Our challenge focuses on finding patterns between population density and COVID-19 cases while identifying factors that help predict hotspots of disease spread.
COVisualize addresses the challenge in 2 ways:
According to WHO (World Health Organization), Covid-19 spreads primarily through liquid droplets from a sneeze or cough of an affected person. It is highly recommended to stay 1-2 meters away from each other to reduce the risk of spreading the disease. As of now, every country around the world has either declared a State of Emergency and/or have implemented social distancing, some sooner than others.
Let's take Canada into Consideration, on March 11th, 2020, WHO declared COVID-19 as a pandemic. Provinces started to slowly see cases pop up and then on March 16th and 17th, many Provinces including British Columbia and Ontario decided to implement strict social distancing with schools and businesses closed. However, Quebec found itself still operating regularly until March 22nd, when it implemented social distancing as well.
Now we see that there was a 5-6 day delay between Quebec and other Provinces. Could this have an effect on why Quebec carries 56% of all Covid-19 cases in Canada? Yes, along with other factors of course. It was estimated by the Institute for Health and Metrics and Evaluation that intervening with strict social distancing as early as one week, can reduce COVID-19 cases, especially death by approx. 65%.
BC, Ontario and Quebec Cases And Deaths Chart
In Quebec’s case, there was an extra 5-6 days of asymptomatic people mingling with the public and it is estimated that Covid-19 would have been passed to an average of 2-3 people a day. This could have resulted in thousands of people spreading the disease unknowingly.
COVID-19 Community Mobility Report has conducted research into the percent change in visits to places like Parks, malls, grocery stores, transit stations, and more. When first looking through the document, we expected to see British Columbia’s percentage of visitors to parks to dramatically increase due to the outdoor culture that resides here. To our surprise, B.C. had the lowest percentage in change while Quebec and Ontario have increased by 75% of visitors in parks. Now with the warm weather here, the number is expected to climb and more people will be exploring the outdoors.
Ideally, we wanted our mapping application to focus on Quebec, being the current hotspot for Covid-19 cases in Canada and specifically targeting local parks, as residents of Quebec are more prone to be visiting parks.
Quebec City, the capital city of Quebec has seen many complaints and tickets issued for the lack of social distancing in the city parks. Especially with COVID-19 cases still emerging by the hundreds every day and warm weather approaching, public areas and especially places where people tend to sit and stay longer can become a risk of further spread.
As of now, our application focuses on the parks in Vancouver and in theory, would gather location data from our users or purchase location data from telecommunication providers to provide our users the most accurate data as possible.
Algorithm
Our application's algorithm would determine the risk rank of each park by dividing the park's area by the number of users pinged in that area, to determine the rough estimate of how many people per m2. Our risk scale is out of 10, and the closer to 10, the less space there is between people in that park's area. As B.C. recommends 2m per person as the minimum safe distance, our risk scale will have the minimum safe distance placed in the middle at 5, and parks with a rank between 5 and 10 will be deemed as "risky".
There is still a possibility that the spread of COVID-19 may increase in the future as the restrictions related to COVID-19 are lightning up in many countries. In order to help prevent the increase of such dangerous and infectious diseases, our team decided to explore the human factors challenge, as we believe this challenge would be feasible, yet stimulating enough for our current skill sets.
Our approach to developing this project was first to spend Saturday gathering data from various resources and identify patterns to know what factors have a major impact on the increase of COVID-19 cases. Focusing on Canada allowed us to narrow our time and efforts with analyzing the data. By Saturday evening, were then able to come to a conclusion of what to focus our efforts on for developing a viable solution that could potentially prevent further spread of COVID-19.
Sunday was used for development and finalizing the details of our project, such as creating the video and finalizing the look of our application.
Utilizing an agile method throughout the weekend, we used Jira to generally map out our sprints and tasks to stay organized and focused, as well as Github to host our repository and google docs to host our charts and documents. Splitting our team of six into two teams, frontend and backend, allowed us to utilize each other's expertise while also ensuring everyone got the opportunity to learn and contribute. We held at minimum two meetings per day over the entire weekend to help through challenges, ensure everyone is on track and if changes had to be implemented to complete our project on time.
In order to develop our project, we used the following technologies and tools:
Front-end
Back-end
Data Visualization
Project Management
The main problem that our team faced was finding the correct datasets that would help us better analyze and predict Covid-19 related information. Additionally, analyzing the provided space agency data was challenging. Initially, our team was considering using IBM Watson or AWS for predictive analysis. However, due to the lack of enough resources, we were not able to do it. Consequently, we had to use alternative methods to analyze data and use it effectively for this project.
Data we used for analysis:
Research Information:
Video References: