Platt Perspective on Business and Technology

China, the United States and the world, and the challenge of an emerging global COVID-19 coronavirus pandemic – 54

Posted in macroeconomics by Timothy Platt on August 11, 2020

This is my 59th posting to specifically address the COVID-19 pandemic that we now face and that by now has found its way into essentially every nation on Earth, and into every facet of our lives. And it is also the 54th installment to this specific series on that.

I began laying a foundation for comparing COVID-19 to other significantly impactful human disease outbreaks in Part 52 and Part 53 (with a brief digression into zoonotic diseases too.) And my goal for the first part of this posting is to bring that into specific focus, with a more detailed discussion of how such comparisons might even be made. So I will write here about COVIV-19 and the SARS-CoV-2 virus that causes it, but I will mostly be writing about potential and realized epidemic and pandemic diseases in general. As such, I am going to dispense with my usual World Health Organization, COVID-19 update start and begin with a consideration of a more general pathogen model. Then I will discuss human response to that and its emerging spread. And one of my primary goals leading from that, will be to propose some possible approaches for moving forward from where we are now with this pandemic. But first, and to set the stage for that, I repeat here an organizing framework for thinking about an event such as our current COVID-19 pandemic, as offered in updated form in Part 53 and as labeled here (in boldface) for purposes of this posting:

The disease mechanism: the pathogen that is causally responsible for a disease outbreak is a mechanism that can be considered to be responsible for that disease itself. So it and any disease spread arising from it can be seen as holding specific medical and epidemiological significance here.
The pandemic enabler that creates opportunity for that level and range of disease spread; epidemics and more widely spread pandemics are shaped by, and even enabled by sociological and sociopolitical forces too.
A basic response and resolution framework: effectively addressing an epidemic or pandemic and on both its pathogen-defined disease front and on its human response front, calls for a systematic infrastructure level response.

My goal here is to at least offer a first take analysis of some of the key factors that would enter into the first of those points, and with an initial emphasis of simply identifying the pieces that enter into them. I begin with the disease as an explicitly biological and medical phenomenon and with the pathogen and its properties. And I will couch the points of detail raised there in large part in question and comment form. And I will begin with the obvious: the question of what would bring a microbial species to human attention in the first place and certainly as a possible source of human risk or harm.

• What does this pathogen (this somehow contagious organism) do to people once it becomes symptomatic? COVID-19 initially presented itself in Wuhan China, as a pulmonary disease, so an initial response to this from before the pathogen responsible for it was even identified, is that. Subsequent experience with this disease and its pathogen has expanded upon that start as new courses of disease involvement and progression have been identified, at least in specific patient demographics.
• Where did it come from? This is actually several questions. It is a geography and locale question: COVID-19 first made its appearance as a human disease in an open market that sells wild animals of all sorts for food. It is also a question of original pathogen hosts. Did it come from an animal species, or did it arise as a novel mutation of an already existing microbe that was already present in human populations, with that mutation arising in a person? In this case, COVID-19 arose as a zoonotic disease. Initially, it was assumed that the first animal to human transmission was from a pangolin but it is now known that the closest genetic match relatives to the SARS-CoV-2 virus, and certainly anywhere near Wuhan, are endogenous to species of bats.
• Does this disease transmit directly from person to person, and if so how? This is a crucially important question. Cholera and related diseases transmit to people from contaminated water and from food that has been exposed to it. It does not transmit directly from person to person. Ebola does transmit directly from person to person, or from contaminated surface to contaminated surface, where that requires exposure to infected bodily fluids. So while it transmits readily by fomite and direct human contact routes, it does not transmit as an airborne illness. Measles is an airborne disease spread by contact with droplets that are spread when an infected person breathes, coughs, or sneezes. COVID-19, it turns out is largely an airborne disease too, though it also spreads by fomite transmission too, from contact with contaminated surfaces.

There is an important timing progression built into those first three points. The first of them is probably going to be fairly clear from the beginning of an initial outbreak, at least for a primary disease manifestation. The second of them takes time to answer. And the third of them can be a real learning curve challenge. COVID-19 is, unfortunately, a textbook example of that; here we are some eight months into this pandemic with close to 20 million confirmed cases of it worldwide and with that known to be a significant undercount of the actual number of people who have caught this disease, and we are still learning the basics of how it spreads and filling in crucial gaps in our understanding there.

• Who, demographically, is most at risk of catching this disease? What are the risk factors there?
• When do they become contagious for it and how long do they remain so?
• Are there asymptomatic carriers and do they actively spread this disease? How long are they contagious and what viral loads do they carry and spread while they are contagious?
• Who are more likely to fit this pattern, demographically?
• Who, demographically, is most at risk of serious complications or death from this disease?
• Are people who are infected with this pathogen and who will become symptomatic from it, contagious for it during their pre-symptomatic incubation period? And if so, for how long?
• Returning to the issues of fomite (surface or related) transmission, how are involved surfaces contaminated? How heavily are they contaminated? How long do they remain infectiously contaminated? And of course, what types of surface and surface conditions would foster disease transmission from them and for more extended periods of time?
• Returning to the issues of airborne transmission, when someone coughs, sneezes or breaths for that matter, and they release infectious viruses or other microbes into the air from that, they do so through the spread of droplets and micro-droplets. How large are these infectious particles (with this generally measured in microns, for droplet diameters)? The smaller they are, the longer they can remain suspended in the air as sources of airborne contagion, and the farther they can travel in airborne suspension as such. This is where our perhaps realistic, perhaps unrealistic presumption of 6 feet of separation for safety with COVID-19 enters this narrative.
• Measles viruses can remain suspended in the air as infectious particles for half an hour or more as naked viruses, not requiring a mucosal droplet sheath for protection. And that significantly contributes to it being among the most contagious diseases known, and it also explains to a significant degree why herd immunity is only possible for that disease when well over 90% of a population has personal immunity to it.
• And in either case: fomite or airborne transmission, how much of a viral or other pathogen load does someone have to be exposed to, to become infected with that disease? This is certain to be a question with multiple valid answers depending on factors such as the age and general health of the people involved, and whether they have a robust or compromised immune system.
• I have focused essentially entirely on fomite and airborne transmission in this series as they appear to be the two most likely and important transmission mechanisms for the SARS-CoV-2 virus, just as they are for other coronaviruses. That said, there are five generally recognized transmission mechanisms that pathogens can readily spread through: fomite, aerosol, oral (as in food and liquids ingestion), direct contact (from another person by whatever means), and vector (from a nonhuman host.) How many of these routes of transmission have to be taken into account as representing serious sources of risk for a given pathogen?
• Turning back to the issues of source and with possible vector transmission in mind, do other species carry this disease? Do they constitute ongoing uncontrolled reservoirs of it that could lead to new outbreaks or wider expansion of already occurring ones? The SARS-CoV-2 virus has been observed in a few other species as what are most probably rare events and not as sources of ongoing contagion concern. The flu strain responsible for the 1918 flu by comparison, sickened and killed members of a very wide range of species. And for a second example here, of this phenomenon, rabies is known to infect a wide range of species, some of whom become symptomatic from it and die from it and some of whom appear to more commonly serve as asymptomatic carriers.
• And then there is the question of mutations. And once again, I cite the flu virus in its seemingly endless mutational varieties as an example here, for how that can lead to public health and individual healthcare crises. Every year, people who seek vaccination coverage from the flu have to get revaccinated with what are hopefully going to be effective vaccine formulations for that year – with them covering the correct set of new viral variants that appeared likely to become the most important ones for a coming season. Even when a vaccination development system is as routinely standardized as that one it is, with its annual implementations, there is going to be guesswork, based on what is emerging in China, before it has had time to spread. So far at least, the mutations that have been found in the wild of open populations, have not shown the types of surface protein changes that would make them appear new to those who were infected with a more routine strain and who might have some immunity from that now.

But COVID-19 is so new still that we do not have any real answers to the questions implicit in that last bullet point. Even if we do see an effective vaccine against its virus, can that hold lasting value in the face of mutational change, or will it be necessary to get revaccinated and even yearly for this too?

Let me put COVID-19’s numbers up to now into perspective. Every year, according to US CDC studies, “between 291,000 and 646,000 people worldwide die from seasonal influenza-related respiratory illnesses each year” (see Seasonal Flu Death Estimate Increases Worldwide. The 1918 flu is estimated to have killed approximately 194,000 people in the United States alone, in October, 1918. COVID-19 killed over 700,000 people worldwide between the start of March, 2020 and now and even when only considering a greatly underestimated official confirmed count.

Is COVID-19 a contender for becoming the second worst pandemic ever, as categorically discussed in Part 53? Will mutational changes make it more deadly once contracted than it is now, or more readily transmitted or both? Will a mutation arise that undoes any seeming benefits that have accrued from survivor immunity, or from a vaccine against this virus when that becomes available, or some combination thereof? We do not know; no one can know the answer to any of these yet, baring for example the incontrovertible emergence of some specific new mutational form that explodes in numbers of cases it appears in. And that does not seem to have happened; the mutations that have been found for the SARS-CoV-2 virus to date at least, seem to be more benign than that.

So what would a perfect pathogen look like, where perfection would be measured in terms of the virus fulfilling its programmed goals of replicating and spreading to new hosts to further replicate? Let’s consider the above points of discussion as a recipe guide for that, if you will.

• Such a pathogen might kill its hosts but it would not do so too quickly. Effectiveness here means capacity to spread and that means a host it is infecting being able to spread it.
• Asymptomatic carriers would obviously help there and a great deal, as would pre-symptomatic carriers being able to transmit the pathogen during their incubation periods.
• And the more transmission routes that that pathogen can effectively transmit through the better, and particularly where those routes are really robust for that. For a working example of this robustness, consider the measles virus that as a naked particle, not requiring a protective droplet cover, can stay afloat at least in the air, for hours and longer. And for fomite transmission, consider the anthrax bacillus. Bacillus anthracis can and does sporulate, effectively going into what amounts to suspended animation while waiting for contact with a possible new host organism that it can revive and replicate in.
• For timing though, it is not necessary for a perfect pathogen as discussed here to last forever on a surface, or for it to float suspended endlessly in the air. It is only necessary that it remains viable long enough, by whatever transmission routes for it to transmit, and effectively enough to create high transmission rates from host to host.
• A capacity to initiate an active infection with a minimal number of copies of the pathogen: with a minimal infectious load or infectious dose as it is also called, can also help here.
• And having zoonotic reserves and in multiple host species, some of whom can carry it asymptomatically can help too. Though as I will discuss in the next installment to this series, human behavior can create that same infected reservoir population effect in enabling ongoing and recurring epidemic and pandemic spread capability too.

