Determination: Be prepared to make tough decisions including those that constrain business activity. Data Driven: Transparency is the key to identifying risks and avoiding the bias that can come with gut feel.
Resourceful: be creative in how you use your time and resources to focus on analysis and decisions that have the most impact. Respected: Cultivate respect by being measured in what you say; using data to support your case. Listen and be open minded. Risk/Reward: Understand the Risk/Reward tradeoffs – be commercial in your decisions.
Insight/ Intuition: Follow your intuition to identify potential future risks. Challenge success. Be proactive. Independence: You have the independence and freedom to focus on the areas of risk you think could manifest a number of years down the road. Many catastrophic risks are years in the making.
Vigilant: Defend against the recurrence of risks that have occurred in the past or in other organizations. Complacency kills! . Visionary: look for the next risk that could threaten your business. Be creative – think about the worst case scenario and use this as an opportunity to strengthen controls to prevent that scenario from occurring.
Empowered & Empowering: You are empowered to chase down risk. Empower your team with the same mindset.
What behaviors do you think are critical for success in risk management today?
This post is inspired by a question from a subscriber. Thank you!
Ah – the peace and tranqulity of Vermont. Who wouldn’t want to escape the heat of the south, or the bustle of the city with a relaxing vacation in the countryside.
Well before you pack for that trip consider the states “CROSS STATE TRAVEL” advisory. If you come from a higher risk state or country forget it (or for my Brooklyn friends fuggedaboutit!
This is a sensible step for Vermont – it is already one of the lowest risk states in the US – with virtually zero average death rate in the past week or so. Lets look at how Vermont has defined it. It says if your home county “caseload” is greater than 400 per million of population – you can’t come in.
I don’t see a definition for caseload – but I’m going to assume this is the likely number of contagious people. If we assume a contagious period of 2 weeks – and look at the average daily number of new cases over the past 2 weeks for each state – we can begin to see who qualifies for that. Answer not many.
By my calculations – the states that would qualify to travel to Vermont are (in lowest order of caseload first)
But bear in mind – the calculations are done at the county level
Here’s a number you don’t see grab the headlines much…. the number of people in the US now recovered from COVID is likely in excess of 1 million!!!
The number of daily cases of COVID in the US peaked on April 24th – around 46 days ago (at the time of writing this blog). At that time 31,430 cases per day were being identified. Today that number is closer to 22,000 – also at a time when testing has vastly increased. (see my blog entry on 400,000 tests per day).
With the first cases diagnosed in February this year, how long do we have to wait for a case diagnosed today – to be an officially ‘cured’ case? Here’s some assumptions that I’ve made to help us better understand the active case load today.
Assume that a newly diagnosed person is contagious for 2 weeks.
Assume recovery takes around a month
Assume fatality (if applicable) takes place within 1 month
The problem with assumptions is that you can always poke holes in them… but as a starting basis these seemed reasonable to me.
Using those assumptions for the US we have a total of 1.9m cases since inception; 109,000 fatalities. That leaves us with….. drum roll please….. 1.1m people RECOVERED from COVID; 388,000 coping with COVID; and 281,000 actively CONTAGIOUS.
As a sense check – at an average of 21,000 new cases per day – and following the self quarantine requirement for 14 days – that would give around 290,000 people in self quarantine.
By the end of this week, the US will likely pass the milestone of 2 million COVID cases. You can bet that will be widely reported. But will the in excess of 1 million recovered be as widely reported? Lets see….
For the following states, the level of fatalities from COVID is too high – and is trending in the wrong direction. Not enough is being done.
Masachusetts has the highest current level of fatalities per million of population per day coming in at 9.8 New cases are also very high at 134 per million of population per day. The fatality rate is stable, week over week, but that’s little consolation when the numbers are so high.
Michigan is also deteriorating. At 5.8 fatalities per million per day – and increasing rapidly, at 54% over the prior week.
Pensylvania, 4.9 fatalities per million, increasing at 8% week over week
Louisiana, 4.4 fatalities per million, increasing at over 40% week over week.
New Hampshire, 4.4 fatalities per million – increasing at about 15% week over week
Indiana 3.6 fatalities per million – increasing at about 6% week over week
See the detailed charts below.
