Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Each app (row) has values for catergory, rating, size, and more.

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

googleplaystore.csv

googleplaystore_user_reviews.csv

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Haha

📌 Introduction

This dataset is a synthetic yet realistic simulation of newborn baby health monitoring.
It is designed for healthcare analytics, machine learning, and app development, especially for early detection of newborn health risks.

The dataset mimics daily health records of newborn babies, including vital signs, growth parameters, feeding patterns, and risk classification labels.

🎯 Motivation

Newborn health is one of the most sensitive areas of healthcare.
Monitoring newborns can help detect jaundice, infections, dehydration, and respiratory issues early.

Since real newborn data is private and hard to access, this dataset provides a safe and realistic alternative for researchers, students, and developers to build and test:
- 📊 Exploratory Data Analysis (EDA)
- 🤖 Machine Learning classification models
- 📱 Healthcare monitoring apps (Streamlit, Flask, Django, etc.)
- 🏥 Predictive healthcare systems

📂 Dataset Overview

• Total Babies: 100
• Monitoring Period: 30 days per baby
• Total Records: 3,000
• File Format: CSV
• Synthetic Data: Generated using Python (pandas, numpy, faker) with medically-informed rules

📑 Column Description

🔹 Demographics

• baby_id → Unique identifier for each baby (e.g., B001).
• name → Randomly generated baby first name (for realism).
• gender → Male / Female.
• gestational_age_weeks → Gestational age at birth (normal: 37–42 weeks).
• birth_weight_kg → Birth weight (normal range: 2.5–4.5 kg).
• birth_length_cm → Length at birth (avg: 48–52 cm).
• birth_head_circumference_cm → Head circumference at birth (avg: 33–35 cm).

🔹 Daily Monitoring

• date → Monitoring date.
• age_days → Age of baby in days since birth.
• weight_kg → Daily updated weight (growth trend ~25–30g/day).
• length_cm → Daily updated body length (slow increase).
• head_circumference_cm → Daily updated head circumference.
• temperature_c → Body temperature in °C (normal: 36.5–37.5°C).
• heart_rate_bpm → Heart rate (normal: 120–160 bpm).
• respiratory_rate_bpm → Breathing rate (normal: 30–60 breaths/min).
• oxygen_saturation → SpO₂ level (normal >95%).

🔹 Feeding & Hydration

• feeding_type → Breastfeeding / Formula / Mixed.
• feeding_frequency_per_day → Number of feeds per day (normal: 8–12).
• urine_output_count → Wet diapers/day (normal: 6–8+).
• stool_count → Bowel movements per day (0–5 is common).

🔹 Medical Screening

• jaundice_level_mg_dl → Bilirubin level (normal <5, mild 5–12, severe >15).
• apgar_score → 0–10 score at birth (only day 1).
• immunizations_done → Yes/No (BCG, HepB, OPV on Day 1 & 30).
• reflexes_normal → Newborn reflex check (Yes/No).

🔹 Risk Classification

• risk_level → Automatically assigned health status:
  • ✅ Healthy → All vitals normal.
  • ⚠️ At Risk → Mild abnormalities (e.g., mild jaundice, slight fever, SpO₂ 92–95%).
  • 🚨 Critical → Severe abnormalities (e.g., jaundice >15, SpO₂ <92, HR >180, temp >39°C).

📊 How Data Was Generated

The dataset was generated in Python using:
- numpy and pandas for data simulation.
- faker for generating baby names and dates.
- Medically realistic rules for vitals, growth, jaundice progression, and risk classification.

💡 Potential Applications

• Machine Learning: Train classification models to predict newborn health risks.
• Streamlit/Dash Apps: Build real-time newborn monitoring dashboards.
• Healthcare Research: Study growth and vital sign patterns.
• Education: Practice EDA, visualization, and predictive modeling on health datasets.

📬 Author & Contact

Created by [Arif Miah]
I am passionate about AI, Healthcare Analytics, and App Development.
You can connect with me:

• 📺 YouTube → [https://www.youtube.com/@intelliaiworld]
• 💼 LinkedIn → [www.linkedin.com/in/arif-miah-8751bb217]
• 💻 GitHub → [https://github.com/Arif-miad]

⚠️ Disclaimer

This is a synthetic dataset created for educational and research purposes only.
It should NOT be used for actual medical diagnosis or treatment decisions.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.

