Government Agencies Should Use Science, Data and Technology to Reopen America Safely

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Using science and data to identify new variants, track vaccinated populations, and vaccinate the most vulnerable is a national imperative.

Government agencies have played an instrumental role in coordinating the supply, production, and distribution of COVID-19 vaccines nationwide. But with much of the country expanding eligibility guidelines for who qualifies to get a vaccine, health officials are now tasked with one of their most complex logistical challenges to date: how to equitably distribute doses at scale, while simultaneously tracking vaccinated populations and the spread of the virus.

As millions more Americans prepare to gain access to the vaccine, government agencies must use the most advanced technological tools at their disposal—such as machine learning—to track the spread of new COVID variants and help states reach the right people at the right time This will ensure the final phase of the federal vaccination effort is efficient, effective, and equitable.

Since the onset of the pandemic, government agencies like the Health and Human Services and Defense departments have worked closely with the White House to coordinate distribution plans, prioritize areas of the highest-risk and need and set up the infrastructure needed to distribute vaccines. 

Yet the potential for leveraging data and science to aid this strategy has, in some cases, gone untapped. 

Initial federal guidance on vaccine allocation, for example, did not fully incorporate data on social determinants of health (SDOH)—such as pharmacy and food deserts, lack of access to affordable health care, and crowded or overpopulated living conditions—when determining how states should distribute vaccine supplies, resulting in many minority populations being overlooked and creating vaccine deficits across the country. 

Using science and data to identify new variants, track vaccinated populations, and vaccinate the most vulnerable is a national imperative.

Drawing together clinical health data and advanced technological tools to analyze this information will enable health officials and government agencies to identify communities that require additional resources for vaccinations—particularly vulnerable, low-income and minority populations. These advanced tools will also help health officials monitor health outcomes for individuals post-vaccination, which will be essential to provide insights into how the virus spreads and to help us stay one step ahead of emerging COVID variants. 

Accessing clinical data from insurance plans, machine learning and AI technologies can help health officials identify the most vulnerable in our communities, determine best pathways to reach these individuals, and monitor patients for safety and efficacy of the COVID-19 vaccine. 

Through a public-private pilot project prior to the vaccines becoming available, HHS identified some of the most vulnerable Americans and determined the best ways to reach them. Drawing from insights provided by machine learning technology, which analyzed demographic, clinical, and SDOH data, along with peer-reviewed medical literature on COVID-19 and other coronaviruses, HHS was able to precisely identify the communities with the most pressing needs down to the ZIP code.

This pilot project shows the kind of granular insights that can be uncovered when we marry real-life clinical data with science and machine learning.

With a possible fourth surge ahead, there is no time to waste implementing the best methods to keep Americans safe and healthy.  Health agencies must harness all the technological tools in their arsenal to monitor the spread of the virus, equitably distribute vaccines, and track the vaccinated population as we move into this next—and hopefully final—chapter in our fight against the COVID-19 pandemic.

Gary Velasquez is the co-founder and CEO of Cogitativo.

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