And with that, I will turn to the second set of human context issues as noted at the top of this posting when discussing epidemics and pandemics per se, and what it takes to create one: pandemic enabler issues and factors. I will delve into that in the next posting to this series, starting by adding a few more details to this posting’s disease mechanism oriented discussion. And then after completing my basic discussion of those first two sets of issues, I will turn to and discuss systematic response and resolution frameworks and possible approaches to achieving them.

And this brings me to the second part topic of this posting and COVID-19’s longer term issues, as proposed for here at the end of Part 53. My expressed goal here has been to continue a discussion of drug pricing as begun there, but doing so in any meaningful way will call for a more lengthy discussion than would make sense if appended to the above narrative. So I am now planning on completing this posting’s first part discussion as left off here and as just outlined for moving forward with it, in an upcoming Part 55. Then I will turn to consider drug pricing and related longer term, post-COVID-19 related issues as a single topic for a Part 56. I will, of course continue pursuing both here-and-now oriented issues, and longer-term post-COVID-19 ones as well, and through any foreseeable future, starting with the second part discussions list for longer-term consideration, as offered in Part 53.

Meanwhile, you can find this and my earlier COVID-19 related postings to this series at Macroeconomics and Business 2 and its Page 3 continuation, as postings 365 and following.

Business planning from the back of a napkin to a formal and detailed presentation 36

Posted in strategy and planning by Timothy Platt on August 10, 2020

This is my 36th posting to a series on tactical and strategic planning under real world constraints, and executing in the face of real world challenges that are caused by business systems friction and the systems turbulence that it creates (see Business Strategy and Operations – 3 and its Page 4 and Page 5 continuations, postings 578 and loosely following for Parts 1-35.)

I have been selectively discussing two case study businesses here, that I continue turning to in this installment as well:

• Alpha Hardware Inc.: A hardware store that went through a more fundamental transitional change as it came to outgrow its original single storefront and its space restrictions there, to become a two storefront business with a more specialized Alpha Hardware and an Alpha Home Goods, and
• The e-Maverick Group: A cutting edge technology offering, business-to-business oriented software development company that also faces business transition challenges.

I finished Part 35 by stating that I would reconsider “how innovation driven my two case study businesses actually are or are not” here. And I immediately added in that context, that “I have been basing my discussion of them up to here, primarily in terms of how their business leading owners see their enterprises – which can at times differ from what they are actually doing as businesses.”

But given the nature of this phase of this overall series, with its focus on innovation as a business model imperative, and with that more of a driving force for the e-Maverick Group than it would be for any hardware store, I primarily focus here on that case study in what follows, and on what that business actually does. I will, as such, primarily just comment on this set of issues in an Alpha Hardware context, for comparison purposes, at least for now. And I begin addressing all of this by stating that in a fundamental sense, I have already offered all of the puzzle pieces in this blog and even just in this series here, that go into what I would offer in this posting on that. It is just that I have not organized those pieces together as more precisely needed here. So the basic puzzle pieces that I will cite should sound familiar to any more regular reader of this, but the connecting points that I add in their context might be a bit less so.

Let’s start with the puzzle pieces themselves:

• I have recurringly written of innovation acceptance (or resistance) diffusion curves, and of how genuinely disruptively new and novel innovations would most likely only appeal to a perhaps small segment of a possible, ultimately realizable market for their type of product: the small number of pioneer and earliest adaptors who learn of this more novel purchase and use opportunity who would be willing to enter into that in an immediate time frame.
• A small but reliable market segment of this type might offer real value to a business that seeks to gain revenue and overall competitive strength from sales. But small is small, and most businesses, and even the most innovation driven of them, need larger and more reliable market shares than they could get and keep from this pioneer and earliest adaptor group alone.
• So even a business that seeks to be cutting edge for what it develops and offers, still needs to be able to appeal to a wider audience, and one that includes participants that run at least closer to mainstream mid-range adaptors too.
• I have in a way already made note of this in earlier installments here when writing of how the e-Maverick Group has a dedicated team for managing updates and related support for their already available, more legacy offerings: software products and versions that they no longer see as their cutting edge offerings but that their customers have and rely upon.
• That type of ongoing support-oriented product and service offering, as already discussed, can be used to retain current clients in a way that would bring them back as new product buyers again, and as repeat business loyal customers. But just as importantly, this type of added cash flow of revenue received, can in effect bankroll next-step new and disruptively new product development too, where that might not be anywhere near as sustainable if it had to be entirely self-sustaining and from day one of its new product releases. Yes, I am arguing here that at least short-term and for more immediate impact, innovation development or at least disruptively novel innovation development can effectively qualify as a loss leader, with time required to turn that around and even when such innovation would eventually come to sustain the business as a whole.
• But focusing on the short term and the more immediate here of this, the revenue and profits defined value creating potential that is realized from the volume of sales actually achieved up-front, is going to be a function of the size and the market diversity reach that can be achieved for such a new offering, with diversity there a measure of how wide a swath of that innovation acceptance curve that can be brought to buy in and as early as possible.
• And marketing and sales campaigns that would help to drive that, have to be designed and executed with this demographic targeting diversity in mind.
• So what does innovation actually mean in anything like an e-Maverick context and when looking past the more generic, broad brushstroke marketing image of that business as being innovation driven and of it being “the source of New for the business world”? That has to be at least significantly defined by the business. But in actual practice it also has to be at least partly, and even significantly defined by the marketplace too. And those two visions and understandings have to connect together in ways that would at least ideally find an optimized balance point that would offer value to both the business and its market. And it would be a minimax balancing point that is based on market size reached through efforts made to strategically expand there, while still being true to what the business seeks to be.
• Yes, strategically catering to progressively later and later adaptors and going further than that optimal point for how much the business caters to routine and standard software needs, might at least seem in principle to be a way to increase revenue streams, profitability, and competitive strength. But if this business goes too far in that direction it might in effect lose itself – lose its identity and its reputation for offering new and cutting edge at all. Reaching for too wide and diffused a market share might lead to their effectively destroying their more unique value proposition that has set them apart from the crowd that comprises software developers as a whole.
• And this is where owning and running an entirely separately branded and run “standard software offerings” subsidiary with its own distinct business model and presence might work. But would it make sense to expand out in that direction at all, and particularly when this would mean intentionally seeking to compete in a now expanded market, but one in which they would not be offering anything particularly unique or notable and where profit margins might be minimal at best?

And this brings me back to Alpha Hardware, Inc. and its business model and its way of achieving what amounts to a true unique value proposition in what it offers to its markets – while offering standard products and almost entirely so. Basically, the question raised in the last of those bullet points, can be rephrased as:

• Would it make sense for an e-Maverick Group to try to also be a software equivalent of an Alpha Hardware and even if that called for setting up a separate brand and identity for that expansion? And what would that even mean, strategically and operationally and from an outwardly reaching market facing perspective?

I am going to continue this discussion in a next series installment. And in anticipation of that, I note here that I will focus at least in part there, on the fact that the e-Maverick Group is a business-to-business oriented enterprise, with all of the added complexities faced there from how their customers have to follow their decision making processes.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory.

Rethinking exit and entrance strategies 39: keeping an effective innovative focus while approaching and going through significant business transitions 29

Posted in strategy and planning by Timothy Platt on August 7, 2020

This is my 39th installment to a series that offers a general discussion of business transitions, where an organization exits one developmental stage or period of relative strategic and operational stability, to enter a fundamentally different next one (see Business Strategy and Operations – 3 and its Page 4 and Page 5 continuations, postings 559 and loosely following for Parts 1-38.)

I pursued a largely dual narrative in Part 38, that can be roughly characterized as discussion of:

• How it can be both important but also difficult for us to see our business creations dispassionately and impersonally, when facing an at least possible need for change there, and particularly when that would mean more profound change, and
• How change might be viewed in that and similar contexts for how profound it really is, with that depending on its degree of newness and novelty, and on the level of impact that it brings.

I delved into the first of those topic points, with two case study examples that I have been developing here, firmly in mind and overtly referred to:

• ClarkBuilt Inc. is a business that has reached a point in its development, as previously discussed here, where its owning founders have to make some fundamental decisions as to what type of business they have now, and what type they will have moving forward: as a manufacturer and just that, or as a design shop that monetizes and gains profits as such from its brand and name recognition, or as some combination of those two options.
• And Palabraum Inc. is a business that has reached a point in its development where its owners and executive managers have come to see a need to bring New back into what they do, and into what they offer to a marketplace that has come to see their product lines as staid at worst, and as retro at best.

And I at least began a discussion of the second of those points, with that line of discussion in mind, pursuing it in terms of a conceptual model of long standing, that I have made use of on numerous occasions now in this blog: a basic innovation acceptance diffusion model that would trace and explain the patterns of acceptance to and resistance to change, among different demographics in an overall marketplace, over time and depending on the degree and type of change involved.

To bring this initial orienting note up to date here, I went on to at least loosely define four terms in that overall narrative context:

Compartmentalized planning and execution, and uncompartmentalized planning and execution. The first of these refers to planning and execution upon that, that are carried out dispassionately objectively (at least for the most part) and with emotional influence held to a minimum for what is decided and how. It is data driven. Emotions and I will add other shaping biases and sources of influence that are not data-driven, all add to and significantly so, to its uncompartmentalized counterpart. And it their active and at times even overriding impact that define that decision and action pattern. These terms arose for significance in the context of the first topics point portion of this discussion, in Part 38.
Conservative change versus aggressive change, and the dynamics of how these terms would be variously, and at times quite differently applied, arose here in the context of the second topics point portion of Part 38 and as that line of discussion will be continued here too.

And I begin this posting’s continuation of both of those set of issues, and certainly as both would be applied conceptually at least in a business development context, by picking up on some of the specific wording the first of those more-definitional bullet points where I wrote “carried out dispassionately objectively (at least for the most part) and with emotional influence held to a minimum.”

