In this episode of the Futurum Tech Webcast I’m joined by fellow analysts Daniel Newman and Fred McClimans for a conversation around the technology drivers and considerations around the upcoming and highly anticipated COVID-19 vaccinations and the technology that’s involved in making those vaccines a reality.
We started by talking about the Pfizer and BioNTech COVID-19 vaccine, which was submitted on Friday for emergency authorization. If approved, it will be available to a limited number of Americans in a few weeks. We also discussed the Moderna vaccine, as well as the fact that there are a slew of other vaccines in development around the world, all with different delivery modes, storage needs, manufacturing processes, and the like.
Our conversation revolved around four key areas where technology can play a significant role. These included:
- Supply chain considerations, getting the manufacturing just right, the monitoring and analytics needed and role technology plays in supply chain;
- Distribution of the vaccine, not just in the US but around the world, where different storage situations might be challenging;
- Delivery of the vaccine, the actual dosing of the population, what type of data collection is required for that, what the privacy concerns are; and
- Tracking the progress of people who have been vaccinated, their progress, side effects, etc. and how involved monitoring will be and what technology will be required.
What are the tools that we deal with every day that will play a role here? This is all about technology and rapid digital transformation. In order to get to this point, vaccine manufacturers have already had an abundance of high performance and AI done at scale, data analytics, shared data and analytics across a massive spectrum, then being able to identify who the appropriate candidates are for drug trials. Now, as we get to the point where the vaccines are close to coming to market, there’s even more technology involved.
What tech will really come into play as we begin to distribute this at scale? Short answer: A lot. In fact, this whole undertaking is going to rely heavily on a bunch of different technology, which is pretty exciting.
Our conversation touched on (for starters):
- The IoT (sensors/controls)
- The IIoT
- Sophisticated analytics
- AI layered into analytics using machine learning
We also talked about the math involved in the process of even deciding who gets vaccines, the marketing and healthcare PR campaigns that are going to be involved in this massive undertaking, and so much more.
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These are exciting times as we move close to a COVID-19 vaccine, and for a bunch of tech geeks, equally exciting to think about the digital transformation at work here and all the technology drivers involved in making a worldwide vaccine initiative a reality.
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Shelly Kramer: Hello and welcome to this week’s episode of the Futurum Tech webcast. I’m your host Shelly Kramer and I’m joined today by my colleagues Daniel Newman and Fred McClimans. Welcome gentlemen, it’s great to have you and we are going to dive into the topic today of the technology drivers around the coronavirus vaccine that is on people’s minds today and some of the key considerations that we think are worth thinking about. Before we get into that conversation, however, I’ll give you a disclaimer, this show is intended for informational and educational purposes only. We might mention publicly traded companies. We have lots of opinions and none of those opinions are to be taken as investment advice.
So with that disclaimer, we’re going to dive right in. We have a couple of companies with vaccines. Actually, the Pfizer and BioNTech are seeking emergency authorization for the vaccine today, and they are… it’s looking like that might be available in December, and the Moderna vaccine is also right on the heels of that, and we have had some conversations around this, and we’ve kind of broken this into four key buckets that we think are worth considering right now as part of this conversation. So Fred, you want to give us just a quick overview of what those four things are, and then we’ll go into those separately?
Fred McClimans: Yeah, absolutely. So with these vaccines, I think it’s important to understand that it’s not just one vaccines and one set of technology components that we’re talking about here. There’s the Pfizer, there’s the Moderna, but then there’s a whole slew of other companies around the world that are also developing vaccines that will be flooding into the market here hopefully very rapidly, and the challenge you have there is that each of these vaccines will probably follow a little bit different type of technology, different type of manufacturing process, different distribution and different types of storage and deliver systems in place. So we’ve got a number of different issues here that we’re trying to dive into. I think the way it makes the most sense is to think of this, as you mentioned Shelly, as four discreet buckets, or four different areas where technology could play a significant role here.