• I've downloaded and wrangled the John Hopkins dataset on the number of reported COVID-19 cases by country.
• I joined this dataset with a bunch of additional metadata on country-level.

The data only covers the period Jan. 22 - March 23, but it should be a piece of cake to apply the metadata provided here on a larger range of data (just perform a join operation).

I used the dataset for an online lecture on data visualization --> https://www.youtube.com/watch?v=l85l1qmosEU

The additional variables provided here could shed some light on correlational relations between - for example - the share of government expenditure in the health care system and the growth rate of the virus in a given country.

• Data sources:

--> Reported COVID-19 cases by country by day: https://github.com/CSSEGISandData/COVID-19 --> Data on health expenditure comes from WHO: https://apps.who.int/nha/database/Select/Indicators/en (created my own table) --> Population data and other socio-demographic data: https://www.worldometers.info/world-population/population-by-country/ --> Countries divided by continent: https://www.worldometers.info/geography/7-continents/

• Methodology:

1. John Hopkins data was sometimes inconsistent in their level of aggregation; reporting country-level data in most cases, but sometimes opting for county/state level data (e.g. for the United States). I aggregated all data to country-level. Moreover, there were plenty of inconsistencies in country notation between the different datasets, which required some fuzzy matching techniques.
2. I melted the data so that one line corresponds with one day in a particular country. This is the default data scheme for anyone trying to do some data viz on the dataset at hand (I sacrifice data size for convenience here, I know...)

Some of the interactive dashboards created with this data:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2F2d8e73336e269038f06b43f81183fd87%2Fcovid19%20dashboard.JPG?generation=1597334049308430&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2342187%2Ff08488ae7bded1f5850e730b87437782%2Fcovid19%20dashboard%202.JPG?generation=1597334070819173&alt=media" alt="">

Have fun!

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

TfL statement: We've committed to making our open data freely available to third parties and to engaging developers to deliver new products, apps and services for our customers. Over 11,000 developers have registered for our open data, consisting of our unified API (Application Programming Interface) that powers over 600 travel apps in the UK with over 46% of Londoners using apps powered by our data. This enables millions of journeys in London each day, giving customers the right information at the right time through their channel of choice. Why are we committing to open data? Public data - As a public body, our data is publically owned Reach - Our goal is to ensure any person needing travel information about London can get it wherever and whenever they wish, in any way they wish Economic benefit - Open data facilitates the development of technology enterprises, small and medium businesses, generating employment and wealth for London and beyond Innovation - By having thousands of developers working on designing and building applications, services and tools with our data and APIs, we are effectively crowdsourcing innovation How is our open data presented? Data is presented in three main ways: Static data files - Data files which rarely change Feeds - Data files refreshed at regular intervals API (Application Programming Interface) - Enabling a query from an application to receive a bespoke response, depending on the parameters supplied. Find out more about our unified API. Data is presented as XML wherever possible.

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. This data is extracted from the same link and made available in csv format.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus.

The data is available from 22 Jan 2020.

Acknowledgements

Johns Hopkins university has made the data available in google sheets format here. Sincere thanks to them.

Thanks to WHO, CDC, NHC and DXY for making the data available in first place.

Picture courtesy : Johns Hopkins University dashboard

Inspiration

Some insights could be

1. Changes in number of affected cases over time
2. Change in cases over time at country level
3. Latest number of affected cases

Dataset Structure

Columns: 1. User ID: Unique identifier for each user 2. Date: Date of the activity 3. Step Count: Number of steps taken 4. Distance (km): Distance covered in kilometers 5. Calories Burned: Total calories burned 6. Active Minutes: Total minutes of physical activity 7. Workout Type: Type of workout (e.g., Running, Walking, Cycling, Swimming) 8. Duration (min): Duration of the workout in minutes 9. Heart Rate (bpm): Average heart rate during the activity 10. Sleep Duration (hours): Total hours of sleep 11. Sleep Quality: Quality of sleep (e.g., Good, Fair, Poor) 12. Water Intake (liters): Amount of water consumed 13. Calories Intake: Total calories consumed 14. Weight (kg): Weight of the user 15. Mood: Self-reported mood (e.g., Happy, Stressed, Tired) 16. Notes: Any additional notes about the day or workout

Example Entry:

Data Collection Methods: 1. Wearable Devices: Smartwatches or fitness trackers can provide step count, distance, calories burned, heart rate, and active minutes. 2. Mobile Apps: Health apps can log workout types, durations, and track water and calorie intake. 3. Manual Entry: Users can manually enter sleep quality, mood, weight, and notes. 4. Integrations: Integrate with other health apps and devices for comprehensive data collection.