• We all carry unexamined assumptions, and we all base at least some of our decisions at least in part on them and even if we do not explicitly examine and validate them in any given instance, and even when we see them as objectively based. And it is this very “taken for granted invisibility” that can lead to their becoming both sources of influence in our decision making, and even guiding ones. This does not matter for the most part when decisions are non-controversial and when we do in fact more actively check to make sure that “business as usual” would be acceptable, for a lack of novelty in context that might challenge that. But this can become quite important when change and uncertainly become important and when the consequences of what is being decided upon, and of what is to be done and how, become very significant.
• The types of change-facing decisions that the owner founders of the two case study examples of Part 38 (and preceding): ClarkBuilt Inc. and Palabraum Inc., fit that second pattern and almost by definition for being consequential and for bringing novelty into play. And it is the type of data plus emotion and more, decision making and with that being pursued by competing but necessary decision making participants, that can bring such enterprises to seek out an “honest third party broker” in the form of an outside consultant, who would seek to help them to expand the range of options that they might consider, in order to break an impasse in place. But that is a digression here; the important point is that uncompartmentalized there, can stymie and precisely because the “and also …” that drives it can be so hard to pin down and define in workable, explicitly convincible strategic and operational terms. Resolving this type of impasse means widening the range of options considered, in order to find a way around those barriers – not psychoanalysis: tactical reasoning and hopefully convincing tactical reasoning.

And this brings me to the issues of change her se and to an easy to presume assumption that I would address and dismiss, as I continue on here:

• Anyone who would take the risk of founding a new business, with all of the risks and unknowns that that of necessity brings with it, would have to be comfortable with the new and even the disruptively new, and they would approach buying into and making use of change from a more early adaptor, or even more of a pioneer adaptor perspective.

No! Just consider entrepreneurs who look for the safety and stability of entering into a franchise system as a franchisee. Yes, they face a measure of risk as well as of opportunity that a more usual in-house employee does not generally have to deal with. But this new business outlet context means that both risk and I have to add benefits are going to be a lot more constrained than what might be expected when setting out to build a new venture from scratch, on their own.

Returning to my consulting digression for a moment, one of the most important homework tasks that a consultant facing this type of assignment can carry out, is an at least preliminary assessment of where the people who they would have to work with on this, actually fit on a change acceptance or resistance scale at all. (Another such task is one of taking at least an initial assessment of who would actually have to be included there, and with a goal of expanding or contracting that list as needed, and of bringing the people who need to be on it, into these conversations.) But for purposes of this discussion, I simply stress that any real resolution of the types of disagreements that ClarkBuilt Inc. and Palabraum Inc. face, will depend on the people holding those competing views, understanding both themselves and those others who they disagree with (at least to start), who they have to work with on this. And that has to include their coming to understand something of the Why for their differences of opinion and of judgment, as well as the precisely What for that. They each have to know when a possible change or stay decision would be more conservative or aggressive and why. And they need to be able to negotiate what I would call “mutually acceptable comfort zones” for the sticking point issues that do emerge.

I am going to continue this discussion in a next installment where I will address how a conservative versus aggressive distinction here can be timeline determined, or scale and scope determined, or both, as initially noted here in Part 37.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory.

China, the United States and the world, and the challenge of an emerging global COVID-19 coronavirus pandemic – 53

Posted in book recommendations, macroeconomics by Timothy Platt on August 6, 2020

This is my 58th posting to specifically address the COVID-19 pandemic that we now face and that by now has found its way into essentially every nation on Earth, and into every facet of our lives. And it is also the 53rd installment to this specific series on that.

As usual, I begin this posting with newer updates to a set of basic epidemiological findings, sharing more recent globally sourced data as offered by the World Health Organization as to the current overall state of this pandemic:

• August 03 at 01:39 GMT: 18,231,535 reported cases with 6,094,997 currently active, 12,136,538 now closed, and with 65,753 active in serious or critical condition (1 %), and 692,694 closed cases reported as deaths (6 %)
• August 04 at 01:25 GMT: 18,440,141 reported cases with 6,071,004 currently active, 12,369,137 now closed, and with 64,675 active in serious or critical condition (1 %), and 697,094 closed cases reported as deaths (6 %)
• August 05 at 02:22 GMT: 18,700,833 reported cases with 6,081,263 currently active, 12,619,570 now closed, and with 65,480 active in serious or critical condition (1 %), and 704,347 closed cases reported as deaths (6 %)
• August 06 at 0:35 GMT: 18,956,630 reported cases with 6,104,844 currently active, 12,851,786 now closed, and with 65,514 active in serious or critical condition (1 %), and 710,038 closed cases reported as deaths (6 %)

One of the biggest challenges that we face coming from this pandemic, is the ongoing belief as held by so many, in the palpably demonstrably false. That obviously includes the stridently insisted upon belief that social distancing and wearing masks or other face coverings are just adversarial political theatre, intended to rob those on the political right of their personal liberty. But even when considering those who see such disease containment as valid and even essential, many still hold their own disease enabling beliefs. And one of them, that I have been repeatedly challenging here, is the belief that since children and young children in particular rarely show COVID symptoms, they do not catch or transmit this virus. I have offered at least a brief succession of references to the contrary of that, in this series and certainly more recently in it. And add one more to that list as coming from a politically red, Trump supporting state:

Georgia Camp Outbreak Shows Rapid Virus Spread Among Children

And to quote from that news story:

• “Three-quarters of the 344 attendees and staff for whom the CDC was able to obtain test results tested positive for the virus.” And that was with incomplete reporting, so the actual transmission rate of the SARS-CoV-2 virus as potentially calculable from this event was probably underestimated there.

I will pick up on the implications of this type of cognitive disconnect and its consequences, later in this discussion thread, when address the question of what relevant historical data and insight mean when attempting to compare COVID-19 as a pandemic event to other historical human disease outbreaks, epidemics and pandemics. But with that in mind, I continue where I left off at preparing for that comparison itself, at the end of the first portion of Part 52 with its historical timeline and related commentary.

I said there that I would turn here to consider the 1918 flu pandemic and more recent events and I will do that. But before doing so I am going to further discuss one of the entries that I offered in Part 52, and I will add one more that is largely contemporaneous to it. The one that I will discuss further, as repeated from there is:

• In the New World, in what is now Mexico and Central America and further south extending into South America, the Cocoliztli, or pest in Aztec: a massive smallpox pandemic, is believed to have killed upwards of 15 million people. It is believed to have been brought there by European explorers and it is known to have contributed to the downfall of the Aztec and with time the Incan Empire too. We have to assume that 15 million figure is a significant underestimation so this might in fact be a second place contender too.

I begin expanding on this by offering a link to a sobering if well researched reference work: The Effect of Smallpox on the New World. This work and the specific research that it cites, estimate that smallpox killed off as much as a third of the entire population of Native Americans in North America and in just a few months. And it spread southwards with devastating impact too. And also see How Europeans brought sickness to the New World. But this is not just a story of a disease spreading East to West, from Europe to the New World. It is a story of a disease traveling from a region where it had already struck before and where it was known, to a region where it was completely unknown and where there had been no natural selection for even just the most nominal resistance to it. And that goes both ways here, where Europeans picked up New World diseases and brought them back home to their countries of origin too, and with devastating effect there.

The best known putative New World, to Old World, Europe disease exchange is syphilis. While there are those who would argue that this disease was already in Europe from before Columbus and his voyages, it is likely that his crew did in fact bring this disease back with them. The first known outbreak of it in Europe, tellingly occurred in Naples, Italy in 1494 and 1495. And this disease killed millions. It went on to become a major killer in the time of the Renaissance in Europe. And some studies argue that in the late 18th century, up to 20% of the citizens of London in the age range of 15 through 34 were treated for this disease. See this History of Syphilis for further details.

But to pick up on a detail that I noted in passing above, the now European syphilis of the late 15th and early 16th centuries was far more deadly than this disease is now. As Jared Diamond described it:

• “…when syphilis was first definitely recorded in Europe in 1495, its pustules often covered the body from the head to the knees, caused flesh to fall from people’s faces, and led to death within a few months.” As quoted from:
• Diamond, J. (1997) Guns, Germs and Steel. W.W. Norton.

As bad as that seems, early European syphilis was actually worse. It, for example, also caused necrotic bone lesions that led to bone crumbling fractures. And it attacked the brain and central nervous system far more rapidly than it does now as tertiary syphilis or more specifically as neurosyphilis.

And with that, I turn to the best known of the major historical pandemics, and the one that COVID-19 is most commonly compared to: the 1918 flu pandemic. And I begin doing so by discussing its perhaps most common name: the Spanish flu.

This flu virus and the disease it caused did not arise in Spain, or even in Europe for that matter. So why was it called this? Blame nationalistic animosities for that. Similarly, when syphilis first began its march through Europe, the French called it the Spanish Disease, the Spanish called it the French Disease … and it is said that the English called it both, depending on who they were speaking with. They were unhappy with both the French and the Spanish at the time. Moving forward, president Trump and his followers have taken to calling the SARS-CoV-2 virus the China virus, or the Wuhan virus and for similar jingoistic nationalistic reasons.

• Divisiveness in the face of shared danger can only enable the source of that danger by limiting or even preventing anything like an effective commonly shared response to it.
• This was true for past epidemics and pandemics, and it is just as true now and with the evidence of that emerging and expanding before our eyes, every single day.

So far at least, every single point that I have made in this posting from its beginning, has in fact been relevant to the line of discussion that I will offer here when discussing pandemic severity, and the question of where COVID-19 stands for that in comparison to earlier historical epidemics and pandemics. This divisiveness and its implications for mounting a more widespread and even global response to such a crisis, does too.

There is an extensive reference library’s worth of material available now, regarding the 1918 flu and both in the popular press and in the professional literature. And this includes a wide range of books of note. But to restrict myself to one such reference work from that, I suggest:

• Barry, J.M. (2018 edition) The Great Influenza. Penguin Random House LLC.

I have already offered the more abstract and even dehumanizing dates and numbers for this event in the course of writing this series, as I have cited it as a source of comparison to what we now face. So I pick up on that narrative from a somewhat different perspective that will hold particular importance later on in what I will offer here.

The viral strain that was responsible for this event was readily transmissible from person to person, making it a deadly disease that was quite capable of spreading. But it was human behavior that gave it the means and opportunity to spread as widely and as quickly as it did and with the numbers of lives lost that resulted from that. The story of that pandemic, as narrated in Barry’s book, is one of heroes and of people of knowledge and insight, and of fools and people where were purblind in their folly for what they were unleashing in this disease from that.

