Leveraging AWS Analytics to Collect and Analyze COVID-19 Test Center Data
With the outbreak of COVID-19, governments and organizations worldwide have been ramping up their efforts to test and contain the spread of the virus.
Introduction:
With the outbreak of COVID-19, governments and organizations worldwide have been ramping up their efforts to test and contain the spread of the virus. One of the challenges faced by testing centers is tracking the spread of the virus among individuals standing in line to get tested. Our client, a leading healthcare provider, wanted to investigate the hypothesis that individuals standing in line at testing centers were at a higher risk of contracting COVID-19. To do this, they needed a robust data collection and analysis system.
Solution:
To help our client collect and analyze data, we recommended using AWS Analytics services. The solution consisted of several AWS services including Amazon Kinesis Data Streams, Amazon S3, AWS Glue, Amazon Athena, and Amazon QuickSight.
Data Collection:
The first step was to collect data from the testing centers. We used Amazon Kinesis Data Streams to ingest data from IoT devices placed at the testing centers. These devices captured data such as the number of people in line, the time they spent in line, and their temperatures. This data was then stored in Amazon S3 for further processing.
Data Processing:
Once the data was collected, we used AWS Glue to transform and prepare the data for analysis. We created a pipeline that cleaned, enriched, and structured the data into a format that could be easily queried. This pipeline allowed our client to have a consistent and reliable data set for analysis.
Data Analysis:
With the data collected and processed, we used Amazon Athena to query the data and perform statistical analysis. We investigated the hypothesis that individuals standing in line at testing centers were at a higher risk of contracting COVID-19. We analyzed factors such as age, gender, and time spent in line, to see if there was a correlation between these factors and the risk of contracting the virus.
Data Visualization:
To make the analysis easily accessible and understandable, we used Amazon QuickSight to create visualizations and dashboards. This allowed our client to quickly and easily explore the data and identify trends.
Results:
Our analysis revealed that individuals standing in line at testing centers were at a higher risk of contracting COVID-19. We found that the longer an individual spent in line, the higher the risk of contracting the virus. We also found that gender and age were not significant factors in the risk of contracting the virus.
Conclusion:
By leveraging AWS Analytics services, our client was able to collect and analyze data to investigate their hypothesis. They were able to identify the risk factors associated with COVID-19 transmission at testing centers and take necessary precautions to protect individuals. The use of AWS Analytics services allowed our client to create a scalable and reliable solution that could be easily extended to other testing centers in different locations.
