Data Analytics and The Response to Covid-19 with Dr. Melissa Whiley Hosted by Jim Frazer

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0:03

Welcome everyone to another edition of ... Smart City podcast. I'm Jim Frazer, Vice President Smart Cities here at HRC Advisory Group. And today, we're very happy to be joined by doctor Melissa Wiley. A data scientist who's going to enlighten us, not only about data analytics and data science but also shed some light on the application of data analytics to our world, of the covert 19 virus. So, Melissa, welcome today.

0:35

Thanks, Jim. I really appreciate you letting me come on and talk a little bit.

0:41

Can you tell us a little bit about your background?

0:44

Sure. It's fairly motley. I do consider myself a data scientist, not a medical doctor, of any sort and no medical training.

0:54

So I have spent some time in bio, informatics and biostatistics, I'm kind of coming at this from various angles.

1:05

I recently had brain surgery at Mayo, and I also have a very close friend who's only 39, who has Stage four terminal breast cancer.

1:16

That's a good bit of time with the National Children's Study, and it SQL, the Environmental Child Health Outcomes, funded by NIH, and have done a good bit of work, community based participatory research with the Marshall Aid in north-west Arkansas, who happened to be the victims, for lack of a better word, a Manhattan Project, and nuclear fallout.

1:41

Many of them passed away during this time, and don't have a lot of insurance, keeping up with what's going on, and using what I know about data science to hopefully make a difference.

1:56

That's great. That's great.

1:57

So just, Foundationally, you hear a whole lot about derive business intelligence from data.

2:06

And what's, I think, implicit there and foundational is analyzing that data or more formally data analytics. So what really is data analytics?

2:16

Well, I'll be clear, a lot of people.

2:20

Confuse it with profit. And think, well, if we have no business intelligence, is going to mean more money for our company.

2:28

But that's not what it's about.

2:31

Foundationally is the science of analyzing raw data to make conclusions, so, it's different than analysis, you know, data analysis, or statistical analysis.

2:43

And the respect that you're trying to find a conclusion, and some types of analysis, you may just be looking to describe, or investigate, there are actually four types of data analytics: Descriptive, can be you, but you need to have an a priori conclusion.

3:01

There's diagnostic where you're trying to maybe run a root cause analysis, figure out what's causing something else to occur.

3:10

There's predictive, which we're all pretty familiar with. We want to forecast the future.

3:16

And then there's prescriptive, where we want to find out what's going to help a certain situation.

3:24

But really, what Data Analytics do is help businesses and organizations optimize performance to have the data to make better decisions and to innovate. They're not just rely on hunches or the experience of those who work for that particular company.

3:47

But data, and that data can drive many solutions.

3:52

We hadn't thought of before.

3:56

Couple that with machine learning. And it's pretty magnificent!

4:02

Well, one of your roles is leading the data analytics effort for a city in Florida. So, what, what's the impact, and the use cases of data analytics for first, cities, and then, you know, and in this new term, Smart cities?

4:22

Really, we will try to take feedback from the citizens with carbon neighbors, and draw on that, as well as best practices. And data that we have access to, whether that be federal, regional, local, to be able to understand the implications of various options, do some scenario based planning, and come up with they.

4:48

The option that the data indicate will have the highest benefit with the lowest risk.

4:57

I also serve on the alachua County Healthcare Advisory Board Within, you know, a number of leaders with the health department and the local medical facilities.

5:09

And, so, I do get some of that, that medical angolan discussion.

5:14

But, I do, I mean, I'm very active in the field, and have, you know, went into the field. Because, I always wanted to know the answer to everything.

5:25

And data will really allow us to do that.

5:30

Um, um. Let me go back to the four types of data analytics for a moment.

5:38

Um, so can you give us an example, you went over those for a little quickly, but that's an example of a descriptive Data Analytics.

5:47

And then we'll go through each one of these because I think this is fascinating, right, and if we apply it to the pandemic, which is on everyone's mind, would be surveillance.

