4 ms·

This site openly admits the obvious flaws in their model: - R0s for interventions are guesses, in some cases informed by data. There is no historical precedent

This site openly admits the obvious flaws in their model: - R0s for interventions are guesses, in some cases informed by data. There is no historical precedent for what is going on right now to draw from. - The default R0 used in this model is an average. The model does not adjust for the population density, culturally-determined interaction frequency and closeness, humidity, temperature, etc in calculating R0. - This is not a node-based analysis, and thus assumes everyone spreads the disease at the same rate. In practice, there are some folks who are “super-spreaders,” and others who are almost isolated. Interventions should be targeted primarily at those most likely to spread the disease. Are there any epidemiologists using less naive statistical models who are producing easily readable results like this? The "average R0" logistic function model seems better suited for examining historical epidemics than estimating risk to inform decision-making for ongoing epidemics.

aivosha 7y ago
this
termain 7y ago
That's generally what you should do with a scientific model: state your assumptions and methodology clearly. And most if not all scientific and engineering models spread properties across a group of objects and use averages. R_0 is an average. And it's the average behavior that matters here In a well tested population, you might be able to focus interventions on infected people and their contacts. In the US population, the people most likely to spread the disease are those who interact most with other people. That means getting those people to reduce their interaction rate. It's a semi-empirical model, as are many if not most models used to make decisions.
AndrewStephens 7y ago
I agree. The graphs are very pretty but would look very different if the R0 was even slightly modified. The mortality rates probably varies widely by state demographics as well. That said, the implication that strict isolation should be maintained is almost certainly correct.
- maxwhenderson 7y ago
  Hey guys - owner of the model in question here. Thanks for your feedback, and yes, the model isn't perfect. Idea is just to give some forward-looking picture for policy-makers to use to make decision. Interestingly, the model is not nearly as sensitive to R-values as you would think, until they get under 1.5 (half the current observed number in most places). See here: https://www.researchgate.net/figure/Doubling-time-Average-doubling-time-dots-and-95-CI-vertical-lines-as-a-function-of_fig5_258956572 https://www.researchgate.net/figure/Doubling-time-Average-do...
  - ahnick 7y ago
    Really appreciate you putting this together.

aivosha 7y ago

this

termain 7y ago

That's generally what you should do with a scientific model: state your assumptions and methodology clearly. And most if not all scientific and engineering models spread properties across a group of objects and use averages. R_0 is an average. And it's the average behavior that matters here In a well tested population, you might be able to focus interventions on infected people and their contacts. In the US population, the people most likely to spread the disease are those who interact most with other people. That means getting those people to reduce their interaction rate. It's a semi-empirical model, as are many if not most models used to make decisions.

AndrewStephens 7y ago

I agree. The graphs are very pretty but would look very different if the R0 was even slightly modified. The mortality rates probably varies widely by state demographics as well. That said, the implication that strict isolation should be maintained is almost certainly correct.

maxwhenderson 7y ago
Hey guys - owner of the model in question here. Thanks for your feedback, and yes, the model isn't perfect. Idea is just to give some forward-looking picture for policy-makers to use to make decision. Interestingly, the model is not nearly as sensitive to R-values as you would think, until they get under 1.5 (half the current observed number in most places). See here: https://www.researchgate.net/figure/Doubling-time-Average-doubling-time-dots-and-95-CI-vertical-lines-as-a-function-of_fig5_258956572 https://www.researchgate.net/figure/Doubling-time-Average-do...
- ahnick 7y ago
  Really appreciate you putting this together.