With media attention focused on the November US elections, domestic protests against globally condemned police violence, and a lingering pandemic brought on by the Corona Virus, the continued decoupling between the US and China has received less attention than would be expected. Integration between China and the Western world has driven much of global growth for the past 30 years, and this disentangling will be a major driver for the next 30. There are no shortage of articles with political angles, such as the National Review’s American Universities and Their Ugly Relationship with China.
The two main events that caught media attention earlier in 2020 and late 2019, prior to the lockdowns due to COVID 19 occurred at the University of Texas and Harvard, as indicated the in quotes below:
“Investigators allege that beginning in 2011, Lieber became a “Strategic Scientist” at Wuhan University of Technology in China, unbeknownst to Harvard University. From at least 2012 through 2015, according to prosecutors, Lieber was a contractual participant in China’s Thousand Talents Plan.
“Under Lieber’s program contract, prosecutors say he was paid $50,000 a month by WUT and living expenses up to $158,000. He was also awarded more than $1.5 million to establish a research lab at the Chinese university, prosecutors said.
“University of Texas professor Bo Mao, prosecutors say, took proprietary technology from an American Silicon Valley start-up and handed it over to a subsidiary of Huawei, the Chinese telecommunications conglomerate.”
Concern about this activity is not contained to the US, as Australia reports that the Chinese Communist Party is organizing efforts to reduce free speech at two Universities where protests supported Hong Kong independence;
“Other Chinese students in Australia say they are afraid of speaking out against the party line, fearful they will be reported by their compatriots to the Chinese embassy or have their families in China targeted.”
Communicating complex ideas clearly is a valuable skill; skills improve with practice. I started this blog in 2010 without any goals and shifted to using it as a place to deliberately improve my communication skills. I followed four steps:
Choose a Goal
Pick a Metric
Create a Method
Follow the Numbers
Focus has been a huge help (Goldratt Concepts). I’ve been really pleased by how summarizing my personal thoughts for public feedback has helped develop thoughts that have served as the foundation for my professional career. What began as an undirected attempt to share my observations on domains where my background is unique, has shifted to taking those unique perspectives and applying them systematically to broader areas of society which attract a larger readership.
Background
As part of finishing my MBA from UNC in 2005, I’d written some papers around private equity and economics that we were using in the early days of marketing Parish Capital. I registered my own name as a domain and was using the blog hosting platform that came with the original Mac as the publishing software.
My target audience, which I hadn’t identified at the time – was the pensions and other institutional investors that were reading those early documents. My goal was to put those writings out into the public domain so that we could easily reference them. Later, after leaving Parish in 2008, those same documents would serve as a framework for consulting with private equity groups (Operational Framework for PE).
Building out my catalog of writing, I had only 3 themes I wanted to pursue.
Better writing.
Insight.
Relevant topics.
1/ Choose a Goal
In 2013, after returning to work in manufacturing, I developed clearer goals. Leading international teams, it became important to use social media, blogs, and other tools to help a global team get to know me as a person. I wanted to make use of modern technology to create a digital water cooler that allowed a globally distributed professional organization with very diverse skills to get to know each other despite vast differences in culture and vast distances between us.
Writing blog posts was an easy way to spend more time writing clearly about a topic, and then allow those who cared to read it at a time of their choosing. My team knew that I blogged, as I’d often cross post to LinkedIn or share topics that I’d developed here and reference back to the original drafts.
2/ Pick a Metric
It was easy to follow the readership and click metrics that are generated by WordPress. As I wrote on topics I could see which ones picked up traction and which did not. I’ve enjoyed working in niche areas of manufacturing and finance, which meant my target audiences were always going to be slim. As I wrote about the industrial topics I was interested in professionally – filtration, customer service, electrospinning, membranes, biopharma and others – the difference between a ‘big hit’ writing and a piece that was a dud could be just a few hundred clicks or readers.
This meant that early on it became clear that topics with a long tail – topics that could continue to get new readers, or win in long term search optimization games – became important. Posts that got only a few early clicks, but that could ramp over time to create their own gravity and become important contributions, required a lot of thought to identify and pursue.
Then on June 13, 2013, when writing a post thanking my European IT team for helping me understand why they were concerned about US spying on internationally hosted servers, I had a hit. Suddenly it was clear what a single big post could do and what kind of reach you could achieve with the right voice, the right target audience, and the right messaging.
I had a few other ‘big’ posts writing about things that just interested me, and it was hard to predict what would or would not lead to clicks:
Replicating the success of that big post was not going to come easily. Because I wanted to write about the things I new most about, and to which I could add value in my commentary, I had been constrained to niche areas of finance and manufacturing. Everyone loves to write about venture capital – but the reason so much writing about VC is targeted to entrepreneurs is because that’s where the numbers are. From a GP and LP standpoint – when I was in the industry from 2003 – 2008, there were probably no more than 10,000 practitioners. A big selling book would barely cover the cost of publication, and a blog targeting this sector would quickly find itself having to reach out to other areas of finance to generate enough traffic.