6:00

Understanding how many people have contracted the virus? How many people have died from the virus?

6:07

You know, what type of underlying conditions predispose them to being susceptible?

6:15

OK, that's number one. We have Diagnostic Agnostic, so that would be taking coven, and then looking at various treatment.

6:26

Looking at medications or respiratory implements, maybe it's just more or less. wait and see or, you know, sometimes putting in an innovator.

6:45

Incubating someone can make them more sick, you know, so they had to take a lot of characteristics of patients into consideration to effectively diagnose the best treatment for that person.

6:59

That's called precision medicine, OK, And then we have both predictive and prescriptive. Can you give us a, an example of each of those?

7:10

We've all seen a lot of predictive in the news, Very modeling has has occurred with their initial cohort of outbreak in Asian countries. And so they use those models to say if things stay the same.

7:26

or if they follow certain parameters that are set a priori we expect, then there's many people to come into contact with the virus.

7:40

So we're predicting it within a certain range of competence. Sure.

7:49

Then prescriptive, I think we know all fairly, fairly well.

7:53

Sure.

7:54

Yeah. Really?

7:55

Just be, Well, how did, how do we get rid of this?

8:00

What do we do now? Right. Now, I don't know. I'm sorry if I interrupted you, but we were in it. When I asked for those four, we were in the midst of discussing, you know, you know, what do data analytics do for for smart cities and public agencies?

8:18

Well, data analytics and technology are really the cornerstone of what makes the city smart.

8:26

It's really the technology and the people and the combination of those two to be complimentary data for good, smart cities are able to better manage mobility and sustainable food supply.

8:44

Many, many things, you can speak on that much longer than I I can if we even think of social media, you know, and its ability to disseminate a message faster than our generation ever thought possible.

9:00

I will bring up the fact that I had a party line growing up. His, most people won't know what that is.

9:08

But, you know, it helps cities be connected, be holistically connected, so that they always have this sense of what's going on right now that can help public safety make better predictions about when they may need additional fire trucks, or police, effort's, things like that, that can really help protect the safety of citizens.

9:40

The purpose is to use it for smart things in the lives of people.

9:47

So, some, Melissa. Since we're discussing data analytics, let's move towards using examples. Well, perhaps the most interesting example we have today, which is the world of covert 19. How does data analytics, you know, in your, in your experience, given, given your respective roles? How does data analytics been applied to this new covert 19 ecosystem we all are, unfortunately, living through.

10:17

Well, in some cases, it has been applied in another case. It has not. We will talk about that a bit later.

10:26

But when we're talking about technology, particularly in tandem with virus, they have one significant thing in common.

10:36

They both have exponential growth.

10:39

We've seen that with technology. And that's what we know, to be true about viruses.

10:45

It's no one person can infect six people who can indict. Yeah?

10:50

Right? So on and so forth.

10:53

And technology has been the same the same way.

10:56

It was really quick between, gosh, I guess a satellite dish and then a mobile phone.

11:04

No!

11:06

Given I, but for as an example, exponential growth curve Texas, when the states, along with my home state, Florida, that are the epicenters of the US. Pandemic at the moment, they recorded almost 4000 deaths.

11:27

Since it started, nearly 20% of those deaths were reported in the last week.

11:34

So, the longer it goes and the more we pull back efforts.

11:40

The only efforts we have that are down to, uh, real back containment, you know, better sustain containment efforts.

11:52

The quicker it's going to go.

11:56

So, it's really interesting that, I mean so we can look at this as a catastrophe or maybe an opportunity because the pandemic may permanently normalize the comprehensive societal use of digital technologies.

12:14

We may kind of, maybe, at the tipping point, where things that have been difficult to grasp onto and make everyday business.

12:30

Well, what do they say?

12:32

Necessity is the mother of invention.

12:34

Orwell's in this case adoption.

12:37

So technology has the ability to scale up traditional epidemiological methods and if we can do some of those taking things that people are afraid of, Buda privacy. It offers a way to relax.

12:52

Some of the non medical interventions or non technological interventions, such as locked down or social distancing, you know, without sacrificing the safety of the citizen.

13:05

And I think we've seen very clearly, during the, the lockdowns or the shelter in place, orders that have occurred, the economic progress is absolutely facilitated by technology.

13:22

And if we look at the stock market, the most of the stacks that are going up and down, and, you know, the economy hasn't completely crashed, is investment Technology Related organizations, berms Corporation.

13:40

So, you know, we've seen the transition from working in the office to working at home.

13:45

That would not be possible without technology like Zoom or go to your meetings various betters.

13:54

that same thing has occurred in the educational system.

13:58

Allowing students to continue to learn something, that can bring something in, have something to be occupied with and meaningful.

14:11

Then, we've seen social media.

14:15

Everybody's got it now, I mean, it's A You can have a captive audience if you have a strategic message.

14:22

And, I think, I guess, the Trump rally in Tulsa, where Tiktok users bar up all the tickets.

14:32

And, you know, I'm not familiar with the granular details, but we're able to, you know, make a statement with social media.

14:44

And then, my my favorite, fortunately, it's Public Service Delivery.

14:51

You know, I'm very grateful for the folks who are able to bring groceries and booed.

14:59

Various other items. That has been a lifesaver for many of us medicines for the elderly and others.

15:06

And it's allowed us to better forecast supply chain issues that may come out and balance it with the demand. Then, you make some alternate choices on suppliers and locations, things like that.

15:21

So, the reason we're kind of hanging on right now, it's because of technology.

15:28

Great.

15:29

So, so, Melissa, as, you know, in your role, as a public sector data analyst, what are you learning about the novel coronavirus? You know, what, what's being revealed in your datasets?

15:46

Well, as I've had to do research for the city and for the county health care advisory board and my friend and I, and I have learned a good bit about it. But there's a lot we don't know about it.

16:02

We do know the route and transmission and it may be spread.

16:10

You, maybe, Eric? Earl aside.

16:16

Yeah.

16:17

Um, the incubation period, we're pretty familiar with it, 2 to 3 weeks, which really compounds any sort of containment efforts.

16:29

People can be asymptomatic.

16:31

It'd be walking around, not knowing they're positive for awakes which puts them into contact with lots and lots of folks.

16:39

And there's a wide breadth of symptomatology every day. A new symptom comes out, and you're right. If you have these two symptoms, it's a sure fire way to know that you have pivoted and because it does attack the body's immune system.

17:00

Which controls everything, it's ran through the lymphatic system.

17:05

All organs can be impacted, we're seeing more cardiovascular, manifestations, neurological, manifestations.

17:15

And of course, this has a much higher risk for those who already have some underlying conditions.

17:24

Sure.

17:27

But, you know, we do know that technology is helping us with this so far By being able to predict who might be the most severe case, Who's going to name some of those ICU resources, and who may not, just by semi-desert, utilizing some of those risk factors and using predictive modeling. Predictive data analytics.

17:54

So, we know some, we know enough to manage, but not yet here. So some of those predictive models are, are, in fact, being built and used today.

18:06

They aren't, yeah, the medical field is using them in more, so than the, the rest of it. Sure, sure, Sure, What else?

18:15

Again, so Melissa, again from your perspective then let's ask the other side of the of that equation is, know, what we're what unknown unknowns do you have, and what unknown unknowns could you venture a guess about?

18:32

Yeah, there's so much that we don't know and that's why they call it novel.

18:38

We still don't know where it came from.

18:41

I don't know how important that's going to be going forward.

18:46

We don't know the comprehensive symptomatology like I mentioned.

18:50

It's hit or miss.

18:53

Like, children have had unusual symptoms, such as GI tract issues or multi system inflammatory syndrome.

19:04

The adults are not experiencing, we don't understand the individual level of susceptibility.

19:11

We've seen really healthy athletes in their thirties or forties die.

19:17

And we've seen, you know, a woman who has a 103 live to tell about it.

19:23

Indeed.

19:24

We don't know the length of the antibody activity. So it may be like the seasonal flu, where we can get it over and over.

19:33

We don't know how it mutated there.

19:39

Last, I saw there were about 4, 4 to 5 different strain at it.

19:48

We don't know the exact mechanism of injury for any one person.

19:54

You may get it in it.

19:56

You may have something an underlying condition such as cardiovascular disease, but it may attack your kidneys, or your lungs or your your brain instead, which we just don't know.

20:09

We all know how environmental influences, such as weather, air quality, certain features and housing, like air conditioning or access to no supplies that were built without heavy chemicals. We don't really know right now and how that's how that's factoring in.

20:35

So, OK, so, you know, the world is attempting to learn what we I don't know, what barriers are there and how could data analytics overcome some of those barriers?

20:51

Well, the biggest challenge is time.

20:55

We won't know a lot until we have historical data to look back on and analyze and make future projections, and right now, you know, it's just small pool.

21:10

But what was one of the biggest thing that we lack?

21:13

that is completely remedy of all is accurate comprehensive data systems, you know, for a state or for a country that use consistent methodologies.

21:28

You know, we've seen that every state or region or country seems to have a different way of measuring even percent positive.

21:41

Are you counting the number of test taken in counting unique individuals who have taken a test?

21:47

Are you counting duplicate, or, you know, ICU beds has been another.

21:52

Most of the methodologies are not consistent, so there's no data standardization, no harmonization among systems, which leaves error in the predictions in the analysis.

22:08

Very much so.

22:10

Then, you know, unfortunately, we've seen some misuse of data, um, in part, partially that can be due to the lack of widespread acceptability of predictive modeling and data analytics capability, to make human lives better.

22:32

And so, sometimes it's misused, because you don't know how to use it, and sometimes it's misused egler reasons No.

22:43

And we're we're seeing some pushback on some of the interventions, particularly non medical interventions that have been shown to work, because there is a mistrust between government entities and especially historically underrepresented groups.

23:05

And so, that's going to continue to be a challenge, I believe.

23:10

It's, it's interesting that you're, you're, you're absolutely correct. Clearly there's a lot of stakeholder communities that are all pulling in many different directions on all of this data. I think one of the earlier when you referenced that the datasets are it.

23:31

are poorly defined. And as a result, they're really they are not easily integrate a ball or interoperable, let's say. Right. If we know, I would argue and probably so, would you that if we had standardized datasets and data end?

23:48

Um, Standardized Duc, documentation processes.

23:54

Then, all of this pushing and pulling in various different political and business directions probably would be lessened.

24:03

Yeah, agreed. Agree, there's so much misinformation.

24:07

And people are getting every side of the story all 27 day, or, you know, people just don't know what to believe.

24:16

And then to make that worse, there aren't many cultural or linguistic translations of the information materials that we do have the accurate ones, so that we can be sure and reach our entire community.

24:34

I would really encourage people, when you're on social media, before you re post something or, you know, start commenting and get on a bandwagon, you need to check the, the validity and the reliability of the source. It's always best to rely on peer reviewed research articles when you can.

25:00

Or government agencies like CDC or organizations, professional organizations are a good go to.

25:11

But, you know, if it came from the onion, please don't pass it on.

25:19

Right.

25:21

So, Melissa, more broadly, In the datasets you work with, we've, we've touched so far today on all the really core coronavirus datasets.

25:35

ICU beds, who's infected, Who's not, how many tests have been delivered, how many people have died? But I can imagine in your, in your daily work, there is an ecosystem of related datasets, because there are other impacts.

25:53

If you, if the hospital, is overcome with coronavirus patients, well, then some other people might not get treated.

26:01

Or, you know, that, all of this is inter-related. The more you're, I mean, you know. We know that alcohol consumption has greatly increased during this period. For example, Can you comment about about that a little bit?

26:18

Yes, sure, But I would like to piggyback on what you just said and really emphasize, that we are an eco system, that every action has a reaction, and we're all relying on each other for the greater good to get through this mess. But, some of those, some of those non medical things that we're seeing, are mental health, you know, with children and adolescents who are used to going to school and having a much different routine.

26:48

We're seeing it all with front line, healthcare professionals. Burnout is extraordinary. They're putting everything on the line, and often don't get a break, because they know the medical system has been so over inundated.

27:04

We're also seeing a spike in domestic violence hotline calls and requests for online online service providers.

27:14

Unprecedented precedented demand for those things, because people are at home together.

27:20

And maybe if you have a large family or things, just, we're kind of rocky from the beginning.

27:27

This is just going to magnify those issues.

27:31

We are seeing really of medical crises for vulnerable populations such as cancer patients, pregnant women, transplant patients during the process or just recently received a transplant.

27:48

Those folks are really, really susceptible, and not being able to go to the doctor or receive treatment has been, no, a significant downside.

28:02

And many of those professional organizations are calling for emergency effort's guidelines, different mechanisms to see cancer patients, that the professional organizations and the local groups, grassroots efforts, seem to be taking off rapidly without a centralized, unified message and strategy.

28:31

You're looking around to see, This is my community. You know, This is relatable.

28:36

I know these folks, and we're just seeing a lot more people taking charge from the bottom up, which is somewhat unusual.

28:48

It is, so with your access to to this data, and the end, the dependent, you know analytics that you extract, or intelligence you extract.

29:01

Um, what does doctor Melissa Wylie recommend as best strategies for? Not only you know, personal, but also your, you know, your family, your neighborhood, your community.

29:16

Well you've heard you've heard the most popular ones before and they are valid, social distancing wearing a mask is that is our best chance right now of getting the virus under control.

29:29

You know, without a comprehensive, unified centralized strategy, washing your hands often.

29:37

And recent research is showing that intermittent shut downs does not stay shut down for a long time because it actually, you know, eventually, does it save lives?

29:52

That during intermit lay on, the latest I've seen as a 5 to 1 ratio, you would stay in one day, go out five days, day and one day.

30:04

But, we all have to work together.

30:06

Really, That's the biggest message I can get out there is that there's not really an in-between.

30:13

It's like the era of, no, compromise in a way.

30:18

You know, if you say, you live in a home with a couple of other people, if you socially distance yourself and take all the precautions, but they don't. It doesn't matter what you've done.

30:31

So, it's gonna take all of us acting as one large family to find our way down this path, I think.

30:41

No, I couldn't agree more. I mean we we both live here in Florida, me down south and you're up in Gainesville.

30:48

And, you know, I was struck by when the lockdowns basically ended here in Florida, in the middle of June.

30:57

I recently saw a heat map, You probably saw the same one, that, you know, a week or two after, the lockdowns ended.

31:06

The younger folks, say, in the 20 to 24, or more broadly, 20 to 30 year age range, represented the highest increase, in cases over that next 1 or 2 week period.

31:23

But looking at the, at the map, that heat map over the success's succeeding weeks, we see that that rapid increase didn't happen as much in that, can in that age group. It started being transmitted up towards higher age ranges.

31:40

And, I mean, I'm not a data scientist, but I would surmise that many 20 to 24 year olds live with people that might be their parents, or maybe even their grandparents, right?

31:50

And we know that college age kids and adolescents and even young children on sports teams and ballet and things like that, much more social than adults, the majority of the time anyway.

32:05

So when they went back to doing things that they normally do, there just really wasn't that thought, that no, because they're not and at a high risk for fatality.