All of these states have a current level of COVID fatality higher than NY when compared equivalently using per million of population statistics. Think about that for a second. NY was the epicenter. Now it is very definitely the case that for current data, 13 other states have a worse fatality level than NY. NY continues to improve.
It seems inevitable that COVID will not simply disappear so we need to turn our focus to “what is the acceptable level of COVID risk” that we are willing to live with. The fact that many states continue to reopen (or at least not roll back on some of the opening measures) means that our leaders are implicitly defining an acceptable level of risk.
Lets try to define an acceptable level of risk. My suggestion – 1 fatality per million per day. Here’s the reasoning. 1) It seems like a very small number 2) this is broadly equivalent to the US level fo FLU fatalities in a 6 month season 3) many states are already well below that level.
Using this measure, and looking at the week over week changes in fatalities – we can do some automated analysis of the states in the US. Here’s what we get:
27 states (plus Washington DC) – are not where they need to be – by a long shot. Of these 27 – 15 are improving.
Massachusetts and Rhode Island are far in excess of any reasonable acceptable level for COVID fatalities and are worsening. By the flip side – many states including Kansas, Arkansas, Tennessee are well within an acceptable level of COVD fatalities – using the above measure.
There’s a risk in some of the current reporting that small spikes in countries with very low levels of COVID get represented as very large percentage increases. This methodolody corrects for that. A state with a fatality level of less than 1 person per million per day – will still be reported as acceptable – even if there is a short spike.
The worst states today – with an unacceptably high current COVID fatality rate AND trending in the wrong direction are:
Massachusetts, Rhode Island, Georgia, Colorado, Minnesota
Georgia was one of the first states to reopen quite widely. The data shows us that Georgia has not improved sufficiently, perhaps indicating that the lock down did not go for long enough, or that the Test, Trace, Isolate protocols are not sufficiently developed.
NY is improving rapidly – but current levels are still too high to be acceptable. Well done NY – keep it up! At that rate of progress, NY could be lower than Georgia within 2 or 3 weeks.
I’m very puzzled by Rhode Island. This is ranked #5 on the scale of COVID fatalities per million people. Recently it has advanced significantly up the league tables – and as the chart shows – the pace of fatalities continues to grow – even though new cases don’t seem to be growing as rapidly.
If anyone has any insights to share on this – please let me know.
Rhode Island is a small state with a population of just over 1 million – but that’s significant enough – and equivalent to many cities in the US.
This resource analyses the number of COVID tests performed per 1000 people – enabling a comparison across countries. It shows that the in each of the last 4 days of May the US performed over 400,000 tests. That’s amazing! The US testing is about 1.2 tests per thousand per day. South Korea (a country often associated with high rates of testing) is currently testing 0.2 people per thousand (although the incidence of new COVID cases in that country is very low).
Only Australia, with a population of 25 million people, is testing more people per capita than the US. I’m make the point that testing 25 million people has to be easier than testing over 300 million people. So I think the US deserves a pat on the back for reaching this significant scale quickly.
Now, in the interests of full disclosure – some countries are testing more individuals PER confirmed case than the US. If you like – some countries are doing a wider canvassing of people each time they find a positive case. The US does do that in some populations i.e. testing all of the people in a prison, if there is a confirmed case – or in Meat Processing facilities (see my other blog post on that). So there is some further room for improvement – but that shouldn’t stop us from celebrating what has been achieved.
400,000 tests per day is a great milestone.
New US cases per day re not falling as rapidly as some other countries – but putting this in the context of more testing – simply means the US is catching more cases that would otherwise be undiagnosed. Even with that caveat – the US is showing sustained progress even as States emerge from lockdown.
NY State has a population of 19 million people, Florida has a population of 21 million. According to PRB.ORG 20.5% of Floridian’s are elderly – versus 16.5% in NY. Florida is ranked #2 by this percentage – whereas NY is number 26.
We might expect Florida, with a higher elderly population to have a worst COVID outcome than NY – but that’s not even close to being the case. Florida as 2,258 deaths from COVID and NY has over 29,000 – that’s more than 10x worse.
Here’s the public transport connection. Consider this, the NY public transport system has the most passenger trips per day of any system in the US ( and probably very high compared to many cities globally). It has over 9 million passenger trips per day. Florida has a public transport system in Miami (the Metrorail) – with average weekday ridership of 62,000.