I’d had a big hit – and with that needed to create a method to create more hits. For that, we needed a system of consistent writing. I knew I wanted to improve my writing and write more clearly, and with the combination of these insights, I started re-reading some of my favorite books.
I re-read with the goal of taking away the best written sentence on each page. I then rolled up the best sentences on each page, to come up with the best sentence per chapter. As I pulled these summaries together, I realized that the summaries might be useful themselves, and with that I wrote summaries on the following books:
I’ve started some other reviews – I published only a few chapters on Charlie Munger’s “Poor Charlie’s Almanack” after seeing that it was a coffee table book that was regularly checked out of libraries. I love financial writing and have read most of the Wiley Investment Classics – but the writing was choppy and the stories repetitive. I couldn’t force myself to write more than two summaries. The focus on books – and the aversion to bad books – felt thematically similar to my in depth look at Musso’s thesis on Materials Science Commercialization.
By focusing thematically on non-fiction books, I could develop a clearer perspective and body of expertise in what I was analyzing. I could then use my professional background to create insights that readers found valuable.
4/ Follow the Numbers
Most recently, with the corona virus, Covid-19, and the quarantines – I’ve focused my writing on these areas, but I focused on areas within the problem to try to bring a helpful and unique angle. I looked at the disease from the point of view of the goal, and then tried to get deeper into the local numbers in Massachusetts. At the start of the crisis, when medical PPE was a major issue, old blog posts I’d made on filtration topics were being inundated – which led to some more topical responses on different safety and hospital needs.
By having worked on the first three steps, I was able to react to the new situation and quickly see the differences in click through rates. This made it easy to tell when a topic had found resonance with a larger audience.
Continuing to look at how states message the Covid-19 pandemic from the earlier review of the top 5 states by number of citizens with a positive diagnosis, we now look at states 6 – 10; Pennsylvania, Michigan, Florida, Texas and Georgia.
Florida’s COVID-19 mantra, “Safe. Smart. Step by step.” is so clear and supported with their protection of the elderly that it likely saved lives.
Florida leads the way with more of a ‘marketing style’ message. Yes, they share public health data, but their website has been hands down the clearest.
Florida’s messaging is so clear, and supported by the Massachusetts mortality numbers, that it changed my personal messaging – “Protect the elderly.”
Many states have the de facto ‘Public Health’ style update; here are the numbers, make of them what you will.
None of the states have made an attempt to report on ‘Recoveries’ – which would be a compelling aspect to focus on from a marketing standpoint and alignment of public incentive.
California has the 5th highest number of positive diagnoses in the US, 67,939 as of Wednesday, May 13. When combined with the four states with the most diagnosed cases; New York, New Jersey, Illinois and Massachusetts – the daily California Coronavirus COVID-19 Response shows that the disease continues to be deadly in the elderly population.
95% of deaths come from those 50 and older in the top five states; New York, New Jersey, Illinois, Massachusetts, and California.
Differences in how states report age groups make it hard to compare – the data also indicates that 85% of the deaths come from those age 60 and older.
38,344 of the 40,227 deaths recorded to date – 95% – are from those age 50 and older.
The missing component in all of these reports is clearer visibility about the number and status of recovered patients.
COVID-19 kills senior citizens at a very high rate.
New York, New Jersey, Illinois and Massachusetts represent the four states with the highest number of diagnosed cases of corona virus infection, 51% of all US diagnosed cases, and 57% of the deaths.
As a resident of Massachusetts, I’ve looked at the daily Massachusetts COVID-19 Reporting Dashboard and compared it to past Massachusetts death reports – the most recent being 2017. As we start to emerge out of the quarantine, comparisons to the other states show that the differences in reporting styles continue from state to state.
The Disease: Covid-19’s Impact
The disease is lethal to older member members of society. In Massachusetts #C19 will be the third leading cause of death in 2020 with a mortality rate of 70 per 100,000, and a rate of 1,009 for those 70 and older. The data from Illinois, New York and New Jersey show the same pattern. Each of the states also have special call outs for infections in assisted care facilities and nursing homes.
C19 gets into nursing homes and the elderly population and accelerates the rate of death significantly. This population will require special protection going forward.
The Style of Reports – Why are All the Age Brackets Different?
Ideally, these death reports would be summarized in the same way from state to state and also roll up to the CDC and Federal level in a consistent fashion. They don’t.
New Jersey uses different age brackets – closer to that of the CDC.
