Click here for a short video from Dr. Kieran Moore, Medical Officer of Health for KFL&A Public Health and Medical Director of ACES, describing how to use and interpret the data in the Pandemic Tracker.

Frequently Asked Questions:

Different graphs are shown on each page of the Pandemic Tracker. The main graph (page 1) displays the number of patients admitted to hospital for symptoms grouped into “Syndromes of Interest” for pneumonia, influenza-like illness, infection, or if their visit was flagged as “COVID-19” (see FAQ: How are admissions flagged as COVID?). Pneumonia, influenza-like illness, and infection are called Syndromes of Interest because they represent groupings of patients with certain symptoms that are related to COVID-19. A patient can only be counted once, even if they have more than one of these syndromes. The total number of admissions for all syndromes are calculated each day, and the Tracker displays this number as 7-day and 30-day moving averages (see FAQ: What are moving averages and why do we use them?).

The Pandemic Tracker displays admissions for these syndromes and the flagged COVID-19 in order to tell a more complete story of the impact on our healthcare system.

To compare recent admissions to hospital to what would be expected under normal conditions, admissions for the Syndromes of Interest are added up for the 2018-2019 period, and the 7-day and 30-day moving averages are calculated and displayed. Flagged COVID-19 admissions are not included because it did not exist in 2018-2019. The historical 7-day and 30-day averages are displayed with +1 standard deviation and +2 standard deviations above the historical average to make it easier to compare to the current numbers. See FAQ: What are moving averages and why do we use them? What is the standard deviation of a moving average and why do we use it? How do I use and interpret the graph? 

The graphs on pages 3 and 4 of the Tracker—click on “Data by syndrome (excluding COVID)”—display each syndrome individually. COVID-19-flagged visits are not included here. You can select any combination of syndromes.

Admissions are flagged as COVID based on several criteria that are regularly reviewed and updated. The criteria are developed by reviewing the words in patient admissions records that are being used by healthcare staff to identify COVID-19.

Currently, the criteria for an admission to be flagged as COVID include the following:

  1. Identify all admissions that that include the words/part of words “covid”, “coronav” (for coronavirus), and “ncov” in the reason for admission.
  2. Remove any admissions that include keywords suggesting COVID is not probable (e.g., low risk or unlikely).
  3. Keep only admissions that meet at least one of these additional criteria:
    1. The word “susp” is included. Hospitals have been asked to identify potential COVID patients using the phrase “suspect covid”.
    2. The admission is classified into a syndrome that often captures potential COVID cases. These syndromes include the syndromes used as baseline (i.e., pneumonia, asthma, influenza-like illness, and sepsis) and a few others (e.g., bronchitis and coronary artery disease).
    3. The admission is classified into a generic syndrome (e.g., other or general surgery) and does not include keywords that suggest the patient was only being tested or screened for COVID (e.g., “query” or “rule out”).
    4. The admission is classified into the infection syndrome, but does not include the keyword “unspecified” (this may refer to other coronaviruses).

Just because an admission has been flagged as COVID, it does not mean it represents a COVID-19 positive patient. That cannot be known until the patient is tested and the laboratory result is returned. The COVID flag works to capture the most likely COVID-19 admissions, but will also capture patients with other illnesses.

ACES is a syndromic surveillance system for acute care hospitals. It uses natural language processing to group words or phrases in the “reason for admission” from patient records into into one of 80 syndromes. Syndromes include medical conditions that may indicate an outbreak or public health threat, or capture health system information. For example, ACES captures the syndromes AST (for asthma), OPI (for opioid intoxication or overdoes), ORTHH (for fracture of the femur or hip), and NEWB (for newborn—childbirth).  These syndromes are not clinical diagnoses, but are intended to provide situational awareness of the current health status for a specific population or geography. More information about syndromic surveillance, ACES, and natural language processing can be found here.

The current version of the Pandemic Tracker includes three syndromes: pneumonia, influenza-like illness, and infection (see descriptions of the symptoms captured in these syndromes, below).  They are included because they are known to be symptoms or complications of COVID-19 infection. The syndromes displayed may change in updated versions as we collect more information regarding the symptoms related to COVID-19 that result in patients are being admitted to hospital. If admissions for these syndromes increase to values that are higher than expected, it may indicate community spread in a particular geography.

  • Pneumonia captures symptoms related to pneumonia, an infection of one or both lungs that causes cough, fever, chills, and difficulty breathing. Signs and symptoms vary from mild to severe. Mild symptoms are similar to those of a cold.
  • Influenza-like illness captures a broad range of symptoms that one would have if they had influenza (the ‘flu’). These symptoms can include fever, aching muscles, chills/sweats, headache, cough, fatigue/weakness, runny nose and sore throat.
  • Infection typically captures admissions for nonspecific viral or bacterial infections (i.e., where the identity of the virus or bacteria is unknown). This syndrome is capturing COVID-19 infections without other symptom information provided.

