COVID Caution is a project created at Simmons University's SharkHacks MiniHack 2020. I was in a team consisting of one other student, and I contributed to both research and design. I also served as a mentor, as my teammate had no prior experience working on a design project. COVID Caution received the award for "Best Use of COVID-19 Data
December 2020, 6 hours
Two-member team project at Simmons University SharkHacks MiniHack Hackathon
Designer (discovery + design), Mentor
In order to respond effectively to global crises such as the COVID-19 pandemic, robust tracking on the number of cases in a given area must be implemented for the sake of public health. As states begin to reopen, it is imperative for data on local cases of COVID-19 to be widely accessible to the public.
COVID Caution is an application that makes local COVID-19 data easily accessible to those who need it. Not only does it provide users with information on COVID within their local area but it also allows users to asses the risks of leaving their homes to perform essential tasks, such as grocery shopping. The features of this application provide easy access to information that could potentially save their lives.
As part of the research process, we researched and compared four commonly used COVID tracking resources: Florida's COVID-19 Data and Surveillance Dashboard, Johns Hopkins Coronavirus Resource Center, NYC COVID-19 Data, and New York City Coronavirus Map and Case Count. We found that there are good COVID tracking resources. Most of the COVID tracking resources give a lot of different statistics in countries, cities, districts, but they are not very easy to use. In most of the sites, there are many tabs and people aren’t able to find the data in a concise and efficient way. Many of the websites do not show a specific location or a specific district.
We interviewed two potential users, asking questions about their experiences going outside during the Coronavirus pandemic and their experiences tracking cases of COVID-19, specifically local cases. From our user interviews, we found that people normally go out to do necessary tasks such as buying groceries, they also sometimes go out to pick up fast food or do activities where they know they can be socially distant. They most often use Google and local news as sources for COVID-19 data and mostly want to search for data regarding the areas they live in / nearby areas. They also check COVID-19 data more often for places they are traveling to if they are outside of the town they live in.
Based on the user interviews and background research, I created a journey map depicting a potential user buying groceries at Costco in an outside town in the time of COVID-19. By further analyzing this journey map, we were able to find potential issues the user may have that we are able to address in our design, particularly focusing on users’ safety and knowledge of risks associated with going outside to perform certain essential tasks.
We reframed our user needs to encompass the four following questions:
Based on our research and engagement, we ideated three main features to use in our product: a home page that uses location services or the user’s own inputted location to display an overview of COVID-19 cases in their current location, an explore page that allows users to see current COVID-19 cases in other locations, and a “leave home” feature which allows users to find the best ways to safely complete necessary tasks.
Because of time constraints due to the nature of the hackathon, we were unable to test our prototype on target users. However, we received feedback from the hackathon judges that could potentially influence further iterations of our design. When presenting our project to the judges, we received positive feedback on the simplicity of our design, as it is important for an application with the goal of increasing accessibility to COVID-19 data to be simple and easy to understand. The biggest criticism we received was that of our “risk level” calculators, as the design makes it difficult to understand where this risk level measurement comes from.
The biggest concerns regarding this project that arose were those of data. In order to have location-specific COVID-19 data, we must use datasets populated with this information. However, the scope of most COVID-19 data is not as specific as we would like in order for our application to have maximum functionality, so we must make trade-offs and provide COVID-19 data to users based on broader locations. Additionally, due to the time constraints and the extremely short nature of the hackathon, I was unable to place emphasis on the visual design of the app, something that is important for the next steps of the app.
This project forced me to manage my time as efficiently as possible to research, ideate, prototype, and test. Because of this, I was able to have a clearer focus on what I was doing. My teammate had no experience with the design process, so I explained each step of the process and helped my teammate determine which research would be most valuable for developing solutions to the issue of lack of COVID-19 data accessibility, helping us create a well thought out application and win the prize for the Best Use of COVID-19 Data.