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."
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, providing easy access to information that could potentially save their lives.
To discover the state of COVID tracking resources, I analyzed four of the most popular ones. Most of the COVID tracking resources give a lot of different statistics in countries, cities, districts, but they are not very easy to use. Most of the resources have many tabs and people aren’t able to find the data in a concise and efficient way. Many of the resources do not show users 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.
The key features of the prototype correspond to the user needs we identified throughout our research process. Due to the time constraints of the hackathon, we were unable to test and iterate based on user feedback.
One of the most important user needs we identified in our research was that users felt that there was a lack of resources that can provide them with accessible, accurate information on local COVID data. COVID Caution gives users a simple visual overview of COVID data in their current location, also allowing users to easily change locations based on where they are.
COVID Caution also provides users with easy access to global COVID data within the same platform as their local COVID data, creating a centralized resource for users to be able to view COVID data, allowing users to quickly check COVID data as needed.
It's often tedious for users to search through various COVID resources when checking to see if it's safe to complete tasks in locations they may not be as familiar with to avoid the spread of COVID. COVID Caution allows users to select a location and provides them with a preliminary risk reading to inform users on the location's COVID safety.
When filling in the details of their trip, users are provided with risk assessments based on where they're going, what they're doing, and how they're getting there. Providing users with a risk assessment reading allows them to feel more safe and validated when completing their in-person tasks.
To reduce the amount of effort the user must put in while in the trip flow, COVID Caution allows users to view important local COVID data in the location they've traveled, reducing user frustrations surrounding the amount of resources they must go through to find the data they're looking for.
To address the opportunity area of making information on safety precautions to avoid the spread of COVID after going out more accessible, COVID Caution provides users with safety tips both during and after the trip based on where they're going, what they're doing, and how they're getting there. COVID Caution also allows users to provide feedback on their trip experience, helping improve the safety tips while making users feel validated.
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 test and iterate on the design, 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 served as a mentor, explaining each step of the process and helping my teammate determine which research would be most valuable for developing solutions to the issue of lack of COVID-19 data accessibility. Both of these helped us create a well thought out application, resulting in us winning the award for the Best Use of COVID-19 Data.