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https://covidactnow.org/data-api
Risk metric is received which is displayed on the risks maps. Risk metric is determined by population of the county, weekly infection rate, staffed hospital beds and ICU beds in the county and scaled in a range from 1 to 5.
Vaccination ratio is received by counties which is later converted into vaccination percentage and displayed with counties.
Weekly covid cases by counties are received which is used to determine community threats in each county.
NY TIMES API
New York times API gives county wise data for charts to be displayed in each county mapping showing number of cases with time.
https://github.com/covidcaremap/covid19-healthsystemcapacity/tree/master/data/published
Following dataset is used to receive hospital beds data which is later added to risk data. All the calculations and preprocessing done in. https://github.com/samztz/SENG3011_GeeksForHDs/blob/main/PHASE_2/epi_frontend/data/data_preprocessing.ipynb
Feedback
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which produces final data in this format:
fips | risk | infectionRate | county |
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Feedback
Deliverable 1
Through feedback we realised our understanding of article and report data that we were supposed to produce is not correct. We were also asked to document our project management better through Grantt charts and more detailed communications which we included in the final report
Deliverable 2
We were advised to work on implementation of API
Deliverable 3
In the first Demo we realised our use case and requirements were not properly defined. We focussed on a broad range of users and our website had too many features that didn't cater to a specific target audience. Our main goal from the start was to create a website that caters to policy makers. To help our target user do resource management and surge planning with the aid of our website. We initially decided to make our websites for analysts as we thought we would provide them with different visual aids (charts and map) with some raw data to help them do their analysis better in outbreaks.
After the demo, we got feedback from our mentors that the idea to provide visual aids and data is good but the target audience does not require these services as they specialise on them. We took our time and through the feedback changed the whole website with a more clear target user. Adopting the age old 'Do not fall in love with your code' approach we started our project from ground up but this time with a more clear use case and service we were providing.
We removed redundant features and shifted our focus to policy makers in countries, specially developed countries, so they can get relevant data and visualisations to help them. We also got an idea to focus on small suburbs rather than countries to better represent our data from our mentor. Through this we focussed on making one map but made it as detailed as possible with enough insights so a policy maker can make most of his resource decisions through it.
We also preprocessed datas from different sources to give useful insights to the user. In the end, the feedback process helped us to understand the importance of having a Use Case in mind before implementing features.
Application Architecture and Tech stack
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