Group Abstract Group Abstract

Message Boards Message Boards

A SEIRD Model For COVID-19 Using DDEs

MODERATOR NOTE: coronavirus resources & updates: https://wolfr.am/coronavirus


POSTED BY: Luis Borgonovo
10 Replies

enter image description here -- you have earned Featured Contributor Badge enter image description here

Your exceptional post has been selected for our editorial column Staff Picks http://wolfr.am/StaffPicks and Your Profile is now distinguished by a Featured Contributor Badge and is displayed on the Featured Contributor Board. Thank you!

POSTED BY: EDITORIAL BOARD

Thank you Enrique! You make very interesting points, I started to read your post but it will take me a while to process it because it has so many updates and material spread in the comments, and the long discussion section in there seems very relevant too. The first thing I noticed is that in your model you make different simplifications to the “full” delayed set than I did, but they both give reasonably good fits. I hope you can add soon the code to learn how you dealt with the fit problem. I had my own code spread over a dozen files, so if you work like me, I understand its complicated to gather the essential parts in one single presentation.

I agree with you that what we have developed is a “model of the data” and conclusions about the epidemic characteristics are not straight forward and must be done with care. For example, the derived periods include the time it takes to report, which in some countries can be considerable. Some case detections are done with (cheaper) tests that measure antibodies after you are almost recovered. Others with the PCR method which detects genetic parts of the virus and therefore works even in very early stages of the disease, when still latent. The latter will have little chance to infect others from a strict quarantine, but the first probably already did. On top of that, there is strong evidence that the large majority of cases are asymptomatic, and to our knowledge they are also capable to infect others, to some extent. One might think of the detected, symptomatic cases as the tip of the iceberg that gives you a measure, a “tracer” of the total infectious population. In that sense, I believe the I-curve does represent the infectious group to a good approximation.

I look forward to read your take on why the population size has to be small. I wish this could be derived from the level of stringency and ultimately to have a model that helps you find a cost-effective optimum level for these measures.

POSTED BY: Luis Borgonovo

I do not think using I(t)*S(t) makes much sense in a locked-down environment. Might make more sense to work with log(s), as a surrogate for average number of contacts made by a person per day. This would need to be localized to e.g. city/town levels to account for differences in population densities.

POSTED BY: Daniel Lichtblau

It might work, I tried different nonlinear terms but I didn't think on using log(S), thank you for the feedback, I will check it out!

POSTED BY: Luis Borgonovo

Nice post! I'm glad you had better success fitting the data than I did, and the comparison of several countries with quite varied mitigation strategies is interesting.

I'm still concerned that the total population has to be made so small, and we should be able to devise a model that effectively reduces the number of susceptible individuals in a mechanistically plausible manner.

The time delays, in principle, replace the duration of infection and time to death, and therefore the rate constants [Gamma] and [Mu] shouldn't be needed. Have you tried fitting the data without them?

Looking at the summary table, [Gamma] isn't much greater that [Mu], only a factor of 15-30 for the different fits, so the equation for I should still include the term for death. Did you try modeling without making that assumption?

It seems that mathematician like to make these kinds of simplifications so that the system can be linearized, and additional statements about steady states and stability can be made more easily. With the capabilities of WL, I don't think that's necessary and we can get sufficiently accurate answers to those questions with simulation. Sometimes these simplifications are made to reduce the number of parameters, but again it's almost trivial to get sensitivities and and one can still maintain sufficient rigor in parameter fitting by making use of proper identifiability analysis.

POSTED BY: Robert Nachbar

Thanks Robert! I share your concern about the reduced total population, something I briefly comment in the Discussion, and I’ve been thinking about ways to do it in a less artificial way, but still working on it. I thought about sharing the results of this approach, just to show its pros and cons.

I haven’t tried what you are suggesting, I will check and comment you back soon, in a few days :-)

POSTED BY: Luis Borgonovo

Forgot to mention, I did try to include the mu term in the infectious equation but it was very unstable, so in the end I thought did not worth the trouble.

POSTED BY: Luis Borgonovo
POSTED BY: Luis Borgonovo
Reply to this discussion
Community posts can be styled and formatted using the Markdown syntax.
Reply Preview
Attachments
Remove
or Discard