COVID-19 Visualizations

COVID-19
Visualizations

Cathy Jian, Celine Guirguis, Emily Nie, Michelle Lam, Mina Chong, Palak Agarwal, Rahma Osman, Ria Patel, Risheena Banerji, Sara Gehlaut, Thomas Cwintal, Vanessa Wong

In September 2020, a pandemic visualization taskforce was developed to combat the general lack of knowledge surrounding important data amassed from the SARS-CoV-2 virus in the past year. In preventing this panic, the Canadian government has effectively cut off the knowledge base of Canadians regarding the pandemic and important statistics. To reverse this, the Race to a Cure pandemic taskforce has aggregated current data related to three major points of interest:

Availability of medical resources in Canadian Hospitals
Correlating, Testing, and Reporting of Cases
Chronic Illness Patient Protection [specifically, cancer]

Researchers of the taskforce conducted an extensive review of current information available on the three topics of interest. Following this review of current literature and data, researchers identified datasets (provided directly from Statistics Canada) that encompassed a niche subtopic in each umbrella topic of interest. Said datasets were presented in visual formats, using the visualization software Tableau. The visualizations should accurately shed light on current data available on major topics of interest related to the SARS-CoV-2 pandemic.

Availability of beds, PPE, and ICU capacity
in hospitals

During the SARS-CoV-2 pandemic, many patients who test positive may be admitted to a hospital’s intensive care unit (ICU) based on their condition. The symptom profile of the SARS-CoV-2 virus is extremely varied and on the severe end of the spectrum, can include trouble breathing, persistent pain and pressure in the chest, oxygen deprivation and amnesia (Symptoms of Coronavirus). This symptomology often leads to major complications that land patients in the ICU.

As the second wave of the pandemic is currently underway, it is to be expected the need for beds, personal protective equipment (PPE), and ICU spaces will rise. The issue, however, occurs when there is an equal number of patients in critical care units who are hospitalized for other conditions. These individuals require similar resources to SARS-CoV-2 patients, and a struggle between which group receives which resources arises. A number of visualizations explore this resource struggle and the managing efforts of hospitals across Canada during the second wave.

a

Correlations between ICU Bed Capacities and Deaths

Thomas Cwintal

In this data set, researchers collected the latest ICU bed capacities in the following provinces and territories: Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland, Nova Scotia, Northwest Territories, Prince Edward Island, Saskatchewan, and the Yukon Territory. The data relating SARS-CoV-2 deaths and the subsequent ICU capacities in each region was recorded in a double bar graph. The graph summarized statistics from December 2020 to present day. Both data sets related these numbers in units per thousand people in the province of interest. The data shows that areas with higher bed capacities, on average, displayed lower SARS-CoV-2 deaths per thousand. However, areas with known lesser populations (i.e the Northwest Territories) displayed low bed numbers and a low death rate. This can be attributed to the fact that with a lower overall population to service, lower bed numbers (in comparison to densely populated regions like Ontario) would not affect hospitals as this would be enough to service said population.

c

General Hospital Capacities

Michelle Lam

This dataset focuses on general hospital resources in the province of Alberta. With an increasing trend of SARS-CoV-2 cases in the province, it is quickly becoming a pandemic hotspot. In the graph labelled “ICU cases in Alberta”, researchers have amalgamated all ICU case counts from across Canada (in blue) and Alberta (in red). The graph details ICU hospitalizations from September 20th to November 19th, and shows a clear increase in the number of hospitalizations. This can be attributed to the presence of a “second wave” of SARS-CoV-2. As Canadian provinces transition from warmer summer weather to colder conditions and the flu season, the infectious ability of the virus increases exponentially. The increase in ICU hospitalizations can also be attributed to looser restrictions and the return of students and adults to institutions (school, after-school activities, and workplaces). The graph labelled “Acute Care Services in Alberta in April” provides a comparison of resources available at the onset of the pandemic to the numbers seen in Alberta right now. The data looks at large urban centres (Edmonton, Calgary) and general areas of the province (Northern, Central, and Southern Alberta).