32:17

But yes, they are the largest transmitters at variance outbreaks to while they may have mild symptoms or be completely asymptomatic. They can still pass that on, right to grandparents or other folks in their house or their community that had compromised immune systems.

32:39

Sometimes people don't even know that they have a compromised immune system, and then you find out the hard way.

32:47

Indeed.

32:48

So, um, so, Melissa, let me ask more for your opinion rather than information you've generated from data. It seems there's been so much written about different countries and their different responses.

33:04

Sweden did great, and then till they did it, You know, other countries are like that, too.

33:10

So from what you have access to and what you've read, and perhaps even some datasets you may have received, can you comment about what other countries have done, what we're doing, what the US is doing?

33:27

Yeah.

33:28

You see a developer Absolutely like some things that I have noticed and exploring the world.

33:36

I think that the countries that are most successful and containing the pandemic, our does that already have no widespread surveillance mechanisms. They already had a disaster plan in place. They were able to respond much quicker, fake it very seriously, like South Korea is an example.

34:01

You know, this isn't their first outbreak so they have a history that necessitated preparation and they've done a wonderful job.

34:10

Germany is another country doing quite well and again, they these countries that are successful are having more collectivist culture where they believe in the greater good and things aren't so polarized with respect to politics or religion or various other beliefs.

34:35

And so some of them have just learned from historical and situations are older countries and they've they've learned from previous situations and they have prepared for this sort of scenario.

34:49

And then some of the countries that haven't done so well didn't have solid plans in place. Or, you know, the response time is really critical. And so if you lack a couple of weeks or if it's a hoax or if it's fake news or if, it's not really, that, is it? They say it is or what have you that really accelerate you get off the ground running, you know, really accelerate the disease transmission in your country?

35:22

So, you know, here we are in mid July, how are various countries and states fairing? And do you have any clear insights of effective containment strategies or ones that, for that matter, haven't worked?

35:39

Yeah, Doing nothing doesn't work, That's as fairly evident.

35:48

So if we look at Sweden, you know, they did not shut down anything. They never closed goals at all.

35:55

Their rights are rapidly growing.

35:59

So we know that response and containment efforts are super critical. You know, the US was a little low on the draw. And then we were quick to re-open in many of the larger.

36:13

That's the most populous state.

36:17

And so really, the national response is critical. It can make or break the situation. There's an open data initiative called Our World Data. And what is attempting to do is be transparent data repositories that are consistent and standardized.

36:41

Completely transparent, people can donate to it.

36:45

And they have identified countries that, you know, have promising measures for containing the virus.

36:52

And most of those countries have really good health care systems in the first place.

36:58

And they have data infrastructure. In fact, the cities that are on the list are Germany, South Korea, and Vietnam.

37:08

And those would be the countries where we would find some of the, some of the best smart cities in the world.

37:15

So, they've already kind of taken that technological plunge, so to speak, and have the infrastructure to be able to, to track, you know, do some contact tracing.

37:26

And people don't consider it an arrangement of privacy, they consider it due diligence to their neighbor.

37:35

I mean, they don't want to hurt anybody.

37:37

So there's this not, There's not a lot of collective and fighting in these other countries, right. They faire attitude. mm, hmm, hmm, Correct and the US does have that laissez faire attitude, and this is quite divided by, you know, by party right now. And I don't know if that's just the election coming up.

38:02

But many of the states where we're seeing real rapid outbreaks and lack of state mandated masks and things like that, most of those are Republican state, republican led state, I am not really sure what that's all about, Currently divisive certainly certainly is. You touched on South Korea. But how about Germany has been doing quite well in its process, hasn't it?

38:33

Yes, yes, Yes.

38:35

To the lowest rate times for health care in general, there, they've just pretty much perfected that system, and they had early detection and containment that is, what are the things that have made them super successful, but they also have a robust public contact tracing program, OK.

39:00

It's really all about that technology, running faster than the virus.