This same conflict of vision and understanding and its consequences are what we face now, with the COVID-19 pandemic that we are currently going through. That failure to respond to crisis cost millions and millions of lives, avoidably lost in the Great Influenza’s 1918 through 1920 initial crisis period. And we are in fact still living with its direct sequels and every single year with our new flu virus variants. In a fundamental sense, we have never really left that pandemic behind us; we face its direct offspring every single year and with ever-increasing cumulative loss of life from it. We are seeing similar failures in leadership and in judgment and in willingness to take prudent steps to limit disease spread now, just as then. And we are almost certain to see recurrences of this disease, just as we see recurrences of the flu and on an ongoing basis and from now on – well after this immediate pandemic crisis ends. And yes, we have already seen hundreds of thousands of lives lost and avoidably so, this time too and even just as of now when we are still early in this pandemic.

We have not seen anything like the pandemic of 1918 through 1920 (with its ending blurring out beyond that) since then, and certainly up to the start of COVID-19. The disease comparison question that I have been at least approaching addressing here, is in fact one of whether this pandemic will worsen and worsen until it becomes at least a second place contender, as suggested for several earlier epidemics and pandemics in Part 52. That, ultimately, is up to us. And that brings me to a line of discussion that I have been building towards, and from early in Part 52 up to here: an at least brief and selective analysis of the medical and related side to this pandemic and of epidemics and pandemics in general, coupled with a corresponding listing and analysis of sociological and sociopolitical factors and forces that shape and set the scale of outcomes there. I will turn to that in the opening portion of the next installment to this series. And in anticipation of that narrative to come I add here that I will take a basic conceptual approach that I have offered here in this series for understanding these issues and I will add one more key element to that:

• The pathogen that is causally responsible for a disease outbreak is a mechanism that can be considered to be responsible for that disease itself. So it and any disease spread arising from it can be seen as holding specific medical and epidemiological significance here.
• But epidemics and more widely spread pandemics are shaped by, and even enabled by sociological and sociopolitical forces too.
• So effectively addressing an epidemic or pandemic and on both its pathogen-defined disease front and on its human response front, calls for a systematic infrastructure level response. (I have been addressing this from a more piecemeal perspective in second part discussions in these postings and will pursue a higher level perspective on this complex of issues in this anticipated discussion to come.)

I will add and at least begin to discuss at least a few possible remediations, and preventative or at least adverse consequences-limiting actions that might be taken now, and for moving forward from where we are now with COVID-19. That, I expect to offer on a more piecemeal basis, as I continue writing to this series.

And with that, I turn to the second portion discussion of this posting, and longer-term new normal considerations. I ended Part 51 with a to-address list of new normal, post-COVID-19 issues that have to be addressed in our healthcare and public health systems. And I begin here by repeating them as offered there, noting in advance that I have already at least begun to address the first of them and both there and in Part 52:

• Standardizing medical information, and the questions of what standards would be developed and used, and with what overriding purposes they would be developed and organized for – e.g. insurance use and coding for claims, versus standardization for more directly personal healthcare purposes.
• Controlling drug costs and drug availability issues and challenges.
• The challenge of hospitals and clinics that cannot provide first rate service, and where and why.
• And the emergence and elaboration of telemedicine as disruptively new change, and both as medical appointments might be held remotely and as new types of online connectable technologies are brought into this, informing such encounters.

The first of those points is all about information and its disconnects. I frequently write in this blog of economic friction as a macro-scale phenomenon, and of business systems friction as its microeconomic, more limited-in-scale counterpart. The above repeated first bullet point is ultimately all about friction: all about information development and communications challenges and their consequences, even if those challenges can erupt in conflict of interest forms.

There are a number of approaches that could be taken in addressing the second of those bullet pointed topics, including for example, acknowledging and limiting, through regulatory law or other mechanisms, systemic price gouging and certainly for lifesaving drugs that are well established but indispensible for those who need them. See, for an all too clear example of need there: Why Did That Drug Price Increase 6,000%? It’s The Law in that regard. But I am going to turn to and address some of the challenges that arise in new drug development and pricing here, and how pharmaceutical manufacturers and others in the supply chains leading to direct user consumers, follow practices that are geared against those consumers.

The type of established and known drug re-pricing that I just cited above, is at least directly visible to the marketplace and its customer members. Original, pre-increase prices are known and certainly at a directly consumer-facing level. The problem that I would raise and address here is one of transparency, or rather opacity and a lack of transparency as that reigns in essentially all pharmaceutical development. And the bottom line result of that here, is that consumers face out of control prices that match what would be expected at worst, from out of control monopolies.

• Monopolies stifle competition, and with a variety of toxic consequences that include but are not just limited to excessive pricing, loss of product alternatives and a stifling of innovation.
• A complete lack of transparency in business systems and in their decision making processes can lead to those same things and particularly where the businesses in an industry have what amounts to a captive audience that has to purchase their products and even as a matter of absolute necessity.

What does it actually cost to develop a new drug and bring it to market? How would fair pricing even be defined there, in any practical, implementable sense? There are at least two sides to those questions and to both answering them and even just to understanding them: the manufacturer’s side, and the marketplace and consumer’s side. And both have valid points on their side. My goal here is to at least briefly and selectively touch upon a few of them and from both sides, as they would apply here. And after offering that, I will offer a few thoughts as to how a modus vivendi might be arrived at between them, and with greater transparency requirements and business confidentiality and risk management requirements both included in any compromises reached.

I begin this with a disclaimer of sorts. I worked for a number of years as a research scientist, and with a fair amount of that devoted to molecular virology studies. And as a consequence I was at least occasionally invited to give talks at research facilities that were owned and run by pharmaceutical manufacturers. They were interested in what I was doing related to viral replication. That said, I am also a pharmaceutical consumer with health issues that have called for my taking medications and even expensive ones. I have never worked in the pharmaceutical industry but I do hold sympathies for the genuine challenges faced there, in developing new drugs and bringing them to market. At the same time, I have felt despair, as have many at the costs of some of the drugs that are out there and at how out of line with reality they seem to be, and probably are. So I have biases on both directions here, that probably balance out, and certainly when address these issues in more general terms. (Some disclaimer: I am claiming to be an honest broker here. That, at least, is my intention as I write this.)

I am going to continue this discussion as outlined above, in the next installment to this series as its second part. Meanwhile, you can find this and my earlier COVID-19 related postings to this series at Macroeconomics and Business 2 and its Page 3 continuation, as postings 365 and following.

Rethinking the dynamics of software development and its economics in businesses 11

Posted in business and convergent technologies by Timothy Platt on August 4, 2020

This is my 11th installment to a thought piece that at least attempts to shed some light on the economics and efficiencies of software development as an industry and as a source of marketable products, in this period of explosively disruptive change (see Ubiquitous Computing and Communications – everywhere all the time 3, postings 402 and loosely following for Parts 1-10.)

Up to here in this series, I have been successively discussing the first six of a set of eight paradigmatic steppingstone advances in software development. The first five of them can be thought of as representing mature and established technologies:

1. Machine language programming
2. And its more human-readable and codeable upgrade: assembly language programming,
3. Early generation higher level programming languages (here, considering FORTRAN and COBOL as working examples),
4. Structured programming as a programming language-defining and a programming style-defining paradigm,
5. Object-oriented programming,

And the sixth on that list can be thought of as a transitional example, insofar as it is grounded in established coding approaches and their implementations, but that is more likely to come into its own to the extent that it does, as disruptively new developmental capabilities are built into it:

6. Language-oriented programming,

And this leads me to the last two entries in this list:

7. Artificial Intelligence programming, and
8. Quantum computing.

Artificial intelligence and its programming, represent a second transitional example here, and a much more important one as its implemented reach and its emerging capability have already developed to a point where it is likely to become a defining feature for understanding this 21st century as a whole, and certainly for how it is being shaped and advanced technologically.

Quantum computing represents the far side of transitional and beyond, and for what will come next. And when I look to it and to where it is in its current still-embryonic form, and with all of the unknowns that that involves, I understand something of how a one-time technology visionary and president of a major technology-oriented corporation that was renowned for producing computational devices: IBM’s Thomas J. Watson, could find himself speculating “I think there is a world market for maybe five computers.” Who could have even begun to imagine what was possible there and either for the technology that could and would be produced, or for the range of uses that that would be turned to, and literally ubiquitously and by all? We see evidence of a few test-case quantum computers and cannot even begin to imagine what will actually arise from them, and certainly as this comes into real fruition. And given the challenges of developing and maintaining technologies at liquid nitrogen and lower temperatures, even that “maybe five computers,” at least as a rough order of magnitude guess, does not sound entirely crazy.

• Watson was thinking in terms of physically huge, electrical power devouring devices that requires teams of electrical engineers to maintain let alone run. How many businesses, to consider one early adaptor group, would be expected to maintain that with all of the expenses it would bring them?
• And how many businesses, and certainly ones that are not advanced technology focused, can be expected to take on the ongoing and open ended expenses of today’s large scale cryogenic technology driven quantum computers?

That will change and in disruptively unexpected ways and directions, and certainly as and when quantum computing really begins to prove its worth. But I will address that veritable cloud of the as-yet unknowable, or at least something of the early and more visible edges of it next. My point of focus for this posting is the at least significantly more known and established of artificial intelligence and its programming. And I begin doing so by pointing out that the dream of true artificial intelligence goes back to the dawn of computer development and efforts to achieve that.

• Charles Babbage, in the 19th century, dreamed of developing it through the mechanical engineering technology of gears and shafts and escapements, and related components of his day.
• Alan Turing developed what became known as his Turing Test for establishing that a (there early design paradigm electronic) computer has achieved true intelligence, with his arriving at that understanding in the early days of a still vacuum tube-driven computer era.
• One of the very first software programming computer languages developed anywhere that has had any staying power was an artificial intelligence oriented language called LISP (for LISt Processor). This was in fact the second higher level programming language devised of any historical note, with its first formal specifications release coming out in 1958. The only higher level programming language that could claim to be older was FORTRAN, beating that first formal specification date by one year.

The dream of artificial intelligence in fact goes back centuries further than that would suggest. But for purposes of this narrative, I simply note that artificial intelligence as a computer technology goal, is at least as old as computer technology itself. And efforts to achieve it have been pursued just as long too. And when artificial intelligence programming based on LISP and newer computer languages is considered, with significant levels of that being carried out in languages such as Python, R, Prolog and Java, as well as LISP (still), this is a transitional stage example with a firmly established current and established base. But that said, it is also one that is currently developing at an explosive rate and in disruptively new and novel directions and ways.

