The daily case load in the US has followed a classic series of waves. Communities with rapidly increasing cases respond with social distancing, wearing masks, restrictions on group gatherings, and lockdown of commercial activities. Once the cases decline, reopening happens followed inevitably by rising cases. Waves in national cases  also occur with large communities such as states seeing rising cases at different times.

By the Fall of 2021, the US case count shows three distinct waves, led by 3 different regions of the US. The first wave was led by the North East (NY/MA/NJ), the second by the South (TX/FL/AZ), the third by the Central North (ND, SD, IA). Over time the hospitalizations and deaths are lower relative to the cases, presumably a result of improved health care - discussed in more detail in "Lethality".  

 

 

 

 

 

 

 

 

 

Wave 1 

Covid started in Wuhan China in the fall of 2019, and by spring 2020, the western states that had most of the commercial contacts with China were watching for early infections. Meanwhile, Covid had got established in Europe and arrived unnoticed in New York. 

By mid April, the Covid  infection had rapidly spread
across the US  (40 states in only 13 days) and  wide  
variation in severity  (50x variation in total deaths, with  
the North East was the center of the pandemic.  A sort of
the total deaths, showed that there was a pattern of
neighboring states with declining levels of infection over
time, which consistent with community spread through hot
spots.


The only way the virus can get from New York to
Michigan is by flying as there is not a neighbor path.
Infections spread over time so I used the date of first
infection and then neighbors with declining infection
levels and later dates  to  create a map of the spread.


In the early stages, the infection appears to have started
in New York, and propagated through the industrial North
East.   I then colored the states based on the date when
5 deaths were first reported – each day has a different
shade, but roughly speaking Red is early to mid March,
Purple mid to late March, Blue is April.  


There is a pattern of spreading in infection. The first
infections occurred in a few early (red) hot spots, and
then spread to neighboring states with progressively later
(bluer) and smaller infections. WA was first to detect but
everyone was wary of the risks from China and so the
infection was minimized. NY was obviously worst hit, and
the progression of serious infection to neighbors can be
seen through the north east to NJ, CT, MA, MI, RI.
Within 4 days there were also  hot spots detected in LA,
MI, CO, GA but with smaller infections. These presumably
come from visitors flying in from NY with Mardi Gras
causing the biggest problem. Using the observation of
spreading in the north east as the model, each of these
new hot spots spread to their neighbors again with
smaller and later infections, the spread in the north
central region from MI to Il, IA, IN is the most sever. Texas
had a much later and smaller infection, so cancelling
SXSW was crucial. I show some possible spreading paths
created using the date of first infection for the
neighboring states and connecting them together
illustrated with the green arrows.


It looks like we can make sense of the path of the
infection, but why are there such large variations in
infection level. The next stage was to see if multiple
states could be modelled. I chose the state with the
highest infection level NY, the state that started earliest
but had one of the mid-levels of infection WA, and one of
the latest states that had one of the lowest levels of
infection TX.

The graph shows the 7 day average of daily deaths
along with model fit. In the model, the patient zero date
and the isolation date were adjusted for best fit. In NY the
death rate was nearly 100x higher than WA before they
isolated, as a result the infection took off. TX isolated
very early just like WA and had very low levels. Under
isolation, NY is declining fast, TX on the other hand has
started to reopen and the infections are rising again.  

Wave 2

Everyone has been worrying about the possibility of a second
wave that comes with re-opening. In the first week in June, it is
here, with Arizona leading the way. AZ will probably hit the
maximum case count in NY within 2 weeks. In NY, they
experienced sever stress to their health care system. In AZ
there are already early reports of problems.

The map shows the average case count  over the last 2 weeks
at the end of May - early June  with the 28 states with rising
infections in red. There are 8 more states with rising infections
then just 1 week ago. The only states that are falling are the
ones that had sever infections in first wave.









The reality of the second wave can be seen in the case count
by the mid August. The cases rose after the Memorial Day
opening, triggering action and enough fear in the population to
change behavior. In July a number of counties FL, AZ, and TX
maxed out hospital beds but case  growth stalled, and by early
August it is falling in most states.

Wave 3 

The third wave was led by the North Central States (ND/SD/IA). They had avoided the first 2 waves, and as rural states have had a lower growth rate of 1.5 per person. It took 5 months, but now they have got to over 1000 cases/M, and have hospitals in critical. Most of the other states have also reopened after the first 2 waves and are also growing. Because all states are growing, the US numbers are at an all time high of 400 cases/M. At Thanksgiving we are 1/2 way to the WHOLE COUNTRY being at critical. 

Over time, at the national levels the waves get higher, but grow more slowly. 

There are also major spreading events such as Sturgis Motorcycle rally, Thanksgiving, and Christmas. 

If the event exposes at risk population, such as Thanksgiving, then the hospitalizations should spike starting in 5 days. Otherwise, cases should climb in around 8 days. Then people go home, breaking  the local infection cycle. In the home location, cases and hospitalizations should climb again in another infection and incubation cycle 13 days. 

 

The spike due to the 10 day Sturgis bike rally in August can be seen  in the numbers, 15 days after the rally, but it dissipated as they all went home and contributed to the background trend 10 days later. This is consistent, with 8 days for a cycle of infection and a 7 day average for noise reduction.

 

The impact of vaccination brought Wave 3 to a close.

Wave 4 

Michigan showed the first signs of the next peak, where loosing of mask use overwhelmed the impact of vaccine. 

International Trends in early Jan.

All the western democracies are struggling, with the Ireland clearly the worse. Ireland, UK, and US appear to be levelling off in early Jan.  Japan, Korea, India, and Africa  are all doing noticeably better, presumably due to more social cohesion, local ground roots health care (Africa, India) and a tradition of wearing masks (Asia).

 

India is particularly given its poverty and  population density.

https://www.wsj.com/articles/covid-19-was-consuming-india-until-nearly-everyone-started-wearing-masks-11609329603

Geographic isolation in Australia and New Zealand really helps to make distancing work. 

International Trends in June

In June the is seeing a doubling every 11 days, inspite of the fact that they  have higher vaccination and better distancing than the UK.   The cases in the UK are 80% the Delta variant compared to 6% for the US. The variant will determine the the final wave of Covid. It is estimated to be 50% more infectious, or a R0 with no distancing of around 8. This variant will go through the remianing unvaccinated community very quickly. 

Wave 4 

The Infections per person (Ipp) are driven by social distancing and the infectiousness of the dominant variant.  Goggle created a mobility index based on individuals volunteering to make  their cell phone location data available, which can be used as a proxy for social distancing.  The retail, grocery, transit and workplace mobility metrics showed the greatest variation  during the pandemic. Assuming that the mobility at the peak in the early pandemic was closet to zero, then at a mobility of -20% of normal the Ipp becomes<1 and the infection stops growing. The Delta variant was responsible for the peak in the fall of 2021. 

There is a roughly 1 month lag between a change in mobility and a change in cases. 

https://covid.cdc.gov/covid-data-tracker/#variant-proportions

https://www.google.com/covid19/mobility/

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