The first is in the manufacturing process, so that’s all of the supply chain side. Getting the ingredients together, getting the manufacturing just right, and all the monitoring and the tools and the technology, and the data analytics that we can use to crunch everything that we see in that particular area there. And then the second area is the actual distribution. How do you actually get this vaccine out there? Not just in the US, but around the world where the conditions may vary considerably, and have environments where in one situation, ultra cold storage, such as required for the Pfizer vaccine or minus 70 Celsius, can be achieved relatively easily. But in other parts of the world, that’s going to be a very significant challenge there.
And the third area that we’re looking at here is sort of that deliver, the actual dosing of the population here, so to speak. What type of data collection is required for that. What type of privacy concerns might come into play, and then as a follow onto that, the fourth area, how do we then track the progress of people who have been given the vaccine? Not just between the first and second doses here that are required, but how do we track those individuals that may actually get sick down the road? Or track side effects, because these vaccines are very new on the market here, and we’re going to want to monitor those as effectively as we can. I think with that, from a manufacturing perspective and a supply chain perspective, we’ve got companies around the world. There are companies in Russia, there are companies in China.
The two companies that we’re focusing on here, Pfizer with the BioNTech and Moderna, they have their own unique distribution and manufacturing challenges. While Moderna is made in the US. The BioNTech is in Germany. So I’ll throw that out to you guys. Dan, from a technology perspective, what are the tools that we deal with every day that could potentially come to bear in that manufacturing, that supply chain process here?
Daniel Newman: Yeah, first of all it’s a good background and there is a lot going on. As we’ve sort of seen transformation move at such a break neck speed across all industry right now, and the background, this has really been the moment we’ve been waiting for. We’ve been looking for that technology, which is really what these vaccines are, to be developed, that can be delivered to the population and we’ve got some serious challenges Fred, you mentioned a lot of them. One, of course is the dosing and the volume of dosing that is going to be created versus the number of people who need this. You mentioned and you guys talked about the Pfizer, BioNTech, you also talked about Moderna and they have some very inherent challenges, in terms of the storage requirements, the mobility of this particular vaccine, getting it to the people at the right time.
So that’s why we sort of saw this initial market reaction and the market exploded after hearing about a vaccine, but then everybody sort of stopped and said, “Whoa, this is going to take a minute.” Now Moderna’s much more encouraging, in terms of being more traditional in terms of how it is distributed. I also read about its side effects being a little bit more significant. But both cases we’re hearing now are 95% efficacy. Now I know that wasn’t a question you asked, but it sort of sets the framework for the question that you asked. We have had a huge investment in technology up to this point. Everything from high performance computing in AI done at scale, data analytics that have been put to incredible use, and shared data and analytics across a massive spectrum that we’re able to identify the potential ingredients and compounds that could be used.
Then being able to identify whom the appropriate candidates were for these trials. Now, everybody knows to get to a true use authorization, it’s going to take a whole nother phase, but the EUA can be done in a shorter period of time because of what’s going on in our world right now, right? People are getting sick.
But the way I see it, there’s a couple of technologies that really will come into play, as we start to distribute this at scale. First of all, you see a big opportunity for management of the vaccines itself. I see IOT for instance, being a very significant… everything from being able to manage sensors and temperature control to the distribution. To making sure these arrive in the right place. Second, I do see sophisticated analytics need to be put into place to be able to anonymize, but associate every single vaccine that is distributed to every single candidate that receives it. These are two dose vaccines and that means that there’s going to be this odd period between dosage where people are going to probably feels there’s a level of immunity that we aren’t sure they’re going to have until that 45 day or so period, where the second dose is distributed.
What happens during that period? How many people get sick? What are the side effects? What are the risks and how do we create more scale there? And then the analytics of course, on a continuous and a perpetual basis as this goes from being rolled out to 80,000 or so in the first iteration to 80 million to 800 million. You are going to need a massive database that is able to track all of the results, the outcomes and also not just outcomes that are going to be measurable binary outcomes. I’m good, I’m not good, but your side effect outcomes, your lesser known effects, your… any sort of issues in distribution. Any sort of issues in consistency of the products. There’s a ton of different ways, but I think AI becomes a big layer there, the ability to intelligently sift through data using machine learning to identify anomalies and present those anomalies to the scientists, to the healthcare community.