What do we face when looking towards the more still-to-come side of that transition? I would argue that any meaningful answer to that, that we can turn to in our here-and-now, can be found in more fully considering what we seek to accomplish that we at least currently see as requiring artificial intelligence per se. And that list is already vast and it is still very actively growing, and that growth of need is certain to continue and in unexpectedly novel ways.

• What would qualify for inclusion on that needs list, at least categorically and as its entries would be more generally considered?
• Any task that would call for a flexibility of response and action that would not cost-effectively fit into a more fully specified a priori algorithm based coding construct, and even if such a more standard approach could, at least in principle be used.
• And any task that might at least nominally be amenable to other approaches according to that criterion, but that could still be significantly improved upon for speed and quality of results if an artificial intelligence based within-system flexibility could be built into whatever code that would resolve it.

Let’s consider a few, relatively randomly selected candidate examples there, to put that set of generalities into a more meaningful and perhaps convincing perspective. And setting aside the types of working or at least sought after examples that I have already been considering in this blog (e.g. self-driving cars, computer-based natural speech capability and the like), I offer:

• Rationalized new drug design where that is computationally driven and on a massively open-ended scale,
• And product design and development systems in general, where the scale of complexity there becomes sufficiently high.
• Managing high volume information flow through complex and mutable networks with a goal of real-time homeostatically optimizing resource use while achieving all information flow needs on an ongoing basis. This is a normative function example.
• Managing complex system processes where decisions have to be made in seconds or less and even in milliseconds or less, and where apparent randomness of a type characterized by chaos theory arises. And as a case in point example there, I would cite the identification of and response to cyber-attacks on critical needs networks, where that can include protecting industrial and infrastructure SCADA systems as just one source of more specific working examples.

The world is full of possible examples there, and with artificial intelligence capabilities offering a potential for transforming lives everywhere, where such change might or might not be for the good, and certainly depending on whom you ask. My point here, is that this impact: good, bad or mixed, is already profound. And its reach will continue to expand out and eclipse all that has come of it so far.

How artificial intelligence evolves and develops moving forward will be shaped by what it is being used to do. But that only addresses the perhaps more predictable side to this. As a second change driver and shaper, we have to consider the essentially inevitable role of the disruptively unexpected too, and both for how that can create unexpected barriers to specific lines of development, and for how it can open up entirely new developmental opportunities.

My goal for this posting has been to lay a needs-and-goals based foundation for a more focused discussion to come, that will address this software paradigm in the terms of this series as a whole. I will at least begin to address those issues in a next installment.

Meanwhile, you can find this and related material at Ubiquitous Computing and Communications – everywhere all the time 3, and also see Page 1 and Page 2 of that directory.

China, the United States and the world, and the challenge of an emerging global COVID-19 coronavirus pandemic – 52

Posted in macroeconomics by Timothy Platt on August 2, 2020

This is my 57th posting to specifically address the COVID-19 pandemic that we now face and that by now has found its way into essentially every nation on Earth, and into every facet of our lives. And it is also the 52nd installment to this specific series on that.

As usual, I begin this posting with newer updates to a set of basic epidemiological findings, sharing more recent globally sourced data as offered by the World Health Organization as to the current overall state of this pandemic:

• July 31 at 01:33 GMT: 17,464,995 reported cases with 5,855,674 currently active, 11,609,321 now closed, and with 66,390 active in serious or critical condition (1 %), and 676,409 closed cases reported as deaths (6 %)
• August 01 at 01:22 GMT: 17,754,183 reported cases with 5,913,018 currently active, 11,841,165 now closed, and with 65,563 active in serious or critical condition (1 %), and 682,885 closed cases reported as deaths (6 %)
• August 02 at 01:14 GMT: 17,999,275 reported cases with 5,992,616 currently active, 12,006,659 now closed, and with 65,696 active in serious or critical condition (1 %), and 687,807 closed cases reported as deaths (6 %)

Comparing the early morning data points of August 2 as offered here with their July 2nd counterparts, we see:

• An increase of 7,197,434 overall confirmed COVID-19 cases,
• With an increase in the number of deaths from that, that comes to 168,964 in total. (See Part 42 for the full set of July 2, 2020 numbers for this.)

As I have repeatedly said, and throughout this series up to now, these numbers are going to get a great deal worse in the coming weeks and months. So will their more localized national counterparts and for more nations than just the ones that we currently see as heading in that direction according to their reported numbers as compiled and organized by entities such as the World Health Organization.

What do I see coming, and by the end of 2020? Given the numbers that we see now, and the systematic failures that we see playing out in so many nations now in containing this disease and its spread, I see a tripling or worse in the total number of confirmed COVID-19 cases, and with a corresponding increase in the number of fatalities reported from this too. And if healthcare systems and the hospitals that enter into them become overloaded in harder hit areas and in enough of them, the percentage of closed cases that end up as deaths from this disease, will go back up again. That has progressively dropped down to approximately 6% now, but that trend is not guaranteed to continue.

• Whether this set of predictions holds true, in whole or in part, will depend entirely on how we behave. Our fate here is in our own hands and in those of our neighbors.

And with that, I add four recent in the news reports of particular relevance here:

Coronavirus Is Back With a Vengeance in Places Where It Had All but Vanished.
A Viral Epidemic Splinters into Deadly Pieces. To quote from this news piece: “There’s not just one coronavirus outbreak in the United States. Now there are many, each requiring its own mix of solutions.” This, I add, is a direct consequence of a failure to lead from the White House in either organizing or even supporting an overall national level effort to contain this disease. State by state ad hoc has cost many tens of thousands of lives that should not have been lost. And more will follow them in this and avoidably so.
Children May Carry Coronavirus at High Levels, Study Finds. And to quote from this: “Infected children have at least as much of the coronavirus in their noses and throats as infected adults, according to the research. Indeed, children younger than age 5 may host up to 100 times as much of the virus in the upper respiratory tract as adults, the authors found.”
• That effectively demolishes what should be the last of a too-long held cherished fantasy that children and particularly young ones do not get infected with the SARS-CoV-2 virus, except perhaps rarely. They catch this infection. And yes, in spite of the still wishful thinking of the researchers behind this study, they almost certainly do actively spread it to others too. For the research report itself that underlies that above-cited New York Times piece, see: Age-Related Differences in Nasopharyngeal Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Levels in Patients With Mild to Moderate Coronavirus Disease 2019 (COVID-19). (The authors of this paper hopefully suggest that since very young children have small lungs they probably cannot spread this virus very effectively. But even toddlers can and do catch and share seemingly every cold and other human-to-human transmissible infection that they get near, so common experience argues otherwise.)

And I finish this starting point note here by adding that it is now believed that at least in the United States, some of the local and statewide epidemiological reporting that goes into US CDC and World Health Organization reports has underestimated their actual numbers of COVID-19 cases by up to 13-fold! Are my above predictions too cautious? For a recent reference on that see: Coronavirus Infections Much Higher Than Reported Cases in Parts of U.S., Study Shows. And I quote from that here with: “The number of people infected with the coronavirus in different parts of the United States was anywhere from two to 13 times higher than the reported rates for those regions, according to data released Tuesday (n.b. July 21, 2020) by the Centers for Disease Control and Prevention.”

And that somber opening note brings me to the complex of issues that I said at the end of Part 51, I would at least begin address here, that can be collectively represented in this topics point as:

• The question of how COVID-19 compares with other, now historical epidemics and pandemics, and I add here other human infecting diseases per se.

I said there that I take a somewhat different approach to answering this type of question than most would, and I begin doing so here in human prehistory, and going back tens of thousands of years and more at that. And in that, I begin with the human genome: our inherited genetic history, and our shared human story as encoded there.

To be more precise, I begin with the one type of heritable genetic content that can be expected to enter into the basic human genome from the outside, and with time both routinely and at something of a predictable pace: the piece by piece inclusion of inserted genetic sequences as initially sourced from retrovirus infections. Every human genome from members of every community worldwide is replete with such inserted retroviral genetic sequences, most all of which are damaged remnants of early and long forgotten infections.

To put that phenomenon into perspective for its scale of impact, see this research paper from Genome Biology:

Endogenous Retroviruses in the Human Genome Sequence, which I quote from here with:
• “Around 8% of the genome is derived from sequences with similarity to infectious retroviruses, which can be easily recognized because all infectious retroviruses contain at least three genes, including gag (encoding structural proteins), pol (viral enzymes), and env (surface envelope proteins), as well as long terminal repeats (LTRs.)”

Inserted genetic sequences of this type that are added into a genome through a germline so they are replicated from generation to generation, rarely face any selective pressure to maintain them unchanged. So random mutations accumulate in them that can be used to at least roughly calculate when they first arrived there. And some and even many of these retroviral remnants are of truly ancient origin as part of the human genetic heritage. But how many of these events are recorded in this way, comparatively speaking? How rare, or how common is this type of event for amount of such genetic material that has been added in this way, in comparison to the scale of functionally important proportions of the human genome?

• The entire human genome is comprised of some 3.2 billion base pairs of DNA. This includes some 21,000 protein coding genes that collectively comprise approximately 1% of that genome. So remnant retroviral sequences account for about eight times as much of the human genome as its entire set of functional protein coding genes does.

Much of this inserted genetic material at least appears to be silent, but it is known that some of these virally sourced sequences do play active gene regulatory and other functional roles too, and in both normal and pathological gene expression. They have, in this become usurped into use by the more strictly human portions of the human genome if you will, and to both positive and negatively pathological effect. So there is a lot more to this particular story than I will even try to address here. But one detail that comes from this brief narrative should be clear and even without my explicitly stating it. The history of humanity as a species, and of the hominid line that we evolved from in general is a history of facing and fighting off infections. And this up to here only acknowledges one small portion of the full range of pathogen-based infections that humanity has faced: retroviral infections that can leave persistent traces of themselves that can be maintained from generation to generation and even very long-term.

And I add a few more points here. These are infections that enough people were infected by so their traces have not disappeared by chance. And they are infections that enough people have survived from, so as to continue the species. How many have died from these diseases too, and how has that ongoing gauntlet of challenges shaped our species through its natural selection compelling forces? There is no way to know any real answer to that, but there are a variety of viral pathogens that are known to infect other species of mammals with devastating lethality.

