Links to final source code (github repo) Mashira Farid
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https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties-recent.csv
New-York times API used to populate charts in every county
Data
Data is available at Data folder in github.
To get final preprocessed data https://github.com/samztz/SENG3011_GeeksForHDs/blob/main/PHASE_2/epi_frontend/data/hospitalandrisk.json
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The website’s map’s ability to highlight certain information also makes it easier for users to gather and display information in a quick and efficient manner compared to other websites, and since it’s all accessible on one page, switching between different maps is quick and easy. The fact that each of the areas on the map can be interacted to display as more information, as well as the graphs, makes our website a potent source of information at any given time.
Use cases:
Use case 1:
Policy makers wants to know the risk level and medical resources of the county they lived in and other counties' situation, so they need some vivid visualisation graphs in system which displayed these detailed information.
Thus, the system needs to contain these detailed information and also kept updating. The line chart should be included to track the change of the number of patients, some graphs displaying vaccinations rate and medical resources of each county is also needed.
Requirements:
Based on the need of policy maker. There are four types of visualisation graphs provided to target user:
a. Risk Level.
b. Vaccinations.
c. Staffed All Beds [Per 1000 Adults (20+)].
d. Staffed ICU Beds [Per 1000 Adults (20+)].
e. Licensed All Beds [Per 1000 Adults (20+)].
The target user would be policy makers who are working in local authority in USA.
Our website could deliver visualisation graphs which are related to the analysis of pandemic risk level among all counties in USA and medical resources distribution.
User Interaction:
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When a policy maker checks the severity of one specific county, there is one customised colour bar with discrete number as indicator shown below the risk level graph. It can use colour to differentiate the severity among all counties
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When a policy maker hovers on the specific county, there is one small black box pops up. It describes the risk level of that county in a simplified way.
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When a policy maker clicks one specific county on the graph, there is one side bar on the right-hand side pops up, it contains three parts, first part above is including county name and risk level latest update date. Second part middle includes a status bar that shows the severity of that county based on the risk level. As well as infection rate and other related information. Third part at the bottom exists one line graph which records the change of the number of epidemic cases in past few days. This detailed information can bring a clear overview of the county during pandemic which gives policy maker makes appropriate decision.
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We must flatten the curve to give our healthcare system more time to prepare for this surge of patients. Preparation means enough space, staff, supplies, and systems at the right places and times to meet patient needs. Through mapping and geospatial analytics, we provide situational awareness and foresight for health systems, policymakers, emergency managers, suppliers, and the public to plan and coordinate preparations between facilities, counties, states, or nationwide.
Use case 1:
A policy maker wants to know the risk level of his county in the surge of the pandemic to recognise any resource needs he needs to address
Requirements:
Visual mapping of risk on every county of USA
Displaying risk on every county
Requirements Fulfilled:
Risk data mapped to a map
When a policy maker checks the severity of one specific county, there is one customised colour bar with discrete number as indicator shown below the risk level graph. It can use colour to differentiate the severity among all counties
Policy maker can hover on map to display risk level
Limitations:
Problem:
Data not called dynamically from an API
Solution:
Host the python file in lambda function which in futures can be called to get processed data.
Use case 2:
Policy maker can also look into his county for more information which will give him insights of the risk being shown. For example he can look the vaccination rate of the county and increase vaccination programmes to solve the issue is the rate is less.
Requirements:
Letting the user click on the chart to zoom in the county which displays more metrics of the county
Requirements Fulfilled:
When a policy maker clicks one specific county on the graph, there is one side bar on the right-hand side pops up, it contains three parts, first part above is including county name and risk level latest update date. Second part middle includes a status bar that shows the severity of that county based on the risk level. As well as infection rate and other related information. Third part at the bottom exists one line graph which records the change of the number of epidemic cases in past few days. This detailed information can bring a clear overview of the county during pandemic which gives policy maker makes appropriate decision.
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Use case 3:-
If a policy maker sees his county is in risk , he may want to get an idea of hospitals beds in his county to accommodate more patients in the coming future. That includes total beds, staffed beds and ICU beds to recognise where he should focus to manage his resources to minimise risk. These metrics are given with a ratio to population in the county. For example a county can have a high risk and sufficient beds, but it has less ICU beds which is seen through the charts. Also these charts can give insights about other counties so counties with high resources and less risk can be recognised by the user and the policy maker can communicate with them for resources
Requirements:-
User can change the map types to see different hospital beds ratio in different counties.
