Our understanding of the dynamics of Covid is mostly based on models of the infection process. The basic SIR model is the base for most epidemic model. It splits the population into 4 buckets; Susceptible (S), Asymptomatic Infected (Ia), Symptomatic Infected (Is) and Recovered (R). I have modified the model to allow the infection rate to change over time and an exponential response to changes in behavior, and changes in both the death rate and the delay between infection and death over time to account for improvements in medicine.

The dynamics of Covid infections can be seen in the log graph of daily new cases for different countries, early in the pandemic. In the first days, there was a very rapid rise of 10x in only 10 days. Almost immediately countries instituted lockdowns which stalled the infections and then saw them fall at different rates. The straight line rise and fall in the log graph is characteristic of the exponential growth of an infection.

The idea was to develop a version of the standard SIR

model of infections that accounts for changes in social isolation behavior and health care.

In the classic SIR model, the change in number of infections over time dI/dt depends on the rate of new cases minus the rate of recovery. The new cases per day is given by the infection rate constant beta, multiplied by the number of Infected people I(t) and the fraction of people available to be infected S (Susceptibles). The rate of recovery dR/dt is given by the recovery time constant gamma multiplied by the concentration of Infections I(t).

The ratio of infection to recovery rate constant (beta/gamma) has useful meaning as the initial Infections per person or Ipp(0), often called R0. It is the gain in the chain reaction of infections. For Covid, the Ipp of 5 means that every infected person infects 5 more for the roughly 10 days they are infectious. In thirty days there will be 5^3 =125 x increase in infections !! That is why Covid is so dangerous.

In reality, peoples social behavior changes to avoid infection so the rate of infection changes over time beta(t) and so does Infections per person Ipp(t).

It has become clear that there is a large number of "hidden" infected individuals who are asymptomatic and are not caught by current US testing that is mostly limited to asymptomatic. At current testing levels, the number of asymptomatic people caught by random chance is very low. CDC estimate is that 7x more individuals are asymptomatic infected than symptomatic. The hidden asymptomatic populations do not affect the dynamics of infection at all in the early stages, and then only if the asymptomatic infection provides immunity.

For the unlucky few who die, there is a time lag L between infection and death, and there is evidence (shown below) that this time has lengthened as the doctors have learnt how to manage the disease. Therefore, the number of deaths per day is given by the death rate times the number of infected people at the time of infection I(t-L). Similarly, the Case Fatality Rate can be followed over time as deaths per day / cases per day lagged by time to death L.

These differential equations were numerically integrated in an Excel spreadsheet, to model an infection.

The model can be used to extract metrics of the

process. At the beginning of the infection, the susceptibles

can be assumed close to 1, so the infection grows exponentially and can visualized as a straight line in a log graph, with a slope of Ipp.

The daily infections d log(I)/dt, new cases d log(C)/dt, deaths d log(D)/dt, all have the form = f(I), so they will track each other. Hence the universal use of log plots to analysis pandemics.

Now we have a way to quantify the stages of the infection illustrated in the graph of daily new cases for different countries. In very the early stages, the daily cases have a steep log slope consistent with a initial Ipp of over 5. In response, various levels of lockdown were instituted, and the infection stalled and then started to drop with countries showing different declining log slopes with a Ipp of 0.7-0.9. The effect of the lockdown was to change behavior, which in turn changed in the infections per person. It appears that the evolution of Ipp over time can be generalized as a series of constant levels.

Obviously human behavior does not change as step function. Manually adjusting Ipp to fit gave a curve that looked like an exponential. Using a exponential function, gave a reasonable fit to US, NY, TX and WA data with a 1/e time constant of 7 days, and around 20 days for the change in infection per person to stabilize. This leads to a generalized expression for Ipp(t) with a series of constant levels linked by exponential approach, that was applied to TX data.

The idea that the transition between 2 different levels of social

behavior is an exponential seems inherently reasonable, and has been used to describe transitions inisolation for other infections (Ref 1). A possible physical interpretation has been proposed, borrowed from finance (Ref 2), which suggested that the transition is an "Ornstein-Uhlenbeck" process.

In mathematics, the Ornstein–Uhlenbeck process is a

stochastic process. Its original application in physics was as

a model for the velocity of a massive Brownian particle

under the influence of friction (Ref 3). More generally it

describes the long term transition between two states by

many short term random changes, and has been used in

many application including the change in bond prices (Ref

4). The maths of this have a solution of exponential change

that is identical to the empirical equation I used. The idea

that a change in isolation, producing a change in infection

rate per person, would be accomplished by multiple small

random changes in behavior seems plausible.

Finally, published data on mobility based on cell phone data also shows an exponential response to the lockdowns in March.

Vaccinations started in late December. I reduced S by the fraction of the population that have received at least 1 injection, lagged by 40 days to account for jab to work. It looks like the average distancing behavior has remained unchanged since early November.

Finally in April, distancing starts to loosen, around 30% appear to be committed to not vaccinating, and the new variants take hold. The new variants are 60% more infectious and 60% more lethal.

Modelling the time line for fatality rate.

The number of deaths per day are obviously to the number of cases per day. The log plot of daily deaths and cases shows the log distance between deaths and cases varying, which means that the ratio of deaths to cases (CFR) is changing. Also the time lag between the peaks in March are smaller then than the lag in August, which means that the time lag from infection to death is also changing.

TX had a relatively mild infection in March, so it avoided any effects of patient load on outcomes. A series of CFR's at different lags for TX are shown. Assuming that CFR is likely to be relatively constant, it appears that prior to June, the lag was 7 days with a CFR of 3%, and post June the lag was 20 days with a CFR of 2%. The changing CFR and lag presumably a result of improvements in medical care.

Based on these observations the death rate was modelled as a series of constant steps.

In January, vaccinations have started to take hold with about 8% of the population. This is focused on the >65 group who constitute 15% of the population and are where almost all of the deaths occur. About 40% of the at risk community have been vaccinated. There is a 20 day lag from case to death and 10 days to generate immunity. In 1 months time the death rate should be halved. The graph for TX shows the projected death account after adjusting for vaccination.

Conclusions

The modelling analysis led to a number of key characteristics of Covid infection.

The Infections per person (Ipp) vary as behavior changes.

Under "normal life" behavior, Ipp is around 5.

Under lockdown conditions Ipp follows an exponential approach to a new constant value that takes about 20 days.

The death rate and the time from infection to death both change, probably due to improvements in medical care.

References

Thanks to Andrew Zachary for pointing out the link to financial

modelling.

Ref 1

Ref 2

pandemic-negative-interest-peter-cotton-phd/?

trackingId=K6StB6cgwAAA8dSumxqOfQ%3D%3D

Ref 3

93Uhlenbeck_process

Ref 4 "Modelling to Inform Infectious Disease Control" by Neils

Becker (2013)

Ref 5 "Repeat contact and the spread of disease" by Peter Cotton

(2020)