Rabbits in their various species are subject to a variety of them including myxomatosis and rabbit hemorrhagic disease, both of which have been found to cause well over 90% fatality in at least some outbreaks. And for wild rabbits the mortality rate for myxomatosis has been found to be as high as 99%! So absent any medical care, public health measures or personally protective efforts, viral infections can at least occasionally represent extinction level events and certainly over specific infected regions and for certain mutations of the viruses involved.

Think of that as a worst case possible baseline for what will follow in this discussion. Has the human species ever faced an infection or otherwise-cased threat that has even begun to approach that level of impact? The answer to that is in fact yes, with remnant evidence of that also found in our human genomes. And this is now known to have happened early in the human story, and at least once, when our then living direct ancestors came close to the edge of dying off entirely. See: Endangered Species: Humans Might Have Faced Extinction 1 Million Years Ago and Humans Were Once an Endangered Species.

My point here is very simple, when looking to both those rabbit diseases and to this early human ancestry event. Humans are not in some way immune from or exempt from extreme danger from suddenly emergent adversity, and high population counts do not necessarily offer protection either. I will in fact argue later on in this narrative that higher population densities can increase risk there. But with this offered, let’s turn from the more abstract to consider historical if perhaps largely earlier recorded human diseases, for which there are at least some documenting records. And I begin that by noting that every ancient civilization that has left us anything in the way of a written record of any scale, has left at least some mention of plagues, pestilences and epidemics.

• In 430 BCE, an epidemic ravaged the Greek city state of Athens, when it was at war with its rival, Sparta. It is not known precisely what pathogen was involved, but it is clear that it swept through that city state and its crowded living quarters and with its poor hygiene, and that upwards of 25% of its citizens died of it.
• The Antonine Plague of 165 to 180 A.D. is estimated to have killed between 60 and 70 million people when smallpox swept through the Roman Empire. It is believed that this was brought to the Roman Empire on trade ships and that it most probably initially originated in China. One of its more societally destabilizing effects was that it devastated the Roman Army, leaving their empire open to invasion. It also directly wrought havoc on Rome’s finances and economy, on its food production and supplies and more, and that this contributed to the eventual downfall of the Roman Empire as a whole.
• The Justinian Plague (so named because it first struck during the reign of Justinian I) struck the Byzantine Empire starting with a first major outbreak that lasted from 541 to 542 AD. But this kept recurring, at least until 750AD. And this was both the most devastating epidemic (and in fact true pandemic) in history up to then with somewhere between 25 million and 100 million lives lost just during that initial outbreak. (I have read estimates of up to half a billion lives lost from the 1918 flu pandemic so this is not the worst ever but it is a strong contender so far for second place for that.)
• And only considering genuinely devastating pandemics for a next entry here, I turn to the Black Death. Its precise dates are a bit uncertain and particularly for its ending but they are often given as 1346 through 1353 A.D. (The people of places such as Wales might disagree in particular with that ending date.) And this is believed to have killed somewhere between 25 million and 200 million people though no one really knows true counts for this for any of these disease events and certainly up to here in this narrative. This is the other contender for second place as the worst disease outbreak ever.)
• In the New World, in what is now Mexico and Central America and further south extending into South America, the Cocoliztli, or pest in Aztec is believed to have killed upwards of 15 million people. It is believed to have been brought there by European explorers and it is known to have contributed to the downfall of the Aztec and with time to the Incan Empire too. We have to assume that 15 million figure is a significant underestimation so this might in fact be a second place contender too.
• And to complete this list, the Great Plague of London (of 1665-1666) and the Great Plague of Marseille (of 1720-1723) both took some 100,000 lives. Those numbers only reflect deaths resulting from these disease outbreaks in those cities themselves, though many fled them in an effort to avoid these diseases, taking them with them.

This is a very incomplete list and even just for this timeframe, only briefly noting a few of the more impactful of these events as took place over a roughly 21 century span. But it should be enough to begin to put our current COVID-19 pandemic into at least an initial perspective. I am going to at least briefly discuss a selection of more recent disease outbreaks in a next series installment, beginning with the 1918 flu pandemic as touched upon repeatedly up to here in this series, and even briefly in this posting too. And then I will step back from these specific disease outbreaks to discuss in more general terms, factors and conditions that can and do lead to devastating severity in them. And in anticipation of that, I repeat here a point that I made in passing in Part 49.

• The pathogen that is causally responsible for a disease outbreak is a mechanism that can be considered to be responsible for that disease itself. So it can be seen as holding specific medical and epidemiological significance there. But epidemics and more widely spread pandemics are shaped by, and even enabled by sociological and sociopolitical forces too.

And this perspective will emerge as a defining source of consideration, when I look back at this developing line of discussion to specifically address the questions implicit in its starting point:

• How COVID-19 compares with other, now historical epidemics and pandemics, and I add here other human infecting diseases per se.

I am going to forgo the second portion discussion that I was initially planning to add in here, postponing that for a later posting, given the volume of text I have just offered here. So I will only add one recent highly relevant news story reference here that relates to that, which I will pick up upon in a subsequent posting:

Coronavirus Data in the U.S. Is Terrible, and Here’s Why.

We have tremendous amounts of data coming out of this pandemic and its advance, and certainly when compared to even the most thoroughly recorded of the historic epidemics and pandemics as discussed here. But so much of that data is of such poor and inconsistent quality, and with so much unstated variation as to what is even included categorically in the key variables measured there, that this creates tremendous challenges for managing this disease. COVID-19 is just uncovering already existing gaps, weaknesses and failures there, that were already in place in healthcare and public health systems and even in technologically developed nations such as the United States. What will we learn from that moving forward, and how will we act upon any such lessons learned as we seek to arrive at and implement our healthcare and public health new normals coming out of this pandemic (or at least its first real outbreak)?

I will continue both of these lines of discussion: my historical comparison one and this, in the next installment to this series. Meanwhile, you can find this and my earlier COVID-19 related postings to this series at Macroeconomics and Business 2 and its Page 3 continuation, as postings 365 and following.

Meshing innovation, product development and production, marketing and sales as a virtuous cycle 25

Posted in business and convergent technologies, strategy and planning by Timothy Platt on August 1, 2020

This is my 25th installment to a series in which I reconsider cosmetic and innovative change as they impact upon and even fundamentally shape product design and development, manufacturing, marketing, distribution and the sales cycle, and from both the producer and consumer perspectives (see Ubiquitous Computing and Communications – everywhere all the time 2 and its Page 3 continuation, postings 342 and loosely following for Parts 1-24.)

I have been discussing two basic paradigmatic models of how individuals and communities respond to change and innovation and to New in general here, since Part 16:

• The standard innovation acceptance diffusion curve that runs from pioneer and early adaptors on to include eventual late and last adaptors, and
• Patterns of global flatting and its global wrinkling, pushback alternative.

And as part of that, I have raised five specific case in point contexts in which the issues of acceptance or rejection of change, become important and both individually and societally. I raised three of them in Part 23 and began discussing them there:

1. The development of drought and disease resistant crops that can be grown with little if any fertilizer and without the use of insecticides or other pesticides,
2. Russia’s Novichok (Новичо́к or newcomer) nerve agents, and
3. Disposable single use plastic bags and other petrochemical plastics-based wrapping materials.

And I cited two more in Part 24 and at least briefly and selectively discussed the first of them there:

4. Antibiotics and their widespread use, and
5. Vaccinations and certainly as they have become vilified and particularly in online social media.

My primary goal for this posting is to discuss that fifth and final case study example, and how it sheds light more widely than its specific issues might suggest, on the acceptance and rejection of change per se. In this context that means citing this example as a poster child example of how science per se, and its findings are coming under ideologically framed and supported challenge and even direct attack.

I will circle back to at least briefly reconsider the first three of these case study examples and will then move on from them to address the issues of impact and of who is affected by what acceptance or rejection-driven action, where they might be very different than the people who seek to shape the messages that drive this. But before turning to those issues, I begin here with example Point 5 and vaccinations, and with a particular focus on childhood vaccinations against diseases such as measles, mumps, rubella, tetanus and polio in mind.

I wrote in Part 24, of antibiotics as “at the very least qualify as a strong candidate for being considered the most significant healthcare advancement of the 20th century.” There are two other candidates that come to mind for me as qualifying for at least top four or five status as greatest healthcare investments, and even for all time up to now:

• Improved public sanitation with that including widespread access to safe potable water and safe and effective removal of sewage and other potentially disease carrying waste,
• And the development and widespread use of safe and effective vaccinations against diseases that were once highly contagious, deadly scourges.

I wrote in Part 24 in an example 4, antibiotics context of epistemic bubbles, where people only listen to others who start out sharing their views, and with those connected communities only seeing, hearing, considering or believing facts, “facts”, rumors or opinions that support their already pre-established conclusions on whatever issues are under consideration.

I write here, in this context of the anti-vaccination movement. This began in its earliest iteration when Edward Jenner first developed his cowpox based vaccination against smallpox in the early 1800’s, with people claiming, among other things, that this violated their religious beliefs to administer an animal-sourced material into their body through a wound through their skin. See this History of Anti-vaccination Movements.

The modern version of this that I would primarily focus upon here, stems in large part from concerns over the use of a particular preservative agent: thimerosal – a mercury containing organic compound that was first used in the 1930’s and in both medications and vaccines.

To be clear here, thimerosal is a toxic compound in higher dose exposure. But it is safely broken down and disposed of by the body, with most of it eliminated through the intestines in fecal matter and in a matter of days, when exposure is very small. Only trace amounts of it are used in vaccination preparations when this compound is used there at all. But a since-discredited study was published, based on falsified data that claimed that even the most minute exposure to this in children could lead to their developing autism. See Thiomersal and Vaccines. And with that study, the modern anti-vaccination movement was born.

• This study has been disproven and with carefully conducted clinical research to back that. But perhaps more to the point, backlash from a concerned public brought the pharmaceutical companies that produce vaccination materials, to remove thiomersal from their preparations. That should have made this a non-issue.
• But the same people who said that they would never vaccinate their children because of possible thiomersal-based risk, now generalized their fears and their responses to them through social media and related activism to attack any childhood vaccination at all.
• I have been framing this narrative in terms of two basic paradigmatic models of acceptance and rejection and this story fits fairly stronger into the second of them and even if global flattening per se is not always in play here; this is pushback driven. And as I intimated above, this fits into an ideologically driven pushback against science per se with that including global warming denial, and in our current COVID-19 context, denial of the relevance or the positive value of disease containment efforts such as social distancing and the use of personally protective equipment.