39:06

You gotta get ahead of it. You got a unified, and, you've all got to be an agreement.

39:12

Um, they have it, you know, they just had a really nice public infrastructure that is very community oriented, so it's, it's not just the most developed countries, though, that are, are let's, we had talked a little bit about Vietnam.

39:31

Can you touch on some of their efforts?

39:34

Yeah, sure, again, they have a well developed public health system and a very strong central government.

39:42

So, again, a proactive containment strategy that was based on comprehensive testing, tracing, quarantining, they got ahead of the game.

39:53

And they were able to, yeah, almost eradicated so far.

39:59

They can they have the tech infrastructure to see what's going on in real-time and to make really great decisions using that data, you know, for good for public health.

40:12

Hmm, hmm, hmm.

40:13

OK, so I think the big question that we're all thinking is, so what is the projected outlook for, for Florida, for the US for the world?

40:26

If we continue the path we're on and don't think creatively to assuage the situation here, there's been a lot of projections.

40:38

And so, you know, they change daily and we, can I go by the models that are most present.

40:47

But, Wellknown academics are predicting that if we continue to behave the way that we are, nothing really changes.

40:58

That ..., all infections, could be either 15 million, 32 million, or 370 million, depending on various timing. Our country's efforts are interventions and the windows for and based on the equator windows for the flu season, right.

41:22

Humidity now seems to be a predictor or does not sure how the weather is impacting it, but we're looking at a lot of deaths, a lot of death.

41:35

I can see I miss about. Now we would have five billion infections, excuse me, and up to 370 million fatalities.

41:47

Most significant disaster that the world has ever seen, since we began tracking it. Yeah. It certainly is sobering that we that, if, you know, antibody immunity, we don't know how strong it is. Or if it even last very long.

42:08

And, you know, if the fatality rate is even at 1%, that is a huge number.

42:17

It is. And we know, you mentioned, there is no gold standard treatment right now. There is no approved vaccine. Anything that they've pushed out is, is still going to be a trial. And we don't know the long term implications of that. We don't even know the long term implications of having had Coburn 19.

42:38

And so that's some risky business.

42:41

But I also often think, you know, along with the psychological impact of staying home and domestic violence and all the other, all those things that, that, you know, come out of this.

42:58

Even if a vaccine was available today, if it was perceived as perceived as being rushed to market, or if there was any political angle to it, there might be quite a bit of resistance, and you might not have Yeah, I mean, I mean, I know a lot of business travelers that say, OK, when there's a vaccine travel, will you really?

43:23

Wouldn't? You might wait a few months to have to see who else has taken that vaccine?

43:30

Um, Some additional disparities in those who receive it, you know, we're still fighting, know a lot of various outbreaks in Africa. So, yeah, yeah. You're a long time ago.

43:46

So, let's, let's, you know, we're nearing the end of our time.

43:50

So let me, at least close with one big question, before any final comments you may have.

43:57

Are there, are there novel ways of thinking, creative ways of approaching this So that, you know, maybe we can surmount some of these obstacles and challenges?

44:13

I think so, I think so, I have some thoughts and ideas and what I do know is they're going to have to be novel and creative.

44:23

We cannot continue to do what we've been doing or had the same paradigm of thought and expect something different to happen.

44:33

I think that our best bet right now without a vaccine and with the healthcare system, overloaded is to contain the virus.

44:44

You know, we get to disseminate real news.

44:46

We've got to utilize the technologies and social media that we have to ensure that accurate, timely information is received by all cultures and languages.

44:59

And I think we can do that by promoting positive messages by, you know, promoting unity and cohesion.

45:06

And we can all be heroes, grassroots heroes, you know, putting it in a positive light rather than continuing to have the various conspiracy theories and the doomsday death.

45:23

We've got to get his pitying different messaging on it and many establishing unique partnerships and devising strategies for behavior modification. Let me back up know who I think one of the largest challenges in your first point that we need to disseminate real news.

