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Industrial meatpacking plants in countries all over the world (USA, Germany, Australia) have all become hotspots of COVID-19 (Link). 

The close proximity in which workers working in such plants, the gruelling hours, the lack of access to healthcare among workers (many of whom are immigrants, refugees and POCs), are all reasons why such plants have emerged as hotspots. This Propublica article talks about the amont of preparation that such an industry has for pandemic flu outbreaks that could wipe out animals, but failed to do the same for their workers (Link). Moreover, our desire of meat (bad for the environment and unsustainable), has resulted in these companies having a tremendous amount of clout which allowed some to go over the heads of local officials as the ProPublica article reports. 

A well publicised Harvard study reported an association between long-term exposure to fine particulate matter (PM2.5) and COVID-19 deaths (Link). Another recent study that consider multiple pollutants found a signficiant association between nitrogen dioxide (NO2), a traffic-related pollutant and COVID-19 deaths, and not PM2.5 (Link).

Air pollution and COVID-19 have intersected in other ways. The decreases in air pollution due to the lockdown were seen as one of the few silver linings of the crisis (Link). Although early optimism has been dashed as air pollution levels have jumped right back up in China (Link) and other places when the lockdown was lifted. Some may say that under the cover of COVID-19, the Trump administration also rolled back several environmental regulations (Link), and it is unclear yet what the long-term effects of such rollbacks will be.

Air pollution is also a carrier of COVID-19 (Link), and researchers have been investigating the transmission of the virus by simulating mundane activities such as speaking in the elevator and even flushing a toilet.

Some of the other ways however, in which air pollution and COVID-19 will intersect are at infrastructure such as warehouses, which we will see increase as more and more people move to shopping online. Already in the recent pasts of the building of massive warehouses have been challenged for environmental justice reasons, as they tend to be built in poor, minority communities and result in heavy freight traffic, which in turn burdens such communities with increased pollution (Link1, Link2). Amazon employees themselves have documented the nature of siting of warehouses (Link), and it is likely to become an even more fraught site of contention as we move forward.

The system was built to serve organizations and individuals with humanitarian goals. The system gathers data from report, reviews, and users and compiles it into comprehensible information to help inform decision-making for humanitarian concerns. Portions of the app also focus on education and technical support for field researchers looking to collect large quantities of data.

The system is primarily used by researchers, scholars, and organizations with humanitarian interests. The app also has functions which would attract users that are beginning research and do not have established connections within the field as the app provides a support system.

The development of Twine was funded primarily through donations from individual investors interested in the data sharing aspects of the software and humanitarian aid organizations who benefit from the accessible data.

Twine provides information and software to set up compatible data collection systems that pool information into the larger system, which the app then makes available to its users. The system also includes a publishing and collaboration aspect which allows groups of people from all over the globe to access the same data and report on the findings together.

Data visualization is primarily determined by the type of data being gathered and the system for data collection being used. The app integrates data in order to both make large quantities of data easily viewable and understood, as well as compare studies with existing data stored on the system.