So there’s a ton, IOT, AI are two and then I will tease one other one that I said offline is I do see this as being one of the penultimate opportunities for the blockchain to provide security, the quality of the vaccines being distributed to track the actual chain of distribution. This seems like an opportune moment. Whether or not an IBM or some company gets involved to do that will be a whole nother story. So that was long winded, but I felt it required a context.
Shelly Kramer: But it was the penultimate.
Daniel Newman: Hey, I know you love big words.
Shelly Kramer: Impressive. Now, I think you probably work on that in advance and I like it. I like it a lot. So I want to step back a second and just talk about the math here, because we do have here in the US, I’m sure everywhere, we have people who are excited about a vaccine. We have people that are like, “No way, would I ever let Bill Gates put a microchip in me.” I mean, there are definite vaccine doubters, but the Pfizer… so you’re correct, the Pfizer and BioNTech talks about being able to prevent 95% of cases of the disease, that’s a big deal. Moderna, 94.5, basically 95. Their study, the Moderna study was a 30,000 patient study and one of the things I wanted to note about that is Dr. Fauci, our guru, as it relates to the coronavirus has said that the Moderna vaccine, what he likes about it is it appears to have been protective in the elderly and those in racial and ethnic minority groups.
That’s a really big differentiator I think and pretty interesting, but going back to my whole premise of math, the Moderna has a million… or 1.5 billion dollar contract to provide the US with 100 million doses through Operation Warp Speed, and the contract gives the government an option to purchase an additional 400 million doses. This requires two doses 28 days apart. So managing that is as you said, kind of a big deal. Okay, so the US population is about 328.2 million, all right? So when you start doing the math here and you start… first of all, everybody’s not going to get a vaccine.
Daniel Newman: Right.
Shelly Kramer: This is not in our future. None of the three of us have this in our future. The Pfizer situation, they have said they could produce up to 50 million doses by the end of the year, which is next month and Moderna has promised as many as 20 million within that time period. So it’s just kind of interesting. Pfizer expects to manufacture about 1.3 billion doses and Moderna will make about 500 million to a billion. So when you look at this beyond of course, the United States alone because we’re not the only people who need this vaccine and who will get this vaccine. There is some significant challenges, some significant manufacturing challenges, distribution challenges and then also the process of identifying and figuring out who takes priority and how do we make that happen? And what kind of marketing campaigns and outreach campaigns and healthcare… you know what I’m saying? Just even reaching people and getting people interested in participating. I think that’s a huge deal.
Fred McClimans: Oh, it is. Absolutely. Yeah.
Daniel Newman: I think it’s going to be… and I’ll comment quickly Frank, I know I took a lot. But I think this is another great example where analytics is going to answer a lot of things though. We have a lot of data now. We’re eight months into this pandemic. Nobody expected this to still be happening, but it is. We have millions of patients, millions of good outcomes, millions of bad outcomes and many in between. To know who should get this first, it really is going to come down to the analytics being able to point. We already have kind of as a society decided about risk groups, high risk groups, low risk groups. You mentioned ethnicities, we’ve obviously seen it with age. We’ve seen it with certain preexisting conditions.
Shelly Kramer: Right.
Daniel Newman: That people have and so… of course, we’ve also I think, well… most society agrees about frontline being early accessors to anything that becomes available because of how much exposure.
Shelly Kramer: Right.
Daniel Newman: So we sort of know who should get it first, but I think that analytics in AI could almost pinpoint the exact recipients and which are the most vulnerable and most likely to benefit. And then to your point Shelly, which vaccine do they get?
Shelly Kramer: Right.
Daniel Newman: There’s two now. We got Oxford-AstraZeneca.
Shelly Kramer: Right.
Daniel Newman: We’ve got Novavax, both following near behind. I don’t even know what I’m saying anymore, but the point is we have more coming up, and then I’ll just add this one more thing. We are going to get a therapeutic. This is actually going to be the next big winner, and for parts of society, that may make more sense, especially because vaccines… like I’ve heard a lot from some of the leading scientists, including Fauci about there are risk profiles-
Shelly Kramer: Right.
Daniel Newman: Where vaccine may or may not be better, because you get a point where you’re going to be better off hopefully being able to get medicine. Anyway, so Fred, back over to you.