Usage: - Personal Fitness Tracking: Individuals can monitor their progress and adjust their routines. - Research: Anonymized datasets can be used for studies on physical activity and health outcomes. - Health Monitoring: Healthcare providers can use the data for monitoring patient health and recommending interventions.

Here are a few use cases for this project:

1. Smart Shopping Assistant: Integrate the "Snack" model into a shopping app that allows users to quickly find and identify specific snack products on the shelves. By scanning the shelf with their smartphone's camera, users can receive real-time information about the snacks, such as nutritional facts, price comparison, and personalized recommendations based on dietary preferences.

2. Inventory Management: Retail stores can use the "Snack" model to automate inventory tracking and management. By regularly scanning shelves with a computer vision-equipped device, store owners can receive real-time updates on stock levels and identify which items need restocking or have expired.

3. Interactive Marketing Campaigns: Brands can use the "Snack" model to create augmented reality (AR) marketing experiences for consumers. By incorporating the model into AR apps, users can find hidden promotions and virtual rewards by scanning snack packages with their smartphone, creating fun and engaging brand experiences.

4. Dietary Monitoring: The "Snack" model can be integrated into a health tracking app that allows users to monitor their daily snack consumption more easily. By simply taking a photo of their snacks throughout the day, users can receive instant feedback on the nutritional content of their snacks, helping them make smarter snacking choices and maintain a healthier diet.

5. Accessible Product Information for Visually Impaired Users: The "Snack" model can be used in apps designed for visually impaired individuals, allowing them to easily identify snack products and access relevant information about the product. By scanning the snack with their smartphone's camera, users could receive audio feedback containing product details such as ingredients, allergen information, and nutritional data.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Dataset Card for Dataset Name

  Dataset Summary

MobileRec is a large-scale app recommendation dataset. There are 19.3 million user\item interactions. This is a 5-core dataset. User\item interactions are sorted in ascending chronological order. There are 0.7 million users who have had at least five distinct interactions. There are 10173 apps in total.

  Supported Tasks and Leaderboards

Sequential Recommendation

  Languages

English

  How to use the… See the full description on the dataset page: https://huggingface.co/datasets/recmeapp/mobilerec.

In 2023, it was estimated that social forum and news aggregator Reddit saw over 26.5 million U.S. dollars in revenues from global paying users with an annual subscription. A premium Reddit subscription comes with an ad-free environment, as well as the possibility to join premium subreddits such as r/lounge. In 2022, Reddit counted approximately 530 thousand paying users. By 2026, Reddit annual subscription revenues are estimated to bring in 36.5 million U.S. dollars in revenues.

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

              TikTok interactions: is there a magic formula for content success?

              In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
              The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
              It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.

              What’s trending on TikTok Shop?

              Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
              TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
              accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.

Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication.Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication. You can see the map on Ottawa Public Health's website.Accuracy: Points of consideration for interpretation of the data:Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) on May 12th, 2020. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. Cases are assigned to Ward geography based on their postal code and Statistics’ Canada’s enhanced postal code conversion file (PCCF+) released in January 2020. Most postal codes have multiple geographic coordinates linked to them. Thus, when available, postal codes were attributed to a XY coordinates based on the Single Link Identifier provided by Statistics’ Canada’s PCCF+. Otherwise, postal codes that fall within the municipal boundaries but whose SLI doesn’t, were attributed to the first XY coordinates within Ottawa listed in the PCCF+. For this reason, results for rural areas should be interpreted with caution as attribution to XY coordinates is less likely to be based on an SLI and rural postal codes typically encompass a much greater surface area than urban postal codes (e.i. greater variability in geographic attribution, less precision in geographic attribution). Population estimates are based on the 2016 Census. Rates calculated from very low case numbers are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time.Update Frequency: Biweekly Attributes:Ward Number – numberWard Name – textCumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a Ward, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardCumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward, excluding cases linked to outbreaks in LTCH and RHCumulative number of cases linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19 linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 30 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 30 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHNumber of cases in the last 30 days linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19, reported in the 30 days prior to the data pull, linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 14 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 14 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHContact: OPH Epidemiology Team