4.01,0,0,,,2.59,0,0
+2022-06-17,12-17yrs,,,0.62,0.8,1.12,,,1.07,0.87,2.25
+2022-06-17,18-39yrs,,,3.55,3.56,10.18,,,2.6,2.44,6.98
+2022-06-17,40-59yrs,,,2.3,2.2,7.07,,,2.37,2.27,5.72
+2022-06-17,5-11yrs,,,0.77,2.08,0,,,0.97,1.95,0
+2022-06-17,60+,,,18.36,3.48,6.71,,,23.72,3.39,5.59
+2022-06-17,ALL,,,3.01,2.71,7.6,,,2.65,2.25,5.91
+2022-06-18,0-4yrs,,,4.01,0,0,,,2.8,0,0
+2022-06-18,12-17yrs,,,0.62,1.13,2.24,,,0.98,0.85,2.25
+2022-06-18,18-39yrs,,,4.52,1.99,5.37,,,2.72,2.28,6.56
+2022-06-18,40-59yrs,,,2.3,1.86,5.57,,,2.27,2.04,5.62
+2022-06-18,5-11yrs,,,0,0.92,0,,,0.71,1.92,0
+2022-06-18,60+,,,18.43,4.64,7.08,,,22.07,3.5,5.61
+2022-06-18,ALL,,,3.05,2.03,6.05,,,2.6,2.13,5.78
+2022-06-19,0-4yrs,,,2.07,0,0,,,2.67,0,0
+2022-06-19,12-17yrs,,,2.49,1.29,1.12,,,0.98,0.83,2.16
+2022-06-19,18-39yrs,,,1.94,2.09,5.32,,,2.77,2.19,6.47
+2022-06-19,40-59yrs,,,2.77,2.62,4.48,,,2.43,2.15,5.42
+2022-06-19,5-11yrs,,,0.47,2.54,0,,,0.69,2.08,0
+2022-06-19,60+,,,29.85,4.64,5.9,,,23.02,3.72,5.57
+2022-06-19,ALL,,,2.53,2.44,5.19,,,2.61,2.16,5.68
+2022-06-20,0-4yrs,,,1.8,0,0,,,2.75,0,0
+2022-06-20,12-17yrs,,,0,0.64,1.68,,,0.89,0.87,2.4
+2022-06-20,18-39yrs,,,0.48,1.17,3.31,,,2.58,2.15,6.42
+2022-06-20,40-59yrs,,,2.3,1.35,3.34,,,2.5,2.17,5.46
+2022-06-20,5-11yrs,,,0.62,1.38,0,,,0.73,2.11,0
+2022-06-20,60+,,,22.83,2.71,3.47,,,23.34,3.61,5.63
+2022-06-20,ALL,,,1.73,1.33,3.34,,,2.61,2.14,5.71
+2022-06-21,0-4yrs,,,1.94,0,0,,,2.65,0,0
+2022-06-21,12-17yrs,,,0,0.97,3.91,,,0.89,0.92,2.72
+2022-06-21,18-39yrs,,,3.71,0.87,4.85,,,2.79,2.1,6.54
+2022-06-21,40-59yrs,,,4.84,1.27,4.52,,,2.96,2.1,5.58
+2022-06-21,5-11yrs,,,1.86,1.84,0,,,0.89,2.18,0
+2022-06-21,60+,,,21.59,4.06,4.35,,,23.06,3.81,5.75
+2022-06-21,ALL,,,3.21,1.42,4.52,,,2.74,2.14,5.83
+2022-06-22,0-4yrs,,,2.07,0,0,,,2.63,0,0
+2022-06-22,12-17yrs,,,0.62,0.97,3.35,,,0.89,0.9,2.48
+2022-06-22,18-39yrs,,,2.74,1.58,8.93,,,2.72,1.98,6.55
+2022-06-22,40-59yrs,,,1.84,2.71,7.33,,,2.8,2.27,5.64
+2022-06-22,5-11yrs,,,1.4,2.54,0,,,0.86,2.14,0
+2022-06-22,60+,,,30.5,4.64,7.04,,,23.23,3.95,5.82
+2022-06-22,ALL,,,2.68,2.2,7.54,,,2.69,2.14,5.87
+2022-06-23,0-4yrs,,,3.73,0,0,,,2.8,0,0
+2022-06-23,12-17yrs,,,0,1.29,2.79,,,0.62,1.01,2.32
+2022-06-23,18-39yrs,,,4.2,2.39,8.93,,,3.02,1.95,6.7
+2022-06-23,40-59yrs,,,0.92,3.21,8.42,,,2.47,2.17,5.82
+2022-06-23,5-11yrs,,,1.55,1.38,0,,,0.95,1.81,0
+2022-06-23,60+,,,21.91,5.03,8.69,,,23.35,4.17,6.18
+2022-06-23,ALL,,,3.09,2.65,8.53,,,2.76,2.11,6.11
\ No newline at end of file
diff --git a/02_activities/assignments/centralized_waiting_list_activity.csv b/02_activities/assignments/centralized_waiting_list_activity.csv
new file mode 100644
index 000000000..5f3d6601d
--- /dev/null
+++ b/02_activities/assignments/centralized_waiting_list_activity.csv
@@ -0,0 +1,29 @@
+_id,Quarter/Year,New/reactivated,Housed,Total active waiting list,Household no dependents,Household with dependents,Seniors,Total,Room,Bachelor,1 Bedroom,2 Bedroom,3 Bedroom,4 Bedroom,5 Bedroom,Special Priority,Terminally ill,Over housed,Homeless/Youth/Separated families,Indigenous,Exiting Permanent Supportive Housing,Toronto Seniors Housing Corporation,General list,Toronto Community Housing Corporation,Co-op/PNP/ Rent Supplement
+1,Q1 2019,"6,181",522,"102,049","38,044","28,223","35,782","102,049",0,239,172,71,39,1,0,112,39,8,106,,,,257,393,129