Fred McClimans: No. Dan and Shelly, I mean those are all great points. There’s so much tech here that comes into play that I’d like to think as you mentioned Dan, that… AI analytics will help us identify exactly who those people are. But the reality of the logistics and the reality of the politics, not to mention trying to figure out how many people are going to take a pass. Is it 10% of the population? 20, 30, 40, 50% that say, “No, we’re going to hold off on this.” And when you think about the whole process here, think about the end result we want to get, say 60, 70% of the global population vaccinated for this. Now, kind of step backwards from that, the sheer number of doses and then you start all the way back at the beginning supply chain where we also… Dan, we’ve talked offline a bit, technology such as block chain could actually come into play here. Allowing you to track the actual ingredients and components that go into the vaccine, all the way through to the end individual that has the vaccine.
So if there’s an issue with a reaction, with some type of unexpected event, you can track those components back, track the supply chain back, track the distribution of that all the way back. So I think that’s going to be incredibly beneficial there. From a distribution perspective, we’ve got to get that IOT cold chain storage process nailed, so that we can actually track what’s the cold temperature of this device, of the therapeutic, the vaccine. Where has it been? Where is it slow in transit? Where is it ahead in transit? Because when it actually gets to where it needs to be, there’s I think about a 14 to 15 day window where it can be used.
Shelly Kramer: Right.
Fred McClimans: And once it pulled out of that deep freeze, you’ve got five days to actually deploy and distribute and dose that vaccine. So without that five day window being flexible here, you have a really high probability that some individuals simply aren’t going to be able to get to wherever this vaccine is being distributed.
Shelly Kramer: Right.
Fred McClimans: And you have the risk of overages, underage taking place here. So nailing who that demographic is, that’s a huge issue and where that should be distributed, multiplied against what the political realities are and what the acceptable I’m going to take this reality is here. But then even in that whole process, how do you secure that? I mean, we know that IOT and cold chain will be an important element here, but once you actually get to the point of saying, “We’ve identified X number of people in this area who are going to get this does.” How do you protect that data? How do you gather the data? How do… is it tablets that we throw out there? Are they smartphones that you’re bringing all this information in? Is there some type of tracking ID that each individual gets associated with this? I mean, those are some big questions that nobody really seems to have fixed answers for yet, other than we have a lot of options to explore.
Shelly Kramer: When you think about feet on the ground and really, this challenge that we’ve had across the United States with contact tracing and people being comfortable or not comfortable with all things related to contact tracing, and all the data that I’ve seen around willingness to take a vaccine has really landed at about 50%. So 50% say no flipping way and 50% say let me get to the front of the line.
Fred McClimans: Where have we seen that number before?
Shelly Kramer: What’s that?
Fred McClimans: I said where have we seen that number before?
Shelly Kramer: Right, right. But the other thing is that we are… the United States in particular, we are so highly politicized right now. So then when you start thinking about we have issues with different states reporting information to the CDC in different ways. And some states being incredibly invested in public health initiatives and other states not so much. So a lot of it has to do with leaders, whether it’s… like for instance, in the state of Missouri, where I am. We have a governor who kind of resists all efforts at doing much, but we have local leaders, mayors and so forth who are really taking up… and I also know a lot of governors have said, “This isn’t my problem. Y’all have to figure it out.” So there different philosophies, but then you talk about the effort involved in a vaccine initiative, it’s really overwhelming a little bit to think about that.
Then I’ve also seen comments recently that this is kind of a rich person solution in the sense that people who live in rural communities and not really so much around the cost of the vaccine, and who pays for it, and what it costs, and all that sort of thing, but it’s just like people who live in cities I think are often considered the first contenders for this kind of thing, as opposed to people who live in rural areas. So as crazy politicized as we are already, then when you add this into the mix, and the people who don’t want to take it, the people who do want to take it, the different locations, how states treat this. I mean, this is a huge puzzle to try to put together.
