At first, we conducted the search for an air sampling system capable of being used in different places in order to assist in COVID-19 monitoring. After choosing the most suitable and apparently viable model, we seek the improvement to make it automated, able to performing sequential sampling according to demand, and exposing as little as possible the responsible technician to the possible viral load. After that, we held several meetings with the team in order to align strategies for product modeling and prototyping. In the meantime, we discussed about what would be the possible solutions to make the system able to monitor and anticipate possible critical epidemiological events, as well as the selection of databases to be used.

Development tools:

• Milanote was used throughout the hackathon to brainstorm, save important websites, articles, images, videos and for schedule deliveries;
• Autodesk Fusion 360 and Photoshop were used in the prototyping rendering process;
• Microsoft Video Editor and Reaper were used in video making process for the pitch delivery;
• Inkscape was used to make vector drawings, flowcharts and infographics.

Prototyping and brainstorm canvas:
https://a360.co/2TXevY7

https://drive.google.com/file/d/1lmO48F7EeF2HnqvrlXzp9lDg_HP9lnDX/view?usp=sharing

https://www.youtube.com/watch?v=HqRB0zM_Tf4

Looking for the appropriate tools, databases and scientific articles that could help us in the development of the project, we highlighted the following:

• Study published by the Department of Environmental Engineering Sciences in Florida as a review article titled “Collection, particle sizing and detection of airborne viruses” helped us to better understand the possible methods for air sampling, enabling the choice of a viable system susceptible to adaptation for automated and staggered sampling;
• International Space Station (ISS) air purification system "Bacteria Filter Elements" (BFE) inspired us in the selection of the High-Efficiency Particulate Air (HEPA) filtration medium in the system's air outlet;

Regarding the simplified scope of the programming methodology, we summarized the ideas in the following way:

• The data collected will be linked in visualization panels, such as the Heat Zone and may be associated with other bases, such as atmospheric and demographic available by Worldpop and Johns Hopkins University (JHU) for the disease spread screening;
• Socrata Open Data API (SODA) will be used for the construction of client .NET, in the back-end;
• Our data collection hardware will have both electro-mechanical control components and telecommunications devices. We will use a Raspberry Py card to control each unit;
• Considering that the system will function as an IoT network, we have chosen a serverless architecture to take advantage of lambda functions, which will allow on-demand calls, making possible a global and integrated operation;
• Each BioNotus Spinger module will periodically and automatically collect individual samples, which will be recorded in local memory in the data logger model. This datalogger will send to our cloud data lake. Besides that, GPS data, date and time associated with the serial number of each collected sample.