+2,Q2 2019,"5,085",805,"78,796","29,171","21,081","28,544","78,796",1,347,255,144,52,4,2,175,56,11,114,,,,449,680,125
+3,Q3 2019,"5,434",767,"77,024","28,442","20,665","27,917","77,024",10,313,242,133,58,7,4,109,52,7,126,,,,473,685,82
+4,Q4 2019,"5,151",872,"75,191","27,521","20,202","27,468","75,191",4,363,275,177,45,7,0,101,43,14,110,,,,604,729,143
+5,Q1 2020,"6,067",471,"78,500","29,115","21,059","28,326","78,500",1,218,132,88,30,2,0,93,30,5,67,,,,276,308,163
+6,Q2 2020,"3,678",497,"78,683","29,183","21,141","28,359","78,683",3,190,166,110,24,4,0,107,22,5,142,,,,221,395,102
+7,Q3 2020,"4,111",525,"79,768","29,565","21,527","28,676","79,768",1,215,172,99,33,5,0,153,32,4,134,,,,202,379,146
+8,Q4 2020,"3,517",735,"81,664","30,498","22,014","29,152","81,664",3,265,283,135,47,2,0,228,49,1,120,,,,337,559,176
+9,Q1 2021,"2,969","1,032","79,332","29,987","21,502","27,843","79,332",4,353,384,183,103,5,0,235,60,7,227,,,,503,911,121
+10,Q2 2021,"3,417",431,"78,177","27,417","21,311","29,449","78,177",0,150,170,99,12,0,0,66,29,3,55,,,,278,301,130
+11,Q3 2021,"2,946",673,"78,791","25,895","18,734","34,162","78,791",0,186,262,151,58,16,0,160,51,11,51,,,,400,510,163
+12,Q4 2021,"5,182",685,"78,879","26,208","18,785","33,886","78,879",0,214,268,125,63,15,0,185,42,7,98,,,,353,514,171
+13,Q1 2022,"4,895",462,"79,572","25,715","18,759","35,098","79,572",0,131,196,97,31,7,0,127,17,10,88,,,,220,319,143
+14,Q2 2022,"5,486",610,"80,532","26,147","19,140","35,245","80,532",0,232,244,103,27,4,0,168,25,7,181,,,,229,424,186
+15,Q3 2022,"5,121","1,262","81,042","26,604","19,561","34,877","81,042",0,578,506,156,21,1,0,243,57,16,195,,,,751,1072,190
+16,Q4 2022,"5,236",935,"84,282","28,224","20,605","35,453","84,282",0,360,355,156,61,3,0,219,33,11,162,,,,510,739,196
+17,Q1 2023,"6,002",736,"84,583","28,456","20,866","35,261","84,583",0,305,306,130,50,7,7,214,28,9,119,,,247,435,408,150
+18,Q2 2023,6403,645,84749,28640,20980,35129,84749,0,297,292,113,55,3,2,195,33,7,149,,,257,378,396,109
+19,Q3 2023,7660,616,85464,29181,21197,35086,85464,0,296,274,97,32,2,0,185,24,5,122,,,260,365,321,120
+20,Q4 2023,6430,536,85097,29260,21273,34564,85097,0,236,182,77,23,2,0,132,20,3,104,,,196,261,207,117
+21,Q1 2024,"7,034",655,"86,959","30,155","21,915","34,889","86,959",0,263,260,100,31,1,0,222,39,12,157,,,239,225,289,127
+22,Q2 2024,7263,618,89838,31579,22689,35570,89838,0,263,238,86,29,2,0,138,37,10,120,,,248,313,234,136
+23,Q3 2024,"7,340",599,"92,965","33,145","23,659","36,161","92,965",0,246,252,81,18,2,0,149,29,7,194,,1,228,219,248,123
+24,Q4 2024,"6,879",588,"100,361","37,097","25,341","37,923","100,361",0,233,243,70,34,8,0,169,35,13,142,,5,184,224,258,146
+25,Q1 2025,"5,351",657,"104,239","39,339","26,231","38,669","104,239",0,265,262,93,31,6,0,191,26,21,3,21,3,238,240,263,156
+26,Q2 2025,5818,795,104585,39814,26129,38642,104585,0,282,364,124,18,3,4,241,42,30,18,22,18,237,253,409,149
+27,Q3 2025,"5,805",731,"104,904","40,010","26,287","38,607","104,904",0,312,269,105,38,5,2,215,34,20,26,20,26,220,256,357,154
+28,Q4 2025,4564,655,105115,40364,26423,38328,105115,0,236,290,89,35,4,1,200,39,16,139,18,10,237,233,245,173
\ No newline at end of file
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