Fred McClimans: It is, but… you said something there Shelly that kind of got my attention about the data and the different sources in here, and let me throw this back out to both of you. Is this a task that’s ideally suited for some of the companies out there that specialize in data ingestion? Different types of data, different sources of data, some structured, some unstructured. Is that really an opportunity to kind of bring in all this data? Except for the fact that for the moment that it’s not going to all be in the same format. Is there an opportunity there for some of those vendors out there to be used as that ingestion tool and just analyze the heck out of that data, you know? Sort of in a data like type perspective.
Shelly Kramer: You know, you said something that… my answer has nothing to do with what you just said, but you’re married, I know that you understand that, okay? Well, we all understand we don’t listen to our significant others, right? But do you remember in the early days of the COVID-19 crisis? When there were massive databases created, I saw this a lot in New York, but databases created of people and companies who could help, okay? Whether it was 3D printing companies would could provide PPE to frontline workers or whatever, but there were some really big efforts that were… that got underway where companies could volunteer their efforts to be a part of… doesn’t mean they did it for free. I mean, sometimes they did, sometimes they didn’t, but I’m just saying I’m just feeling like something like that, some kind of huge massive volunteer of player… technology specific players in this space who can contribute and having it be sort of a meeting of all the most brilliant minds across the technology space, I think that might actually have to be part of the solution, you know?
Daniel Newman: Yeah. If I can chime in, there’s some of that has taken place. Like NASA and Oak Ridge and some of the world’s leading laboratories have partnered with the likes of Microsoft and Vivia, HP, IBM and all these companies did this high performance computing consortium, came together and basically said, “We are going to share this data. We will share.” They chained together all their compute resources. I mean, there were some crazy things that happened at a citizen level of course, as you kind of suggested there. I mean, the Nvidia gamer community basically took horsepower of all their global GPUs and created a network of them to derive additional compute power, which is a great example of the human condition being used as a positive, instead of-
Shelly Kramer: Right.
Daniel Newman: A negative outcry on social media. To specifically answer too as well Fred, I mean, this is all really achievable by the technology that exists. The data lake, you’re talking about a common data model, okay? So the first thing that needs to happen is we need to create some consistent schema of data that we need to track on these people. That allows for these hybrid cloud sort of interoperability that exist between multiple hyper scale clouds and applications that run in these clouds to be able to see this data. This is also a perfect example where you want to geek out a little bit, but you talk about containers, right? You talk about how microservices and using architecture for Kubernetes for instance, that could allow applications to flow between clouds, on prem and off prem, because you do have compliance to deal with here.
Shelly Kramer: Right.
Daniel Newman: You have a huge compliance hurdle where now you’re talking about personal medical data, which now anonymized or not, there are big implications with taking all that data and making it widely public.
Shelly Kramer: Right.
Daniel Newman: Then you got to figure out how do we make it anonymous to use for… because there’s kind of two things here. There’s the public consumption of data, right? When we look at our state indicators about COVID-19, we don’t have a clue who any of these people are. We know how many are in beds and that’s very safe, but behind every number, there is a human. Behind every number in a hospital, in a ICU, in a bed, in every positive infection and every negative test, there was a person and that data also has to live somewhere and be compliant and usable. And they need to be able to associate that back to a person to be able to do the types of tracking and the type of validation, and provide the up leveling of that data in some way that it’s going to help societally, and then it’s going to scale.
And by the way, as a husband of many years, going back off topic for just a moment there. I do think there are two really cool things I’ve read a lot about going on, one is vaccines in pill form, in the case of there’s a company called Vaxart. There are companies out there that are actually working on taking these vaccinations and making them in pill form. Logistically speaking, that would be monumental.
Shelly Kramer: Wow, absolutely.
Daniel Newman: And the second thing by the way, because you did mention, I didn’t get to comment on it when you guys talked about it is the politics of it. So here’s a science question, maybe a quick change of direction as we sort of wrap this, come to the end of this topic is… we talked a little bit about identifying people at the forefront that would benefit from this vaccine the most, you know? We also, I think as a society know there are these certain conditions and these subpopulations that are super at risk, that have the biggest chance and obviously, there’s a problem with our children where they’re not as likely to be problematic, but they are very likely to get those people that are. So that’s a problem.