Bringing this back to the issues of vaccinations and of childhood vaccinations per se, communities that have come to exhibit higher levels of anti-vaccination resistance, have also shown reemergence of the childhood disease scourges that those vaccinations had seemed to end: that they had at least seemingly made nightmare stories of a not to be repeated historical past.

• I have to ask this question in this context. How could a parent possibly explain to a child of theirs why they became paralyzed from polio when a safe and effective vaccination was available that would have prevented that from happening, that that parent refused? Unfortunately, this denial has led to new cases of that disease too, so this is not just a theoretical question.
• My point here is that the issues that I raise here are consequential and in the lives of real people and real communities. And this is true for a much wider range of possible accept and embrace, or reject and refuse contexts than just the few that I could raise here.

I am going to return to the first three examples again in the next installment to this series where I will consider, among other factors, the risks and costs, versus positive benefits balances that they raise – and how those balances can be variously understood and evaluated as even just being meaningful possibilities. And as noted above, I will also consider the dichotomies and even disparities of who pushes accept or reject messages, and who more directly faces and has to deal with the consequences of that where they might in fact be very different people. I will at least briefly reconsider all five of my above-listed examples in light of that.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. And see also Ubiquitous Computing and Communications – everywhere all the time and its Page 2 and Page 3 continuations.

China, the United States and the world, and the challenge of an emerging global COVID-19 coronavirus pandemic – 51

Posted in macroeconomics by Timothy Platt on July 30, 2020

This is my 56th posting to specifically address the COVID-19 pandemic that we now face and that by now has found its way into essentially every nation on Earth, and into every facet of our lives. And it is also the 51st installment to this specific series on that.

As usual, I begin this installment with newer updates to a set of basic epidemiological findings, sharing more recent globally sourced data as offered by the World Health Organization as to the current overall state of this pandemic.

• July 29 at 01:36 GMT: 16,891,150 reported cases with 5,772,121 currently active, 11,119,029 now closed, and with 66,504 active in serious or critical condition (1 %), and 663,335 closed cases reported as deaths (6 %)

• July 30 at 01:58 GMT: 17,184,770 reported cases with 5,818,014 currently active, 11,366,756 now closed, and with 66,392 active in serious or critical condition (1 %), and 670,152 closed cases reported as deaths (6 %)

I begin the first part discussion of this posting by posing a question that is fundamental to effectively addressing, and with time resolving this pandemic crisis:

• What are the key factors: the key decisions and actions that have to be taken in order to speed up that process, and save lives and restore our economies in the process?

Ultimately, all possible such actions as raised there, gain their value in one shared way. They all would serve to systematically reduce and then stop disease transmission from individual to individual, and from that through communities. This obviously applies to social distancing and related exposure limiting measures, and to the use of face masks and gloves as appropriate. It also applies to the development and deployment of a vaccine against COVID-19, with that possibility a major goal that is being worked towards, worldwide.

I begin this discussion there, as the United States and a number of other nations have invested, and offered to invest billions of dollars or their local currency equivalents in both developing such a vaccine and in providing it to millions of their citizens and either free or at low cost. There are, as of this writing, 42 separate and distinct COVID-19 vaccines under development that have at least entered Phase 1 clinical trials to prove their basic safety. See this COVID-19 Vaccine Tracker as provided by the Regulatory Affairs Professionals Society. The US government has chosen three of them to help fund though Phase 3 trials now, with each calling for 30,000 volunteers, half of whom would receive the test vaccine, and half a saline solution placebo. And their goal is to validate a successful candidate that would:

• Significantly reduce the infection rate,
• Significantly reduce the percentages of those who are infected who go on to become seriously or critically ill from it, and/or
• Significantly reduce the mortality rate from this disease.

There are other such putative COVID-19 vaccines that are already in, or rapidly approaching Phase 3 trials. And all of these more advanced efforts look promising at least in laboratory studies, where their products in testing all provoke antibody production and T cell activation. Note: T cells are white blood cells that specifically target viral pathogens so that is a very positive indicator, as is antibody production per se. But that still leaves the question of whether this is the right antibody production and T cell response for actually combating this particular virus in actual people. Laboratory success does not always translate into public health success in this type of situation, where such a vaccine candidate has to be able to function in the more complex environment of full, active human bodies.

But let’s assume that one or more of these vaccines do work, at least initially. How long would they continue to hold immunizing value? And would they prevent infection or would they create asymptomatic carriers who do become infected if exposed to the SARS-CoV-2 virus but who do not become ill from it themselves? And how long would this immunity last, given whatever administration protocols are arrived at for them (e.g. single dose, or two doses spaced some interval of weeks apart for when they are administered)?

I have written of immediate immune system responses in this blog, with production and proliferation of early nonspecific IgM antibodies followed by production and proliferation of later more pathogen-specific IgG antibodies. They and associated T cell and other response mechanisms defeat an invading pathogen when a person becomes ill from a disease such as COVID-19, and their coordinated action leads to recovery from it. I have also written here of the development and maintenance of monoclonal memory cell lines that remember this infection after recovery, and that can lead with a very targeted response to the causal pathogen involved and essentially immediately, upon re-exposure.

• Effective response and recovery from infection with a pathogen such as this virus calls for a direct immune system response that is based on antibody and T cell activity.
• Fast highly targeted response as can be launched by maintained memory cells can stop reinfection before it can really start. Longer term immunity is all about memory cells and whether they are maintained or lost.
And this brings me to some points of fact that are particularly relevant here, for this discussion:

• While there are exceptions, as a general rule the strongest long-term immunity to a disease comes from recovery from it, as that can leave you with the most robust and persistent memory cell line for combating it upon re-exposure. So if people who were provably infected with the SARS-CoV-2 virus and who recovered from it, now come back with new infections from that same virus and with new positive diagnostic test results to show that, this would be very troubling.
• And with that, I offer this news story link: Can you get coronavirus twice? Doctors are unsure, even as anecdotal reports mount.
• These reinfection reports are considered anecdotal because it is possible that these people suffered relapses from a single infection with this virus, rather than fully recovering from it and becoming freshly infected with it again. So their cases are cautionary, rather than definitive in nature and certainly as of now. But their implications, if valid, have to be considered and strongly so in any planning going forward, and certainly given the severity of the consequences faced if immunity here does not hold.
• To round out that set of points, I add: Can You Become Reinfected With Covid-19? It’s Very Unlikely, Experts Say and this report from the US National Center for Biotechnology Information: Clinical recurrences of COVID-19 symptoms after recovery: viral relapse, reinfection or inflammatory rebound?
• I personally, take a more cautious approach for this and simply view possible reinfection as representing an open question. And then, if it does demonstrably occur, that raises more questions, including:
• How commonly do people who have been infected with this virus become susceptible to reinfection with it, and over what time frames? The implications there are very different if this is rarely or very rarely, than it would be if the answer is often and most likely within six months or less.
• And another such question would be: do those who are susceptible to reinfection share any readily identifiable personal health condition or demographic markers in common that would help to identify them in advance as needing special follow-up care? That follow-up would include they’re being more closely monitored for possible reinfection and they’re taking precautions post-recovery for preventing they’re becoming reinfected with the SARS-CoV-2 virus, and exactly as if they had never had it.
• And in this questions raised context, I add one final report as published by The Scientist: Studies Report Rapid Loss of COVID-19 Antibodies (which might or might not indicate development of a robust memory cell response.)

Depending on how the issues and questions as raised there, actually resolve through real world empirical findings, issues such as “immunity passports” might become moot. For a reference on that approach to possible more expansive reopenings, with possible recovery-based immunity supporting them, see this World Health Organization paper: “Immunity passports” in the context of COVID-19.

The possibilities raised here, if realized, would in fact impact upon any possible reopenings, however staged or paced. And if immunity does not persist for a meaningful length of time, this would challenge vaccination programs per se as a whole.

I raise these issues here as a source of contingencies that need to be considered, even if they are not likely and certainly in anything like their worst case scenario forms. But this still leaves open the issue of possible new mutations in the SARS-CoV-2. I have raised and discussed this complex of issues in this series, as a matter of general principles (see for example, Part 36.) So I end this portion of this posting by offering another cautionary note news piece that just came out: The Coronavirus Could Dodge Some Treatments, Study Suggests. And with that in mind, as to how mutations can facilitate the spread of this disease between people, I add this news piece on how at least some mutations in this virus can facilitate spread and infectivity within individuals too, affecting its severity as a pathogen: Mutation Allows Coronavirus to Infect More Cells, Study Finds. Scientists Urge Caution.

All of us, and regardless of our age, are vulnerable to this disease and even to its worst possible consequences. To put a very tiny, innocent and vulnerable face to this, I share a link to this family tragedy as reported by CNN: A 26-day-old baby tests positive for Covid-19 following autopsy in Pennsylvania. So the issues that I raise here and in similar ongoing discussions, have impact and meaning that none of us can safely ignore – that none of us should try to ignore as a matter of basic morality and responsibility.

I am going to turn in the first part of my next installment to this series to a complex of issues that I have been thinking a great deal about, and that a reader has recently asked me to explicitly write about here: the question of how COVID-19 compares with other, now historical epidemics and pandemics. We are now far enough along in this pandemic so that at least some answers there are coming into explicit focus. At the very least, some crucially important questions that relate to this are. In anticipation of that narrative to come, I admit that I will take a somewhat different approach to parsing and analyzing these issues than most would, but hopefully that will at least raise new questions that need to be resolved.

And with that, I turn to the second part of this posting, and to the longer-term and (ideally at least largely) post-COVID-19 new normals that might arise. I offered a to-address list at the end of Part 50 that I repeat here as I begin addressing them:

• Standardizing medical information and the questions of what standards and with what overriding purposes they would be so developed and organized – e.g. insurance use and coding for claims, versus standardization for more directly personal healthcare purposes.
• Controlling drug costs and drug availability issues and challenges.
• The challenge of hospitals and clinics that cannot provide first rate service, and where and why.
• And the emergence and elaboration of telemedicine as disruptively new change, and both as medical appointments might be held remotely and as new types of online connectable technologies are brought into this, informing such encounters.

And I begin with the first of those points and by stating the obvious:

• Medical information is systematically coded so as to characterize the individuality and the even sometimes overall idiosyncratically unique of particular individual patients, for how they follow standardized known patterns, for at least key aspects of their medical conditions.
• And on a testing and treatment side, standardization becomes a standard, essential fact too.