Correlating testing, reporting and cases

This sub-project focuses on how the number of COVID-19 tests done daily relates to cases counts and how hospitals are handling this situation. The correlating testing, reporting and cases visualization project gathered data for hospital capacity, ICU capacity, COVID-19 cases reported and testing for a variety of geographical locations across Canada during the year 2020 to see whether there is any correlation between testing, cases and hospital capacity. The hypothesis is that since hospitals only have a fixed number of staff, when testing increases the number of staff treating patients decreases. If more testing is done and cases do in fact go up, then the number of staff treating patients increases and the number of testing decreases meaning that fewer tests are done and people are unable to get their results quickly, further spreading the virus. This project collected, analysed and visually represented provincial data from Alberta, British Columbia, Manitoba, Quebec, Saskatchewan and the Maritimes as well as regional data from urban centers like Ottawa to see if there is any correlation.

a

Hospitalization

Emily Nie

Using data of hospitalization from COVID-19 in Alberta, Manitoba, Ottawa and Quebec we were able to create a histogram with lines coloured blue, orange, red and teal respectively to represent the hospitalization data over time. Alberta hospitalization data is from March 8 to November 3. Manitoba hospitalization data is from May 8 to October 24; this data is the weekly average of hospitalization numbers, additionally, there is a gap in the data on the week of October 11. Ottawa hospitalization data is from February 10 to October 31. Quebec hospitalization data is from March 6 to November 3.

b

ICU Capacity

Palak Agarwal

Using data of ICU Capacity from COVID-19 in Ottawa, Quebec, Alberta and Manitoba, we were able to create a histogram with lines coloured black, purple, light purple, and yellow respectively to represent the ICU cases over time. All four locations have ICU data from March 6 to October 31. The definition of intensive care in Quebec changed on May 19.

c

Regional Data

Cathy Jian

Using data of COVID-19 hospitalization, ICU capacity, deaths, testing and active, confirmed, and recovered cases in Alberta, British Columbia and Saskatchewan, we were able to make the following stacked bar charts to make provincial comparisons. In the hospitalizations, ICU capacity, and deaths graph, the provinces are listed in alphabetical order with patients who are deceased, hospitalized and in intensive care represented by the light purple, purple and dark purple bars respectively. This data was taken on December 13.

In the breakdown of COVID-19 cases and total tests completed graphs, the provinces are listed in alphabetical order with tests completed, active cases, confirmed cases, and recovered cases represented by the yellow, dark purple, purple, and light purple bars respectively. This data was taken on December 13.

d

Testing/Positivity

Ria Patel

Using data of daily COVID-19 testing and active cases in Ottawa, Quebec, British Columbia, Alberta, and Manitoba, we were able to create a histogram with lines coloured black, purple, light purple, yellow, and dark purple respectively to represent the testing and reporting data over time. Alberta testing and reporting data is from March 6 to November 3. British Columbia testing and reporting data is from January 25 to November 2. Manitoba testing and reporting data is from May 3 to October 24; this data is the weekly average of test and case numbers. Ottawa testing and reporting data is from March 10 to October 28. Quebec testing and reporting data is from March 6 to November 5.

e

Interview with Epidemiologist

In order to achieve a deeper insight into the correlation between testing and reporting of COVID-19 cases during this pandemic, members of this visualization project team met with Cynthia Carr, a Canadian epidemiologist, founder of EPI Research and specialist in epidemiology and health policy for remote and Indigenous communities.

Carr advised our team on how to gather and analyze data and she gave insightful opinions into the field of epidemiology in the context of the ongoing pandemic.

First, after reflecting on her experience in health policy and the political side of pandemic planning, Carr explained that prevention is the best method of planning. No amount of testing or lockdowns will prevent what could have been prevented by a strong health care infrastructure. The constant change in governing parties and short term health care plans create a difficult environment to cultivate strong pandemic prevention practices.

Cynthia Carr

Second, Carr reiterated that epidemiologists have studied pandemic graphs and created projections of cases counts based on current infrastructure for multiple pandemics and epidemics. The actual number of cases and the reported number of cases is almost always different as testing can be faulty and not every person gets tested. This validated the concern of testing affecting reporting. If less than ten tests are done in a day, the number of new cases that day will most definitely be equal to or lower than ten, despite potential untested positive cases.

Finally, in answer to our root questions:“Does more testing mean more cases?”, Carr answered no. That conclusion is easy to jump to but doesn’t take any of the other factors into effect. Everything from all citizens wearing masks to abiding by 14 day quarantine rules affects how a pandemic runs its course. Testing simply reveals a portion of the affected population, the amount of tests reflect the sample population size. The rules for testing including letting asymptomatic people or those with mild symptoms get tests also affects case numbers.

In conclusion, the statement “increased testing means increasing cases” is an overgeneralization as there are far too many factors at play to determine a single cause of increasing COVID-19 case reporting.

Protecting cancer patients during COVID-19

Cancer patients are one of the more vulnerable demographics during the time of the COVID-19 pandemic. This sub-project focuses on Canadian provinces and how they are accommodating cancer patients, and was inspired by the world's first clinical trial led by Dr. Rebecca Auer, which aimed to protect cancer patients from COVID-19 by boosting their immune systems.

RACE TO A CURE