We witnessed the consequences of a global underinvestment in public health when COVID-19 struck healthcare systems in January 2020. Vaccine developers, manufacturers, and distributors fell far short of targets set by COVID-19 Vaccines Global Access (better known as “COVAX") and the World Health Organization. Had they been met, researchers estimate that 45 percent of COVID-19 deaths in low-income countries could have been averted. [1] That’s not to mention the untold number of deaths in low and high-income countries that could have been prevented if public health infrastructure had the capacity and accessibility to treat everyone needing care.
Investment in public health has spiked in response to COVID-19, but it’s a race against time. Variants and new pathogens with pandemic potential (such as monkeypox) keep burnt-out healthcare systems working without respite. Meanwhile, many communities, particularly the marginalized, lack access to even basic care.
One obstacle to pandemic-proofing public health infrastructure is construction. Expanding the access to and capacity of this infrastructure means building more of it. However, public health construction projects exhibit a strong tendency to run over budget, which helps explain the reluctance to fund them before COVID-19. [2][3] Pandemic fallout has galvanized investors to overcome this reluctance, but the root of the problem remains:
How do construction companies quickly build urgently needed public health infrastructure without bankrupting the very healthcare systems that they are working to fortify?
The answer is artificial intelligence (AI). Not long ago, it was science fiction, but today, AI is helping construction companies strengthen public health infrastructure while staying on budget and managing COVID-19 concerns.
A predictive analytics model uses machine learning to analyze past data and predict future outcomes. Construction companies can use predictive analytics to make more accurate project-cost estimations. Researchers reviewed 27 studies on the use of predictive analytics models for construction-cost estimation and reported that the models exhibited higher accuracy than traditional techniques such as the floor area method. [4]
Predictive analytics can empower investors to fund public health construction by enabling more accurate cost estimation. In fact, Synaptiq has helped a construction client leverage predictive analytics for this purpose.
A machine vision model uses machine learning to interpret visual data and output desired insights. Construction companies can use machine vision to monitor masking on worksites, which can help prevent the spread of COVID-19. A study found that a mask-detecting machine vision model performed 96 percent accurately on real sites. [5]
The Centers for Disease Control advises that “COVID-19 is airborne and spread by respiratory droplets.” Masking can reduce COVID-19 infection risk by up to 65 percent by preventing these droplets from reaching the nose and mouth. [6] Thus, machine vision can protect on-site construction workers from COVID-19 by ensuring mask use.
Synaptiq can attest to the efficacy of machine vision for construction — and not only for mask detection. We developed a machine vision model to track movement on site, alert the appropriate personnel when a shipment arrives, warn supervisors when a worker isn’t wearing personal protective equipment, and more.
You can learn more about that project here.
It’s now or never for the construction industry to embrace innovation. We need to strengthen public health infrastructure and reduce the transmission rates of COVID-19 (and other pathogens). Construction companies want to win contracts and raise their profit margins. AI can serve these goals by empowering construction companies and their public health clients with more accurate cost estimations and healthier worksites.
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
You can learn more about our story through our past projects, blog, or podcast.