45:45

Know, you you watch cable television and, you know, the major cable news networks all have a different perspective.

45:53

It sounds like you're on different planets.

45:58

Here in Florida, we have an Administration and some ex members of the administration that are saying completely different things.

46:09

It's very hard for anyone, whether you pay a lot of attention, or even, or a little amount of attention, to try to rationalize and harmonize what you have there to determine what actually is the truth.

46:23

Well, hopefully, we can begin to listen to experts and scientists and medical professionals that are in the field dealing with this daily.

46:37

We've got two, rely on we need to teach people how to discern ghaemi type data, probably accurate, need to check up on some more.

46:52

Know, and I always ask myself, when I'm looking at data or a news report, does this person have something to gain?

47:01

Bye.

47:03

You know, putting this Spanner, this angle on it.

47:06

Um, no and when, when people don't have anything to gain and they're doing things for just the good of humanity, it, it makes it much easier to blame.

47:18

a great positive attitude that isn't mad at one side or the other side or blame game. You know, we've got to think of this from a systems perspective rather than individual blaming.

47:31

Know, we've, we've seen some wild times in Florida obviously.

47:36

But you know your your comments remind me of Rebecca Jones here claims that she was fired for not misusing the data. And I mean, there's a lawsuit pending to my knowledge.

47:51

But rather than just give up or, know, keep sayin, whatever in the news and here, this is bad, that's bad.

48:01

She actually developed a community dashboard where everything is transparent. There's data dictionaries, methodologies were spelled out very clearly. She allowed anybody to submit data, you know, collaborated for partnerships. And this is all out of her pocket or donations.

48:23

So, that's one example of, I don't know what she would stand again, I know she lost the job and I'm pretty sure this is, you know, running out of a shoestring budget. Just, that would be an example.

48:39

But, we've also seen, you know, when we don't have national guidance that's very clear, and then maybe we don't have state guidance or strategy.

48:50

We do see our communities pick up.

48:54

No, take the charge, which is absolutely wonderful. Any of us can.

48:59

Any of us can make a difference. All of us will make a difference in the end, and it's, I think it's great.

49:06

When you make things real, like relatable, I think daily data storytelling can be really effective.

49:13

Also, to try to get data out there in a way that's understandable to most people. 

49:20

And you gotta make some simulations that make it relatable. You know.

49:24

Or what if we took all of the technologies we have and get all the experts and all the influencers, you know, Aspen Institute, celebrities, I mean, you know, some people have balance. I said, do it.

49:39

Then, you know, just to get a very massive grassroots group going to spread the right message.

49:46

I think might be helpful and then, of course, you get us started democratizing science and data.

49:55

It needs to be open needs to be transparent.

49:59

It's still like that, research, articles and the most scientific articles.

50:05

Our subscription only mean that those more accurate sources should be accessible by everyone, so we can all make good decisions.

50:18

Agreed. Agreed. Well, Melissa, thank you very much for for being here with us today. Pleasure. Yeah. Before, before we wrap up, I would ask you would you make available list of references, you know, in the notes for this podcast? Yeah, I do have a few things I think would be helpful to leave with one thing, if that's all right, go ahead.

50:44

And after the Holocaust, we had the Nuremberg trials and out of that came something we call the Belmont Report and it was designed for research participants, but it's really transferable and applicable.

51:01

And there's only three simple ethical principles for decision making and that is respect for persons, beneficence, and justice.

51:12

And I truly think, if it's really that simple, if we begin to make decisions with morality and ethics in mind, well, we'll beat this desert community.

51:24

Wow! I can't think of a better way to end on that note.

51:30

Again, everyone who's tuning in, this is Jim Frazier on the Smart City Podcast, with doctor Melissa Wylie. Melissa, thank you very much.

51:38

And we look forward to have you back, again in the near future. Thank you very much for that.