From a clinical and healthcare provision perspective this means organizing what is known about individual patients and their particular medical needs, in ways that support clinicians bringing the best of what is known in general that would fruitfully apply to them, to their patients. And cause-and-effect consistency does mean that different patients with similar medical issues will show a great deal of similarity for what they have and for how it might best be treated – with that individualized by taking into account their own particular medication sensitivities and other medically relevant considerations (such as any comorbidities.) So patients might still be individuals and they might need to be treated as such, but this still means they’re fitting known and even highly consistent patterns for key details and both for what their medical issues are and for how they might best be treated for them. Coding there, is primarily a matter of applying standardized medical terminology.

Alternatively and at least sometimes in conflict with that, healthcare insurance coverage providers have their own system of codes (in number and letter forms) and both for medical conditions and their issues and for tests, procedures and treatments offered. And they set reimbursement rates for services rendered, according to those diagnostic codes as submitted by healthcare professionals or the facilities they work for, or by patients themselves (at least situationally.) And when a patient arrives, for example at a hospital emergency room with multiple emergent medical issues, insurance-oriented coding systems can be used to determine what of that in fact can even be covered in part by a patient’s health insurance policy, of all of it. And this fact can and does influence how codes are selected and entered into that insurance company’s system, so as to generate as much reimbursement coverage as possible, and certainly where the hospital itself would have to eat some or even all of the difference there as a loss.

On that note, I add that I was heavily involved with hospital emergency rooms in the New York City metro area, that receive 911 call ambulances, and for seven years as a member of the New York City Regional Emergency Medical Advisory Committee – and I have never heard of a hospital that did not lose money in its institution’s overall budget and finance calculations, and on an ongoing basis.

Coding, or rather medical nomenclature and coding, and their use do not always align in any functionally productive sense. More importantly, the healthcare and reimbursement needs that they would variously address do not always match each other all that well. That is the basic challenge. I am going to continue this discussion in the second part of the next installment of this series, and will then continue on from there to the second of the above to-address topics points where I will discuss costs, focusing on pharmaceuticals for that.

Meanwhile, you can find this and my earlier COVID-19 related postings to this series at Macroeconomics and Business 2 and its Page 3 continuation, as postings 365 and following.

Finding virtue in simplicity when complexity becomes problematical, and vice versa 22

Posted in social networking and business by Timothy Platt on July 29, 2020

This is my 22nd installment to a series on simplicity and complexity in business communications, and on carrying out and evaluating the results of business processes, tasks and projects (see Social Networking and Business 2 and its Page 3 continuation, postings 257 and loosely following for Parts 1-21.)

I have been discussing in Part 21 and Part 22, the risk management side to how a business controls access to the confidential and sensitive information that it gathers in or develops, and that it holds. And I started that phase of this series’ overall narrative with a focus on externally sourced, legally mandated and supported challenges to their right to control and limit access there. Then at the end of Part 22, I raised an alternative scenario challenge that is perhaps best seen as a small business and early stage business challenge:

• The possibilities of a business setting up a system of access control for risk managing the holding of sensitive information such as personally identifying customer records data,
• But where more senior management – there the founder and owner, sets themselves as not having to follow that policy, and even when that means they’re violating it on an arbitrary and ad hoc basis.

Let’s assume that the basic facts of the first of those two bullet points is true, with a Chief Information Officer, or a Chief Sales Officer or some other particular administrator officially in charge of and responsible for managing a formally developed policy for information control there. And they are responsible for its being followed, with that including the management of who has access to what specific information and under what circumstances (e.g. customer support center representatives having access to appropriate data fields from the records of specific customers who they are working with through a phone center or through online chat, when they are accessing this through their work computers there.) And that policy serves as a framework for specifying work processes that would be allowed there and with that controlling any confidential information sharing that might be allowed.

Generalizing from the specific scenario of Part 22 and the second bullet point, above, what happens if someone senior on the table of organization seeks to in effect, throw their weight around in gaining special privilege access and either for themselves or for a representative who works for them, and who would not normally be allowed to see this information themselves?

• One obvious at least partial answer to that question is that this beach of formal process and policy can only serve to punch an ad hoc hole into that policy and its intended implementations.
• But when this type of exception creating breach is pushed through with the compelling pressures of rank and position in the business to support it, that effectively ends that information policy as a risk management resource, as a whole.

There is an old adage that comes to mind for me in this context. “You can’t be half pregnant.” Either you are or you are not. Either a business has an information management system that it honors through consistently following it, or it simply claims to have one on paper, where that in practice is no longer actually, consistently or reliably there.

And this brings me back to the issues of intermediation and disintermediation, and the details of the types of control that setting up and making use of a system of information access control actually involves. And that is where simplicity and complexity, and their respective costs and benefits, directly reenter this narrative again. And this is where all of this becomes a risk and benefits exercise and one in which different people can see and understand those issues very differently from each other and on both sides of any given risk and benefits balance.

I am going to at least begin a discussion of that complex of issues, in light of discussion entered into here in earlier postings and in this one, beginning in a next series installment. Meanwhile, you can find this and related material at Social Networking and Business and its Page 2 and Page 3 continuation pages. And also see my series: Communicating More Effectively as a Job and Career Skill Set, for its more generally applicable discussion of focused message best practices per se. I initially offered that with a specific case in point jobs and careers focus, but the approaches raised and discussed there are more generally applicable. You can find that series at Guide to Effective Job Search and Career Development – 3, as its postings 342-358.

China, the United States and the world, and the challenge of an emerging global COVID-19 coronavirus pandemic – 50

Posted in macroeconomics by Timothy Platt on July 27, 2020

This is my 55th posting to specifically address the COVID-19 pandemic that we now face and that by now has found its way into essentially every nation on Earth, and into every facet of our lives. And it is also the 50th installment to this specific series on that.

As usual, I begin this installment with newer updates to a set of basic epidemiological findings, sharing more recent globally sourced data as offered by the World Health Organization as to the current overall state of this pandemic. And I begin that by noting:

• July 22 at 01:22 GMT: 15,091,817 reported cases with 5,362,262 currently active, 9,729,618 now closed, and with 63,785 active in serious or critical condition (1 %), and 619,409 closed cases reported as deaths (6 %)

And to highlight the scale of change taking place and on a day-to-day basis since then, I skip ahead from that to:

• July 28 at 01:17 GMT: 16,634,647 reported cases with 5,752,992 currently active, 10,881,655 now closed, and with 66,560 active in serious or critical condition (1 %), and 656,069 closed cases reported as deaths (6 %)

The day-to-day increases that we all see in these numbers are deeply troubling, but look at what a six day change looks like! Just focusing here on two of the variables that I have been reporting on here, of this still selectively limited set:

• The total number of new cases jumped by 1,542,830 new confirmed COVID-19 infections in that still brief period of time.
• And the total number of reported deaths went up by 36,660.

This, I stress, was in just six days. It is easy to lose track of the ongoing scales of all of this when only looking day-to-day. It is vitally important that we never lose track of what those numbers mean, or of the fact that every single digit increase there, represents a unique individual person and their life.

I am going to start my more here-and-now first half of this posting by proposing a crucially important point of detail that I would argue is going to prove to be tremendously impactful moving forward, in shaping both our more immediately emerging situation with this pandemic and beyond.

But before offering that emerging understanding, I set the stage for doing so by reiterating some relevant points of observed detail as already noted in this series:

• It is now known and I add well established that on the order of 30% of all of those who become infected with the SARS-CoV-2 virus as adults, remain asymptomatic from it even as they go through what on average are fairly extensive periods of active infection in which they are actively contagious to others. And it is now known that older children contribute to that too and in a similar manner.
• And while wearing face coverings and social distancing and related disease containment measures are the only widely available tools that we actually have at our disposal that might limit the spread of this disease and save lives from it, they have all become toxically politicized and certainly in countries such as the United States where many people refuse to comply, claiming that these measures are just left-wing attacks on their personal freedom.

That combination of empirically valid truths is going to make it effectively impossible to prevent recurring waves of this disease, and both in areas that have never really brought itunder control and for areas where they ostensibly have too. That is because this combination of factors, coupled with “mask wearing and social distancing fatigue” from people who have grown tired of taking such protective measures but who have done so, will insure disease reemergence and even in cities and larger regions that have seen a virtual secession of at least overtly symptomatic cases.

This will all have a number of significant consequences. And one of them is that we can no longer hope to see a simple tapering off of, and ending of this pandemic that would follow anything like a simple here-then-gone pattern. And we may in fact never see a true end to this disease as an ongoing healthcare and public health issue, and even if an effective vaccination for it is developed. Remember, in that regard, the anti-vaxxers who I wrote of in Part 48 who are already refusing any vaccination against the SARS-CoV-2 virus for themselves or their families and even before one is tested and made available – simply because it is a vaccine.

COVID-19 is not going to slip quietly into history; we will see it fade and remerge and fade and reemerge and repeat and repeat and even if new mutational forms do not arise that would evade any immunity that might be gained from becoming infected and recovering, or from getting vaccinated against this disease.

And even if an effective vaccine is developed, how widely can it be made available and both in developed world nations and in the developing world?

I am offering this posting as a brief note as far as word length is concerned. But the basic point that I raise here will shape all that I add to this series from now on, and both for its here-and-now first part discussions and for its post-COVID-19, new normals oriented discussions. And I simply add here in that later context that any new normal to come will have to be affected by the ongoing at least sporadically reemerging presence of this disease.

I am going to turn in the first part of the next installment to this series, to at least begin to more fully consider a complex of issues that are now starting to come into explicit focus as new public health sourced data is developed: the combined issues of vaccination development and disease resistance as that might or might not remain in place or fade away. I have touched upon these issues already in this series, but will look further into them now, as emerging data takes that more out of the abstract and away from speculation.

I will also, as a second half of the next installment of this to come, begin addressing a set of topics points that I list here in anticipation of their discussions to come:

• Standardizing medical information and the questions of what standards and with what overriding purposes they would be so developed and organized – e.g. insurance use and coding for claims, versus standardization for more directly personal healthcare purposes.
• Controlling drug costs and drug availability issues and challenges.
• The challenge of hospitals and clinics that cannot provide first rate service, and where and why.
• And the emergence and elaboration of telemedicine as disruptively new change, and both as medical appointments might be held remotely and as new types of online connectable technologies are brought into this, informing such encounters.

Meanwhile, you can find this and my earlier COVID-19 related postings to this series at Macroeconomics and Business 2 and its Page 3 continuation, as postings 365 and following.

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