The Pandemic Tracker also includes COVID-19 flagged admissions. See FAQ: How are admissions flagged as COVID?

The black line shows the current (2020) 7-day moving average for hospital admissions for the selected dates and selected Local Health Integration Network(s) (LHINs). The graph on the main page (page 1) shows admissions for pneumonia, influenza-like illness, infection, and other admissions that have been flagged as COVID-19. Page 2 is the same information, but for the 30-day moving average. On page 3 and 4, admissions for the syndrome(s) selected are shown with 7-day and 30-day moving averages, respectively.

The teal line shows the data from 2018 and 2019 (historical) for comparison to the current (2020) data. Lines showing +1 standard deviation (yellow line) and +2 standard deviations (maroon line) for the 2018 and 2019 data are also shown for comparison. If the black line showing the 2020 data rises above the yellow and maroon lines, it is possible that this indicates that community spread of COVID-19 is occurring. One day of data, however, above the past year’s +1 or +2  standard deviations may be due to chance and is not evidence that community spread is occurring. There is strong evidence that community spread is occurring if the 2020 black line is consistently over the +2 standard deviation line.

At the top of the graph you can select to view the data by LHIN, or choose to view the data by 7-day or 30-day moving averages (pages 1 and 2). Page 3 and 4 allow you to select specific syndromes to display, for 7-day (page 3) and 30-day averages (page 4) for the whole province or by LHIN.

At the bottom of the graphs, the date range to display can be entered in the boxes or selected with the slider. Hovering over a date on the graph displays exact values in a popup box.

The map displays the most recent day’s data for the selected LHIN and Moving Average Range. To choose a different date to display on the map, select the date at the bottom of the map. The data that is displayed will appear in the title of the map. Any dates between February 1st, 2020 and the previous day can be shown. If a date after the previous day is selected, the map will show the most recent data available.

The map is colour-coded to compare the current day’s value to expected values from previous years. The LHIN will be teal if 7-day or 30-day moving average is at or under 1 standard deviation above the historical moving average (Level 1). Yellow indicates the current value is greater than 1 standard deviation above the previous year’s moving average  but less than or equal to 2 standard deviations (Level 2). Maroon indicates that the current value is more than 2 standard deviations above the historical moving average (Level 3).

Click and scroll with your mouse to zoom in and move around the map.

Line graphs displaying real-time syndromic data over time offer a simple way to explore current trends. Comparison to previous years can identify changes to the norm.

The calculation and plotting of moving averages (or rolling averages) allows for the smoothing of short-term fluctuations so longer-term trends are easier to recognize.

The 7-day moving average is the simplest approach to remove day-of-the-week variation. The first observation point in a 7-day moving average graph is the average of the first seven days. The second observation point is the average of day two to eight. This is continued so that each set of seven consecutive days is averaged.

The 30-day moving average, in contrast, removes day-of-the-month variation. The first observation point in a 30-day moving average graph is the average of the first 30 days. The second observation point is the average of day two to 31. This is continued so that each set of 30 consecutive days is averaged.

The standard deviation is a statistical measure that shows how much data vary from the average. If there is a lot of variability, the standard deviation will be high. If there is low variability, the standard deviation will be low. The standard deviation of a 7-day moving average, for example, is calculated by taking the difference of the data values on each of the seven days from the moving average for the 7-day period, squaring these differences and summing together, then dividing by 6 (the number of days minus 1) and taking the square root of the result. The standard deviation of the 30-day moving average is calculated in a similar way.

The historical moving average provides a comparison to the current trend. Even with the use of moving averages, there can be random variation in the data. Plotting the historical moving average and standard deviation (both +1 and +2 standard deviations) allows us to take this random variation into account in our comparisons and helps us decide if the current trend is higher than normal.

  • Normal variation is considered to occur when the current moving average is at or under 1 standard deviation above the historical average (Level 1).
    A signal for possible high abnormal activity occurs when the current moving average is greater than +1 standard deviation, but less than or equal to +2 standard deviations above the historical average (Level 2).
  • High abnormal activity is signaled when the current moving average is greater than +2 standard deviations above the historical level (Level 3)

Hospitals share data with ACES in real time as patients are registered. Data is displayed by day. To accurately compare current data to historic data, only full days of data are displayed. Each day at midnight, all admissions from midnight the day before to 11:59 pm are totalled (i.e., at 12:00 am on March 22nd, all records from 12:00 am to 11:59 pm on March 21st are added together). At 12:30 am the dashboard is updated to display the new data.