But I kind of wonder sometimes when I hear about the need to get to 60, 70% and this is just a question. I’m not saying aside, I don’t actually know the answer. If we can get that five or 10% that is really vulnerable. The people that are determined through data that could likely have bad outcomes, couldn’t we… I just would think it would be so intelligent to expedite the delivery of this vaccine to that portion of the population. And then obviously you go to next level and next level and next level of risk factors, because it’s not like it’s two. It’s not binary like one, you’re going to die and the other you’re going to live.
Shelly Kramer: Right.
Daniel Newman: It’s like one, you’re going to possibly, two, you’re probably going to get really sick, but you may survive. Three, you’re going to get pretty sick. Four, it’s going to be like a bad flu. Five, it’s going to be a cold and six, you’re going to basically be like my kid, who had a sniffle for two days and is better. We need to go to one, to two, to three, to four, to five, to six and that way, you could maybe get this rolled out where it’s much more effective. I just haven’t heard anything like that. What am I missing?
Fred McClimans: I hear about that and there are a lot of conversations about prioritizing, but there’s no common consensus on what that prioritization really looks like and even you could, how do you identify all the individuals within a particular geographic region? Within a state, within a county, within a town that are the people that need to get that first. To meet that prioritization list, because every time you go down that path, especially when you start to get into more suburban or rural areas, the logistics of bringing everybody together there can be just overwhelming. Not to mention that even from the perspective of saying we first want to get it to the most vulnerable, or there’s a big argument or discussion there-
Shelly Kramer: Who is that?
Fred McClimans: You get it to a… who is that? But then also, you get it to the medical providers first.
Shelly Kramer: Right.
Fred McClimans: Is that sort of the airline approach to the oxygen masks? When the masks drop, you put the mask on yourself and then you deal with the people next to you, with your children, your family and so forth. So do you follow that approach? I’ve also seen people talk about the opposite of all of this saying, “What we really need to do is to make sure we get the vaccine to those people who are most likely to spread the coronavirus.”
Shelly Kramer: Right.
Fred McClimans: Not necessarily get sick-
Shelly Kramer: I mean, that’s to your point too. I think that you were kind of making this point as well Daniel. I mean, we have kids and there’s so many reasons that kids need to be in school, right? And really, having to do with letting parents work, right? To keep the lights on and to keep kids fed, and all that sort of thing. So there’s so many reasons kids need to be in school, so when you think about that and the fact that they don’t get as sick, but I think we pretty much know that they have the capacity to be super, super spreaders, you know? So making decisions around where it is you start, you know? Are those kids more… and no human life is more valuable than any other human life, okay?
I know we all agree on that, but when you look at… so when you look at nursing home population compared to young kids, who’s most vulnerable? Well the most vulnerable are people who are in nursing homes, right? They’re the most likely to die if they get it, but are they safer where they are? You know what I’m saying? Or is it kids that are so dangerous? So it really becomes this big societal question as to-
Fred McClimans: Oh yeah. Now to throw in there as well, portions of our population that are traditionally underserved by medical resources.
Shelly Kramer: Right.
Fred McClimans: The differences between the different racial profiles between different age segments, so many different considerations in here that you kind of have to step up and say, “Look, we have to make sure that his population gets taken care of, because they disproportionately get sick and perhaps even now the disproportionately high rate of death as an outcome on this here.” So all those factors need to come into play, but I think that the key in all of that we’ve kind of been talking around here is data.
Shelly Kramer: Data.
Fred McClimans: Gathering as much data as possible and being able to analyze that data. Not just upfront to figure out where do we distribute the vaccine and what’s the best approach that we can use that is both fair and gets the fastest best outcome for us here. But then on an ongoing basis, making sure that we capture that anonymized data, making sure that we can process it and derive some really meaningful insights, because we know we’re going to have to adjust this plan as everything moves forward. And then of course to make that all happen, IOT technology to track the vaccines all the ability through to make sure that in transit, in storage, in an application, they’re used the right way and nothing is spoiled, nothing is wasted there.
Certainly we talked about block chain. I think big element there. The containerization of applications, so they can slide easily between on prem, off prem, in different clouds. Dan, I think that’s going to be huge for all this tracking here. Not to mention, all the edge computing that is likely to come out of this as well, because we don’t necessarily need to gather all this data and send everything back into the cloud into some giant government repository.