New York adds two age brackets that Illinois and Massachusetts don’t have – ages 0 – 9 and 80 – 89.
Illinois and New York have an ‘Unknown’ age bracket. Is that really possible? Is it really just ‘Not yet fully known with precision’? If we’re putting the deceased into 10 year age brackets, can an estimate be made?
Illinois has the only branded approach to C19, with their “Restore Illinois.”
Massachusetts circulates a great daily report on C19 every day at 4 PM – the “COVID-19 Dashboard” is posted every day and contains a very detailed, 30+ page summary of the State’s Covid response reporting.
It must be very difficult to pull together these reports, and they are very thorough compared to other states. Why are they so hard to compare to Massachusett’s own annual death reports?
The ideal mortality report shouldn’t be so singularly focused on deaths from SARS-COV-19 and the Corona virus.
Show the daily death totals.
Assign every death to a category.
The report should be an ‘accelerated’ reporting of the annual death data, just done on a daily basis.
The current reporting as done is very difficult to compare to any other death reporting. Why?
Why does the Massachusetts Data look so different than other sources of mortality data?
The age groups are different than the State’s own 2017 mortality report.
The State of Massachusetts’s 2017 mortality report tracks different age brackets than the CDC shows.
This data can’t be fixed by downloading the raw data / information – it still doesn’t line up.
The following are all constraints in how data is reported:
The State of Massachusetts releases daily updates on the status of COVID-19. Slide 11 of the “Massachusetts Department of Public Health COVID-19 Dashboard” has typically covered death statistics to date broken out by age group. (Data here is from the Saturday, May 9, 2020 Dashboard.)
The 4,840 deaths to date in Massachusetts, when adjusted by the current population of 6,939,373 and then back to the standard method shows a mortality rate of 69.7.
Massachusetts most recent posting with thorough analysis of causes of death in the state – the ‘2017 Death Report’ can be found in the states Death Data. If the 2020 year-to-date C19 data is used as a benchmark in the 2017 data – then C19 would be the third leading cause of death, behind only cancer (149.3 deaths per 100,000 people) and heart disease (134.7 deaths per 100,000 people).
Covid-19 Mortality is Highest in 70+ Year Old Population
Massachusetts age brackets share mortality in ages 70 – 79, and 80+ as being dramatically higher for the disease. Nationally, this data is tracked in different age brackets – with the two oldest brackets being 75 – 84 and then 85+. (I’m preparing a summary of all the ways that C19 data analysis is made difficult.) The Center for Disease Control publishes an annual summary, “National Vital Statistics Reports”, and the most recent summary is the 77 page, “Deaths: Final Data for 2017“.
There is an enormous amount of confusion around Covid-19. The data is confusing. Media and personal interpretation make it more confusing. It is hard to understand what is really happening, and without knowing reality, it is hard to know how to react.
Because of the rate of spread of the virus and the potential to overload hospital systems, most of the world is in quarantine. Because of all the safety precautions, fewer people overall are dying. The confusion about diagnosing the disease, detecting the disease in those who have passed away, and inconsistency in how mortalities are labeled still makes it hard to understand what is happening.
All cause mortality tracks how many people have died due to any cause – and that shows that something is happening in the population. The spike in New York City deaths has surpassed 9/11. As the charts below from the Financial Times and other sources show, several regions show a surge in all-cause mortality.
650,000 Italians died in 2019. 2,839,205 Americans died in the same year. If the citizens of both countries are sheltering-in-place, avoiding risks and less likely to die from other cuases – then any rise in ‘all-cause’ fatalities should be due to Covid-19.
Thanks to the Chartstravaganza by @PlanMaestro and Germany’s Christian Drosten who directs the Institute of Virology at the Charité Hospital in Berlin for providing this background information.
Infectious disease is confusing. When Goldratt wrote The Goal, the hero Alex Rogo never wanted for good data. C19 has presented confusing, conflicting data around the goal. This calls into question measurement and observation methods. It makes decisions harder to make.
For all of the confusion that comes from the original data, we see an added layer of confusion in how this data is reported and interpreted. There are clear cases where the way in which the data is presented is driving towards a specific answer.
Case Rates
China’s case growth looks artificially flat. Presenting fatality or case rates which have not been normalized by population size seems like deliberate manipulation of data.
Flat China Data and no normalization for population size – is this legitimate data? This data looks more useful; it is normalized by population and there is no data that ‘looks weird.’
Sweden – Good or Bad?
Sweden followed a different plan on social distancing. The graph on the left is presented to make Sweden look bad – more fatalities. The graph on the right is presented to make Sweden look good by choosing a peer group with less fatalities. The graphs below are based on the same information and presented by the same organization.
