The Epidemic Lab Simulates Epidemics
The model keeps track of people as they pass through six interacting stages: Susceptible; Exposed; Infected; Bedridden; Deceased; Immune.
The Epidemic Lab Model Structure
The Epidemic Lab observes the entire system of interactions as the epidemic evolves.
You can tune the model properties to any epidemic you wish to study.
You can also test social distancing, vaccines and treatments.
In complex dynamic systems, such as underlay an epidemic,
we do not easily keep track of all the stages and their ongoing interactions.
For example, we discuss new cases
without clearly specifying
the stage to which the cases pertain - or the duration of aggregation of the cases.
Same for mortality rate
To one person it might mean total deaths from inception, divided by total population.
To another it might mean deaths during the last week divided by the number of people in hospital beds.
We have dozens of metrics for epidemics, many of them sharing the same name and bearing no formal definition.
The press reports them and our leaders try to respond to them without really understanding what they mean.
The Epidemic Lab has precise definitions for all stages and the flows between them.
It shows, for example, that the sequential stages have sequential peaks.
Indeed, a peak in death rate indicates culmination of the last stage of the epidemic.
As such, it might not indicate a need to distance people, since few Susceptible still remain to contract the virus.
Some say we have a shortage of masks or respirators or hospitals.
We also appear to have a shortage of understanding about the dynamics of complex systems.
Fortunately we can remedy the latter with a few hours in the Epidemic lab.
Typical Epidemic Cycle
Experimenting with a Treatment Program
Experimenting with a Treatment Program (detail)
This simulation above shows the implementation of a treatment program at six months, lasting three months.
Treatment helps "cure" Infected (it transfers them to Immune). This has two beneficial effects.
(1) These Infected do not proceed to Bedridden and Deceased; this lowers the mortality rate.
(2) Fewer Infected reduces the conversion of Susceptible to Exposed; this stops the spread of the epidemic.
At the end of the treatment program, Infected starts growing again and the epidemic resumes.
Some experiments you can run with the Epidemic Lab
1. Social Distancing
Voluntary-distancing strategies (low-effectiveness distancing)
allow the epidemic cycle to proceed at its normal organic rate.
Mandatory-distancing strategies (high-effectiveness distancing) postpone and "flatten out" the cycle.
You can observe the effects of distancing as you change the effectiveness and also the start and end dates.
You may also illustrate the distinction between postponement and prevention.
2. Mid-Cycle Distancing
You can start distancing earlier or later in the cycle and observe the results.
In general, mid-cycle distancing has little effect;
mid-cycle, we have fewer Susceptible remaining to expose.
. Treatments that move Infected to Immune reduce the source of infection and stop the epidemic.
Current treatment candidates, pending trials, include Remdesivir and Hydroxychloroquine-azithromycin.
. Vaccines typically appear at the tail end of a cycle, just in time to take credit for ending it.
Viruses typically mutate, rendering vaccines intermittently effective.
Some observers suggest employing mandatory (lock-down-level) distancing to postpone the epidemic
until a vaccine appears. You can also test this combination strategy with the Epidemic Lab.
4. Lock Downs
The Epidemic Lab models lock downs as very-high-effectiveness distancing.
As such, the model does not have a separate control for implementing a lock-down strategy.
The Epidemics Lab does provide an unemployment impact control
for users who wish to display a proxy for the economic impact of lock-down-level distancing.
This control has no feedback-system effect on other system elements.
It appears as a convenience for people who wish to create graphics to illustrate their theories about economic impact.