Hospital beds ratio mapped to counties with an appropriate colour scale
Requirements fulfilled:-
When a policy maker checks how many empty beds left, the map type would be switched to
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Staffed Beds graph. There is one customised colour bar from light colour to dark colour
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to indicate how many beds left in each county. It could make it possible for distributing the resource between counties because the complete data analysis of staffed beds and risk level provided in the visualisation. The policy makers can compare the risk level of the county they live in with their adjacent counties, as well as comparing the staffed beds left between them. Thus, it could show the possibility to borrow some beds from one county to another which dealing with the insufficient supply of beds for that county.
Limitations:-
Problem:
Data is processed from API’s but is not dynamically available
Solution:
Create lambda function of python file and receive data from hosting an API from it
Use case 4:-
If the policy maker has to do his own surge planning through tools available on different websites like
https://www.euro.who.int/en/health-topics/Health-systems/pages/strengthening-the-health-system-response-to-covid-19/surge-planning-tools/adaptt-surge-planning-support-tool they are able to download data in our website
Requirement:-
Provide link to download data to the user
Requirements fulfilled:-
When a policy maker first go to the page of our website, it would pop up a model which contains the da link and confluence link. Thus, they can download the data as they want.
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Other Limitations for future updates:
Problem 1:
Users cannot be alerted with risk level changes.
Solution in future updates:
When risk level changes which can be achieved through a setInterval method in use-state module of React. Users get notified through email with EmailJS.
Problem 2:
Although charts in counties are updated dynamically through NY times API.The Python file that generates data is not hosted in an API which does not dynamically update the map data.
Solution in future updates
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Future use case:
Our main goal of the project is to increase the maps to developing countries which needs tools like these the most. As we know in the previous pandemic countries like India had a problem with hospital beds and medical supplies. Also these tools focus on small areas as people managing those areas do not always have a data analyst to aid with these important projections. Visual maps makes those policy makers life easier to recognise in detail if they need to act fast with their resource management.
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Responsibilities of Each Member
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Roles | Member | Responsibilities Expected | Responsibilities Delivered | ||||||||||||||||||||||||
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Project manager |
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Frontend Developer |
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Backend Developer and Software Architect |
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Business Analyst |
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Risk Analysis and Testing |
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https://unswseng.atlassian.net/jira/software/projects/SENG3011/boards/46/roadmap
Deliverable 1
Lin Thit Myat Hsu mapped out the report, and was tasked with keeping everyone up to date and coordination of resources, and as such mapped out the project plan.
Mashira Farid and Avijit Prasad were making plans for the API and the basic code, while Zifan Tingzhuang Zhou was tasked with justifying the language and development environments.
Deliverable 2
Avijit and Mashira wrote most of the API between them, with Zifan helping when possible
Lin and Tingzhaung meanwhile handled the documentation, with Lin focusing more on testing while Tinzhaung was more on implementation.
Deliverable 3
The prototype was mainly created by Mashira and Avijit, though every member of the team
For the demonstration itself, everyone had a hand in presenting, while Mashira was the one commanding the final demonstration. Questions from the tutors were answered equally by the team. environments.
Deliverable 2
Mashira Farid made the scrapper
Lin Thit Myat Hsu made the tests
Tingzhuang Zhou made the APis and devided the tasks
Avijit Prasad helped with the scrapper
Zifan Wei helped with the scrapper
Deliverable 3
Avijit Prasad decided use cases and devised work, he also worked on services page that adhered to the use case
Mashira Farid made the UI/UX design and made Home page, Reports page for the demo
Tingzhuang Zhou did project setup and backend
Lin Thit Myat Hsu Did risk analysis and worked on landing page
Zifan Wei Refined use cases
Deliverable 4
Avijit Prasad finalised use case and allotted everyone tasks
Tingzhuang Zhou made the side bar
Mashira Farid made the maps and UI/UX decisions
Lin Thit Myat Hsu Made popup page and risk analysis
Zifan Wei Refined the final use cases and did requirement analysis
Project Summary
Major achievements
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