One the earliest assessment's of Covid was in comparison the flu.  How does Covid compare to other diseases that we are familiar with ? 


There are a number of points of comparison for diseases; infectiousness measured as the infections per person, asymptomatic fraction, hospitalization rate, and fatality rate (CFR))


Of these, one the most difficult to measure is  what fraction of infected population are asymptomatic ?  Many of the early studies  consisted of a snapshot in time of how many showed antibodies, versus symptoms and reported asymptomatic : symptomatic. Recent studies followed people, and most asymptomatic eventually showed symptoms. It now appears that over time, about 80% of the asymptomatic get sick, effectively everyone.

What it is clear is that at any point in time there are a large number of people who you meet  are infectious and do not know it.  

The latest data suggests that people are infectious 2 days before and 5 days after showing symptoms.

In the fall, in the US the hospital admissions are about 1/2 of daily cases. Median hospital stays were 7 days. So daily admissions are 1/14 = 7% of symptomatic cases. 

As noted in the lethality page, the CFR is around 2%. 

In summary, illustrated in the time line;

Under "normal behavior", someone infected will infect 5 others in between 3 and 10 days, average around 7 days.


Average incubation time from exposure to symptoms is 5 days , 95% within 12 days 

Of those infected, on the average,  80% will show symptoms.

Of the symptomatic 

        7% will end up in hospital for 7 days.

         2% will die after around 20 days. 


For the over 70's, around 10% will die. 

After a super spreading event like Thanksgiving when everyone returns home after the event, look for hospitalizations of the elderly  to go up after 5 days, and cases to go up after 13 days. 




Is Covid just like the flu..NOT !

Covid is often compared to the flu to provide different
perspectives, what is the reality? The first graph shows 6
years of weekly flu deaths from the CDC, showing a
remarkable consistency from year to year. An average of
8000 deaths a year.  A model with a constant Ipp = 1.4
infections per person follows the repeating shape of the
infection, consistent with the literature values of  Ipp = 1-2.

The maximum in the model is created by the natural
progression of infection, which probably explains why the
pattern is repeated every year. The maximum is formed
because as more people get infected, the number of
susceptibles drops and the overall infection rate starts to
fall. This suggests that the reason that the flu stops in
summer is not heat but the fact that the flu virus runs out of

The next graph compares the 2019 flu cycle and the  Covid
infection in the US. The model of Covid used an Ipp = 5.6 (
Ref 4 )  infections per person and also  followed the  natural
disease progression (standard SIR). Initially,  the Covid
infection grew much more  rapidly  than flu, fortunately,
isolation stalled the Covid infection. The model shows what
might have happened without isolation, the death rate could
have risen to a disastrous level of more than 10,000 a day.
Even with isolation,  Covid still had a death rate 10 x worse
than the flu.

The very rapid early growth of the Covid infection is the key
evidence that  Covid is nothing like the flu, and isolation was
completely essential.

Covid is nothing like the flu !  The flu is  3x less infectious,
4x less lethal, and  reaches herd immunity in 6 months.

Ranking Covid

It is now possible to compare Covid to some well known
diseases. Covid is probably  much more infectious and  
lethal than the flu. It is as infectious as  Mumps, and more
lethal than Measles.

Ref 1

Ref 2

Ref 3

Ref 4 The R0 value is based on Covid data for the Us. It is very close
to the  median of 5.7 according to a study published online  Sanche S,
Lin YT, Xu C, Romero-Severson E, Hengartner N, Ke R. High
contagiousness and rapid spread of severe acute respiratory
syndrome coronavirus 2. Emerg Infect Dis. 2020 Jul [date cited].

The New Covid variant 1/4/20 

There is a new Covid "Variant  Of Concern" (VOC) running through the UK in the fall. They calculated the R or Ipp for each week and each NHS region, and compared Ipp  to the fraction of VOC. They concluded that "we estimate similar growth differences between the VOC and non-VOC lineages of +49% to 53% per generation".  The best summary graph is shown to the right.

The map shows how the VOC variant spread across the UK in only 4 weeks. likely 3 cycles of transmission 2x each cycle, or a 8x increase. This is about right with the total UK daily cases rate going up 4 x in 4 weeks. 3 cycles of infection covered 300 miles in a month ,in a densely populated country. This tells me that it is not friends transmission, but more likely work related. 

The graph shows a rigorous analysis; "Empirical data analysis of the advantage in weekly growth factors (cases in week t+1 divided by cases in week t) for the VOC versus non-VOC lineages. Each point represents  the ratio   of weekly growth factors for the VOC versus non-variant for an NHS England STP area and week, using the raw data.  Colors and shapes differentiate epi weeks. Numbers above 1 on t  show a transmission advantage. The blue line represents the mean advantage for a particular proportion of VOC among all cases, and the grey lines the 95% envelope. Scatter at low frequencies largely reflects statistical noise due to low counts."

The value of Ipp obviously depends on the degree of social distancing with respect to the primary infection path. The Ipp during the period was around 1.3. We have no way of knowing how much more infectious this variant would be in a normal pre-Covid world. If the primary  infectious path of the variant is the same as the original Covid, then the Variant has a Ipp(0) or R(0) of around 8, compared to 5 for the original.  This puts the VOC in the same class as Rubella. In the pre-Covid world, this difference would have made things much more challenging, and may well make the vaccination level needed for herd immunity as high as 90%.











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