The spread of Covid occurs  in a series of waves caused by changes in behavior. The direct impact of the infection can be seen in hospitalizations and deaths. The rates of hospitalizations and deaths has changed, probably from changes in the demographic of the infected communities, and improvements in health care.  

The Case Fatality Rate for communities with controlled infections has improved from 3% to as low as 1%. When the hospital systems are stressed, the CFR has risen as high as 8%. The infection is most dangerous to the elderly and those with serious medical problems. It also seems to severely impact a few unlucky individuals. 

The time line of infections in the US shows 3 waves in daily cases. The hospital admissions reflect the daily new admissions and the time each person spends in hospital. The hospital admissions relative to daily cases has dropped significantly from wave to wave. This means that now the health care system can deal with a higher level of cases than in March. At the end of Jan, hospitalizations are dropping, vaccinations are above 1M a day and rising. There is a suggestion that the CFR may be starting to drop as the at risk of dying group (>65) is now over 40% vaccinated. It will take around 1 month for the vaccinations to show up affecting death rate. So by end of February the impact should be clear with a halving of the the death rate. (CFR).  

The map of the US showing hospital admissions per million, shows  how many states are in trouble, particularly in Central US. 

In the US here are a total of 1M beds for 330M people or 3000 per million.  At 370 beds /M occupied in Texas, that is about 10% of capacity. The operating assumption is that 60% of beds are occupied by non-Covid patients, leaving 1,200 beds /M available for Covid. In Texas, at 375 on Dec 1, we have have  space for cases to go up 3x. In CA they can just tolerate a doubling in cases.  These are averages for the state, some local hot spots such as LA are already overwhelmed. States maxed out at around 800 beads/M, with some locations  locations overwhelmed. 








In Austin, hospital admissions have closely  tracked daily case
count. At the peak in infection in July, the admissions got to within 2 x of local hospital capacity. In early December it looks as though hospital capacity will reach the limit again in early January. The move to stage 5 and people getting frightened has caused the hospitalization to start dropping by end of Jan. 











If you look closely, any difference depends on the
demographic of the infected population. I looked closely at
the ratio of Hospital Admissions to Daily cases and
compared it to the fraction of the cases over 50 years of
age. The tracking is notable, and statistically significant
99.9% confidence. By extrapolation of the slope, the ratio of
admissions to cases can vary from 0.5 to 5x. The hospital
impact can vary 10x depending on the >50 demographic of
the infected population.

For obvious reasons, the mortality of Covid is a critical
property that affects the entire response to the virus.  In the
early stages of an infection, a popular  measure is the ratio
of Deaths to Cases called the  Case Fatality Rate (CFR)
(see  CFR values
have ranged from  0.5% in  Iceland to  14% in  Italy with a
world  average of 7%.  In the US, the variation is from 1% in
Nebraska,  to 10%  in Michigan, with a US average of  4%.  
In general, these variations have been blamed on non-
uniformities in  detection, classification and reporting.

In terms of Case Fatality Rate (CFR), the CFR has been
transformed over the 6 months since this started.



The CFR time line shows that the lad between deaths and cases has increased significantly (see Covid Characteristics for more detail).   The CFR for the US  with changing lag,  shows am improvement from 8% to 2%.  

The CFR time line for TX shows a smaller change from 3% to 2%, whereas NY showed a change from 8% to 1%. 

 In the earliest stages of the infection, NY had a CFR of 8% compared to TX at 3%. By the end of April it was clear that health care stress has a big impact on fatality. To see if these variations might be real,  I made a sorted table of CFR for the states based on the data from (https://www.worldometers.
info/coronavirus/country/us),  and all the states that were hit
hardest (NY, NJ, MI, LA) have the highest CFR. This clearly
suggests that the loading on the health care system is a
factor. Stress on the hospitals, particularly the ICU should
be related to the daily death count, and the greatest
demand will occur at the peak of the infection.  I plotted the
Maximum daily Deaths per Million (MDM) as a proxy for
health care stress against CFR and found a statistically
significant (>99%, R = 0.64) trend. The correlation is better
with deaths than cases, suggesting that it is ICU pressure
that is the problem. There was less or no correlation with
other metrics of health care availability such as number of
doctors, hospital bed count, the head room between deaths
and beds, whether the state was infected early or late
benefiting from health care learning etc. The caution is that
correlation does not mean causality, so this might be just a
useful indicator of a problem.

We also know that the CFR critically depends on the age of
the infected population. In March, 30% over 80 years old
who were infected were dying.




















The improvements in CFR have occurred across all age groups. Data from FL shows the CFR for the over 80's is now down to 17%. The improvement appears to be related to improvements in medical processes, such as avoiding ventilators and new drug regimes to control the virus and impacts of infection such as inflammation using  steroids. 








By April, the variants have started to dominate. It took 2 months fo for the variant  (B.1.1.7) to take over the UK in late fall, and based on recent publications it appears to be roughly 60%  more infectious and 60%  more lethal.

This translates into an Infections per person (Ipp or R(0)) under normal distancing of 9 and an average  Case Fatality Rate of 2.7%.  

The same variant is dominating in Michigan in April. If you run the models for this, it looks like with relaxation of distancing,  the remaining un-vaccinated population will get infected quite quickly - probably by late August.  There will also be a period of serious pressure on the health care system in May and June.  


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