As businesses work to reopen and students return to schools nationwide, track and trace is a critical component to mitigate potential resurgences of COVID-19. The more densely populated a city, the harder it is to track and trace. However, getting urban and suburban areas moving again is critical to jumpstarting the U.S. economy.
Track and trace is a tall order for several reasons, including public resistance to wearing masks, social distancing and allowing third-party surveillance of location and identity through mobile devices. Furthermore, the question of how to prioritize where track and trace should be concentrated and how and when to leverage automation versus human investigators must be answered.
A combination of IoT, community-oriented, psychology-based messaging and big data analytics could be the key to successfully reopening the economy.
How to ensure successful track and trace efforts
No matter how many tracers are pressed into service, the task will be insurmountable without a change in behavior driven by a change in mindset. Currently, there are programs that provide lessons, guidance and proof that behavior can be changed.
For example, anti-smoking campaigns were very segmented, with different messaging for underage smokers versus adults. In both cases, the point was to use social expectations as a means of changing behavior. In the case of drunk driving, ad campaigns were focused on peer pressure and provided a positive behavior recommendation, such as a designated driver, alongside the consequences of driving drunk.
These programs have been successful, but it has taken years — and in some cases decades — for society to adapt to new norms and expectations. The key is to leverage as many different channels as possible as well as using as many unique messages and mechanisms, and combine those messages with big data analytics to determine what is and isn’t working.
Mayors, city councils, county commissions and other government institutions will need to look to local organizations, such as travel and tourism, public health information, licensing and inspection, and city service portals to help spread messaging on social distancing and mask wearing. They’ll also need to leverage existing and new communication channels, such as anonymous tip lines through designated platforms, such as Instagram, online chatbots and toll-free phone calls, to ensure the public’s cooperation and willingness to opt-in to automated programs for track and trace.
This is exactly where big data analytics can step in, if the proper data science and underlying data warehouse can be put into place quickly. In most cases, the ability to rapidly ingest data across multiple city services and their web click streams, transactions, conversations and other communications will require a cloud platform.
The front-end piece to the puzzle
For those individuals that opt-in to having their mobile devices tracked, local governments will need to work with wireless service providers to support tracking location and masked identities as they move from one cell tower to the next, while also using cell towers to triangulate position. Many wireless service providers already have network analytics for per-call measurement data that can be repurposed for this, but cell tower information is only part of the equation.
Identifying locations, location conditions and who is at said location based on their cell phone is the other part of the equation. However, cities have some IoT infrastructure put in place that can be used to support track and trace programs. For instance, existing video surveillance cameras can be used to evaluate social distancing and mask wearing through facial and movement detection algorithms.
This data can also be used in conjunction with network analytics data to review footage for those who have come in contact with someone who has COVID, confirming if they had a mask on and for how long as well as how far they were inside the 6-foot perimeter.
Adding inexpensive IoT solutions can further improve outcome analysis for opt-ins for track and trace programs. For example, on public trasnportation, seat separation can be monitored either by pressure monitors or LEDs with radio frequency signaling to a local Raspberry Pi. This then maps out how densely packed seating is and whether or not people are adhering to seating guidelines. This could also be applied to classrooms, movie theaters, ridesharing services.
The problem of COVID-19 is pressing, but the use of IoT devices and data gives us options and a way to act quickly. The combination of big data analytics and IoT to support comprehensive smart programs gives local governments the chance to get their cities moving again as well as their local economies, while better avoiding potential resurgences of COVID-19 spikes this coming fall.
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