As opposed to a reactive indication on patient symptoms, AI can provide insights on patients that are likely to react to the vaccination, given their medical history and demographics information. AI can be useful with the vaccine reporting process. For example, public health experts may be basing priority vaccine decisions on county hospital paid claims data, but they may not realize that all of that data is based on people who have health insurance and access to hospital care. One of the key missing ingredients is a government-private partnership rooted in quality data acquisition at the last mile that aligns that data acquisition and utilization process with the data sovereignty of the individual patient. AI can and should help with a vaccine rollout but probably not in the way people are imagining it.

In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.

When Japan won the competition to host the 2020 Olympics in the wake of a devastating earthquake and tsunami, then-Prime Minister Shinzo Abe said it would be a "tremendous opportunity for Tokyo and for Japan to shine at the very center of the world stage." Lauding his country as among the safest in the world, Abe vowed in 2013 that problems surrounding the crippled Fukushima nuclear plant would be resolved and hordes of overseas visitors would see that Japan is "marvelous." Plans raced ahead for new casinos, driverless taxis and a futuristic stadium to dazzle tourists. Yet many of those projects fell into disarray long before the pandemic forced Abe to postpone the games last year. And now just weeks before the rescheduled opening ceremony on July 23, a resurgent outbreak coupled with one of the slowest vaccine rollouts in Asia has prompted even top business leaders to call for them to be delayed again or scrapped altogether -- shining a spotlight on how Japan's Olympic ambitions have deteriorated.

I've spent my entire career looking at data and the world through a scientific lens. Perhaps that's why when observing the vaccine rollout, I discovered some interesting connections between the challenges governments and scientists have overcome and those most enterprise companies face. When we think about the extensive campaigns and mass education that helped build confidence in the science behind the vaccine, the same needs to happen within the enterprise. There is a tendency for employees to dismiss AI on anecdotal evidence. They lean towards their own biases instead of allowing AI and predictive modeling to do its job.

Despite only being early in the year, significant events have already taken place in 2021. Mass vaccinations for Covid-19 have begun around the world, and new strains of the disease have surfaced in the United Kingdom, South Africa and Brazil. For companies, this news has had a direct impact on their ability to conduct business while further placing their pandemic response under the public microscope. How companies are being talked and written about is changing as the pandemic unfolds, and these nuances could reveal more than simply how effective an organization's marketing department is. What if shifts in sentiment could help traders make more informed financial decisions?

DENVER, CO - JANUARY 30: UCHealth registered nurse Karen Nerger administers a dose of the ... [ ] Pfizer-BioNTech vaccine at a mass COVID-19 vaccination event on January 30, 2021 in Denver, Colorado. UCHealth, Colorado's largest healthcare provider, plans to vaccinate 10,000 seniors over 70 during the drive-up event this weekend. The rapid development of the vaccines from companies like Pfizer and Moderna certainly represent amazing achievements. But unfortunately, the rollout of the vaccines has been far from encouraging. The fact is that the U.S. healthcare supply chain is extremely complex and not particularly amenable to quick distribution at scale.

DENVER, CO - JANUARY 30: UCHealth registered nurse Karen Nerger administers a dose of the ... [ ] Pfizer-BioNTech vaccine at a mass COVID-19 vaccination event on January 30, 2021 in Denver, Colorado. UCHealth, Colorado's largest healthcare provider, plans to vaccinate 10,000 seniors over 70 during the drive-up event this weekend. The rapid development of the vaccines from companies like Pfizer and Moderna certainly represent amazing achievements. But unfortunately, the rollout of the vaccines has been far from encouraging. The fact is that the U.S. healthcare supply chain is extremely complex and not particularly amenable to quick distribution at scale.

At a moment when vaccines promise to end the coronavirus pandemic, emerging new variants threaten to accelerate it. The astonishingly fast development of safe and effective vaccines is being stymied by the glacial pace of actual vaccinations while 3,000 Americans die each day. Minimizing death and suffering from COVID-19 requires vaccinating the most vulnerable Americans first and fast, but the vaccine rollout has been slow and inequitable. Prioritization algorithms have led to the most privileged being prioritized over the most exposed, and strict adherence to priority pyramids has been disastrously slow. Yet without prioritization, vaccines go to those with greatest resources rather than to those at greatest risk.