Looking at the public transport systems in the US – here’s what we see:
There’s a clear correlation between average daily ridership and COVID deaths per million. Its not a perfect correlation because other factors have a big influence – like hospital capacity etc.
I’m convinced public transport was a big reason for the spread of COVID in NY.
How does the NY transit system stack up in worldwide comparions? According to the source below – in 2015 NYC was the worlds largest system – as measured by number of station. Bigger even than those in China, or Singapore.
In terms of passengers per year – the Asian countries carry more passengers i.e. Beijing carries 3.4bn per year (2014 data) and Tokyo at 3.2bn – but NYC still carries a colossal 1.8bn passengers per year. Add to this the observation that use of face masks seems a lot higher in Asian countries (even pre COVID) than in the American or European cities and we have one of the biggest reasons, in my view, for the rapid spread of COVID in NY.
Shut Down Public Transport much earlier in the crisis. In NYC there was not an official shut down.
Facemask must be mandatory for all passengers. The NY Governor issued a mandatory face mask order on April 15th. Not bad – especially given conflicting and delayed advice from the CDC on the use of face masks. On April 15th there were a cumulative 217,000 confirmed cases of COVID and almost 15,000 deaths. Interesting point – April 14th marked the peak in average daily deaths in NY at 1055 deaths per day.
By May 6th – NY had already had 329,000 confirmed COVID cases; and 25,000 deaths.
I can’t help but think the subway linkage was obvious from the get go. Our slow response – and delayed acknowledgement of the importance of face masks (compared to Asian cities) was a major contributor to rapid spread of COVID in NY.
The main stream media is geared to reporting on specific outbreaks of COVID as they appear but don’t seem to be doing a lot of reporting several weeks later. The message is typically combined with other broadcasts that reinforce the dangers of COVID. I think its important to go back and check to see what’s happened in these areas of small outbreaks, and that’s what I’ll do here.
Consider the case of meat processing plants in South Dakota. Here’s the headline from the NY Times on April 15th – it’s attention grabbing.
6 weeks later – what’s happening? Check out the chart below. It clearly shows a large peak of cases – leading up to 119 confirmed cases per day around the date of this story. It also shows another peak around 1 month later.
Let’s ask the question – are the implied fears on the reporting of Micro Clusters founded? Answer – largely not. Fatalities in Minnehaha county rarely exceed an average of about 2 per day – at the peak – and currently are at a level of about 0.4 per day. In total 43 people out of a county population of 190,000 have died from COVID. That’s about 233 per million – and about 30% higher than the typical US rate of seasonal flu.
I’ve noticed that when the media reports these micro clusters – they don’t usually say “and 1% of these cases may result in fatalities“. I could be wrong – but in my view – they reporting seems to leave to the imagination a much worse outcome than 1 or 2%.
South Dakota is doing well with regard to COVID. It is ranked #40 in terms of fatalities per million of population – and overall COVID deaths are about 34% of the typical seasonal flu level. The peak in average daily deaths occurred 20 days ago – and the current daily level of fatalities is about 26% of the peak. You can see all of this data in the document below.
Short answer – South America, UK and Sweden. US not quite out of the woods yet but the wide divergence in case load by state means that #onesizedoesnotfitall.
What’s the best way to represent the COVID risk we face today. While there is regular reporting of total COVID cases, and total COVID fatalities, at this point this data is backward looking.
We need to look at how many cases are being created today, and the best way to do that in a way that supports an even comparison across countries of different sizes is to do this in ‘per million’ of residents. The results show that South America is rapidly accelerating in its COVID burden.
New cases today are likely to result in future fatalities. How many depends on the capacity of the health care systems of the respective countries to take care of the sick. Once the health care system becomes overwhelmed the fatality rate increases.
The much higher rate of case creation particularly in Peru, Brazil, Chile will shift the global league tables several weeks from now.
The next table shows the current rate of fatalities per million – showing the top 20 countries in my dataset
Sweden and the UK both lead the league table for the number of daily fatalities per million of population. These countries were also the early proponents of the herd immunity theory. Several months later the data shows that the fatality rates in these countries have failed to come down as much as those in Spain, France and Germany etc.
Re the US – although the US is not low on this league table – the US did not experience the peak wave of fatalities that most European countries faced (to be covered in another post).