Shelly Kramer: Right.
Fred McClimans: Very likely that’s going to have to be processed quickly at the edge to make sure that we’ve got meaningful insights and then those insights then propagate through the system. That’s my take on it. I’d love to hear from each of you, what are the key technologies that you think are going to be essential to making this vaccine roll out work? In the next 60, 90 days, year, however long that happens to be.
Daniel Newman: I think we’ve sort of come to the consensus there. You did definitely hit it on the head and we’ve sort of said leave a supply chain story and then you have a distribution story with analytics, right? You got the supply chain that’s going to be heavily IOT, edge technologies, how you’re going to need data and analytics to track potentially block chain to provide some chain of trust on the delivery, and the integrity, the deliver and the integrity of the product by the way, which is something we’ve sort of alluded to-
Shelly Kramer: But.
Daniel Newman: But not entirely. Then you actually have the patient data, the results outcomes, healthcare, emergency medical, and records, you know? The medical records, which is all very complex, a very high compliance industry. Going to require a lot of inputs and a lot of investment, but you put all these things together and you sort of run those two items in parallel, and then of course you got continued improvement. So you got to take that data and put it into the actual drug development pipeline, because again, we’ve developed these in eight months or nine months. You have to remember, these are not done.
Shelly Kramer: Right.
Daniel Newman: Even… these are approved, these are not done. These will be continually refined and improved and this will become a flu shot. In the sense of it will be something that every year, there’s going to be new strains. We’ve already heard everywhere from 50 days to 90 days of the actual time of mutation before you can be reinfected. That means a different vaccine would be required in many ways, at least on a year over year basis, because the mutation may not… the vaccine may handle a few strains where you won’t get sick, but within a year and five or six mutations later, you will. So there’s more technology, more tracking of… at what point did the vaccine become less effective? Did the efficacy go down. What went down when the strain changed and all of a sudden… so these are all tech and I realize we’re wrapping up here, but this is fun stuff, I mean-
Shelly Kramer: Yeah, it is.
Daniel Newman: If you don’t think that tech is just littered throughout what’s going on here in our society with COVID-19, I mean of course we’ve heard about enterprises changing to address customer needs, but the actual delivery of the medical solutions to the market and the creation has a massive, massive tech underpinning. And it’s kind of fun to make that application, and thankful to you guys for even letting me come into this conversation, because I haven’t really thought about it that much. Maybe because I just don’t think that much.
Fred McClimans: Not that.
Shelly Kramer: Not that. No. I thought it was really a very timely conversation and it’s… I mean, I think the cool thing, one of the best pieces of good news beyond the fact the emergency use applications were happening. One was happening today, the other’s happening right away. Fauci was hoping that we would have results that were 70% prevention, so to be looking at where we are now and see that these two in particular are at 90% effective, that’s a pretty big deal I think. It makes it… and I think that we need exciting news every now and then because yesterday, there were 185,000 new cases in the US and 2,000 deaths in one day. That’s a lot.
So that’s the bad news. We need some good news and it looks like we have some good news ahead of us, and I think that as tech geeks, all of this stuff is so exciting. Just being a part of this undertaking must be so exciting for the people who are immersed in this, you know? I’m exciting just thinking about it, but just trying to figure out how to make all these things happen, I think is really must be stressful and exciting.
Fred McClimans: Yeah. I think the one big thing that we’re… I’m kind of hoping to get out of all this too is the whole aspect of digital transformation and agility and adaptability of systems-
Shelly Kramer: Everything personified right here.
Daniel Newman: Oh yeah.
Fred McClimans: Right here and we’re going to learn a lot from how this has rolled out that I think will benefit every business, every company out there, every society, not just today but hopefully for years down the road, the lessons that we learn here.
Daniel Newman: We all have places to be, so let’s go. Let’s go. Let’s go.
Shelly Kramer: Okay. Well thanks everybody for hanging out with us today. It’s always a pleasure. Dan, thanks for making time and hanging out with us as well. We don’t see you as much as we would like and it’s always a pleasure. And with that, we’ll say we’re out and have a great rest of the day.
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