Different graphs are shown on each page of the Viral Respiratory Mapper – ACES Admissions. 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 Admissions Mapper displays this number as 7-day moving averages (see FAQ: What are moving averages and why do we use them?).

The Admissions Mapper 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 with typical seasonal levels, comparative 7-day moving averages from the previous two years are calculated and displayed. The historical moving average is 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 is the historical data calculated? How do I use and interpret the graph?

On the dashboard page "Data by syndrome (excluding COVID)" graphs display each syndrome individually. COVID-19-flagged visits are not included here. You can select any combination of syndromes.

On the dashboard page “Data by age group (Ontario only)” graphs display admissions across Ontario for the Syndromes of Interest and COVID-flagged admissions for 4 age groups: 0 to 4, 5 to 17, 18 to 64 and 65+. Two combinations of syndromes are available: all of the syndromes of interest and COVID-flagged admissions, or just pneumonia and influenza-like illness. The historical data here is also from the previous two years.

Admissions are flagged as COVID-related based on several criteria that are regularly reviewed and updated. The criteria are developed by reviewing the words in a patients 'reason for admission' (as received by ACES) that are being used by healthcare staff to identify COVID-19 and based on the syndrome the admission was classified as. Currently, the criteria for an admission to be flagged as COVID-related include the following:

1. Identify all admissions that that include the words/part of words “covid”, “coronav” (for coronavirus) in the reason for admission. Admissions that include the emergency ICD10 codes of “u07.1” and “u07.2” are also included.
2. Remove any admissions that include keywords suggesting COVID is not probable (e.g., low risk or negative). This stage also removes words identifying admissions that are incidental (e.g., “asymp” for asymptomatic, or keywords that indicate non-COVID reasons for admission (stroke, appendicitis). Finally, this stage removes admissions with keywords indicating that the admission is vaccine-related or for an indication post-covid.
3. Remove any admissions that fall into syndromes that would be purely incidental (e.g., toxicology, obstetrics, injury).
4. Apply a specific combination of syndromes with inclusion and/or exclusion criteria. For example, all pneumonia admissions are included but sepsis admissions with the keywords “bacteria”, “urosepsis” or “arthritis” are excluded. Decisions for syndrome and keyword inclusion/exclusion criteria were made based on medical review.

Just because an admission has been flagged as COVID-related, 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-related admissions, but will also capture patients with other illnesses. Furthermore, effort is made to exclude incidental COVID-19 admissions but the process is not exact.

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 Admissions Mapper 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.

• 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 Admissions Mapper also includes COVID-19 flagged admissions. See FAQ: How are admissions flagged as COVID?

The black line shows the current 7-day moving average for hospital admissions for the selected dates and selected Local Health Integration Network(s) (LHINs) regions. The graph on the main page shows admissions for pneumonia, influenza-like illness, infection, and other admissions that have been flagged as COVID-19. The page labelled "Data by syndrome (excluding COVID) shows the 7-day moving average of admissions for the selected syndrome(s).

The teal line shows the historic data from the previous two years for comparison to the current data. Lines showing +1 standard deviation (yellow line) and +2 standard deviations (maroon line) for the historic data are also shown for comparison. If the black line showing the current data rises above the yellow and maroon lines, this indicates that current respiratory-related admissions may be higher than normal. One day of data, however, above the past year's +1 or +2 standard deviations may be due to chance. If the current black line is consistently over the +2 standard deviation line, there is strong evidence that current volumes are above normal.

At the top of the graph you can select to view the data by LHIN, and on the syndromes page to to select specific syndromes to display as well.

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.

For the by age graphs, the same principles hold true but the graphs. The date range can be selected from the top centre.

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 September 1, 2023 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 the 7-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.

The historical data is calculated from the previous two years of data. For example, when looking at current data for February 2025, the historical comparison will be calculated with data from February 2024 and February 2023. Using multiple years of data for comparison increases the stability of the comparison and removes the impacts of any one unusual year. The historical moving averages and standard deviations show the typical seasonal trends, and can provide information on whether the current season is behaving normally or not.

The goal of the historical moving averages and standard deviations is to identify unexpected variations in the current 7-day moving average. As such, the intent is to compare a single point (the current 7-day moving average) to an average of values from the same period in previous years and so an average of 7-day averages across the two years is used. Likewise, the standard deviation looks for typical variations in the 7-day moving average (rather than daily variations). 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?

Sometimes there are major outages from hospital networks for an extended period of time. In those cases, that year will be removed from the historical moving average and only a single year of data will be used. In these cases, the standard deviation will show a spike for the first few days and then normalize.

Sometimes there are one of two days of data outages for a specific hospital. In that case the graphs may show a sudden dip in the historical moving average. 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.

Line graphs displaying real-time syndromic data over time offer a simple way to explore current trends. Comparison to averaged data from 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 current 7-day moving average is calculated in this way. The historical 7-day moving average is actually an average of moving averages across the previous two years. Specifically, it is an average of 7 days worth of 7-day moving averages from two years ago and 7 days worth of 7-day moving averages from one year ago (i.e., 14 days of 7-day moving averages, themselves each an average of 7 days).

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. In general, the standard deviation is calculated by taking the difference of each individual data point from their average, squaring these differences and summing them together, dividing by number of individual values minus 1, and then taking the square root of the result.

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)

To calculate the historical standard deviation, the difference between individual 7-day moving average values is used to determine variation. Specifically, the difference for each individual 7-day moving average from the past two years (14 data points) to the average of the 14 moving averages is calculated as described above.

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 22^nd, all records from 12:00 am to 11:59 pm on March 21^st are added together). At 12:30 am the dashboard is updated to display the new data.

ACES captures hospital data from across Ontario. There are currently 8 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)
• West Haldimand General Hospital (Hamilton Niagara Haldimand Brant)