The historical data is the number of admissions for the syndromes/LHINs shown from 2018 and 2019. Since there is no historical data for COVID-flagged admissions, these admissions are grouped with all admissions for the syndromes of interest  in 2020 and compared to the 2018-2019 syndromes of interest.

For each current day, the average of admissions for that day in 2018 and 2019 are used for comparison. For example, for the Central West LHIN, if there were 5 admissions for pneumonia on January 4th, 2018  and 9 admissions on January 4th, 2019, the historical average for January 4th would be 7. As there is no historical data in 2018 and 2019 for February 29th, the historical average for February 28th is used.

Sometimes there are one or two days of data outages for a hospital. On the graph these will show as sudden very large dips in the historical moving average and standard deviations. These are to be expected and there is no reason to be concerned when the current data is above the historical data in these situations.

ACES captures hospital data from across Ontario. There are currently 10 hospitals in Ontario that do not share data with ACES:

  • Almonte General Hospital (Champlain)
  • Deep River and District Hospital (Champlain)
  • Groves Memorial Community Hospital (Waterloo Wellington)
  • Haldimand War Memorial Hospital (Hamilton Niagara Haldimand Brant)
  • Louise Marshall Hospital (Hamilton Niagara Haldimand Brant)
  • Norfolk General Hospital (Waterloo Wellington)
  • Palmerston and District Hospital (Waterloo Wellington)
  • Francis Memorial Hospital (Champlain)
  • Stevenson Memorial Hospital (Central)
  • West Haldimand General Hospital (Hamilton Niagara Haldimand Brant)

The ACES Pandemic Tracker uses two years of historical data (2018 and 2019) to compare with current (2020) data. In order to make accurate and valid comparisons, complete data for these two years is needed. This means that any hospital that does not have complete admissions data for 2018 and 2019 is excluded from the Pandemic Tracker. Hospitals excluded for this reason are:

  • Alexandra Marine and General Hospital (South West)
  • Brantford General Hospital (Hamilton Niagara Haldimand Brant)
  • Children’s Hospital of Eastern Ontario (Champlain)
  • Clinton Public Hospital (South West)
  • Georgian Bat General Hospital (North Simcoe Muskoka)
  • Grand River Hospital (Waterloo Wellington)
  • Grey Bruce Health Services – Lion’s Head, Markdale, Meaford, Owen Sound, Southampton and Wiarton sites. (South West)
  • Hanover and District Hospital (South West)
  • Headwaters Health Care Centre (Central West)
  • Mackenzie Health – Richmond Hill (Central)
  • Muskoka Algonquin Healthcare – Huntsville District Memorial Hospital and South Muskoka Memorial Hospital (North Simcoe Muskoka)
  • The Ottawa Hospital – General and Civic sites (Champlain)
  • Perth and Smiths Falls District Hospital – Great War Memorial and Smith Falls sites (South East)
  • Orillia Soldiers Memorial Hospital (North Simcoe Muskoka)
  • Sault Area Hospital (North East)
  • The Scarborough Hospital – General and Birchmount Campuses (Central East)
  • Seaforth Community Hospital (South West)
  • South Grey Bruce Health Centre – Chesley, Durham, Kincardine and Walkerton sites (South West)
  • Southlake Region Health Centre (Central)
  • Stratford General Hospital (South West)
  • Mary’s General Hospital (Waterloo Wellington)
  • Mary’s Memorial Hospital (South West)
  • St. Joseph’s Health Centre Toronto (Toronto Central)
  • St. Joseph’s Healthcare Hamilton (Hamilton Niagara Haldimand Brant)
  • Sunnybrook Hospital (Toronto Central)
  • West Parry Sound Health Centre (North East)
  • Winchester District Memorial Hospital (Champlain)

When a user hovers over a specific LHIN on the map, a tooltip appears and displays the number of hospitals that contribute data to the tracker, out of the total number of hospitals within the LHIN.

The ACES Pandemic Tracker displays hospital admissions data. We believe that this data source will be the best signal to monitor for indications of COVID-19 community spread and impact on the healthcare system.

We do not include emergency department visits for a number of reasons. Visits to emergency departments have been steadily decreasing since mid-March as people comply with physical distancing measures. People with COVID-19 related symptoms are now being directed to local assessment centers which do not share data with ACES. Therefore, data from emergency department visits may lead to false assumptions regarding COVID-19 visits.

Admissions for syndromes with symptoms related to COVID-19 infection are monitored as they are the types of presentations that are expected to increase when widespread COVID-19 community activity is occurring.