Ct values can be used to estimate epidemic dynamics! We show that the distribution of viral loads changes during an epidemic and develop a new method to infer growth rates from cross-sectional virological surveys without using reported case counts. 1/25https://www.medrxiv.org/content/10.1101/2020.10.08.20204222v1 …
-
-
3. So the distribution of viral loads sampled today depends on the distribution of times since infection, which in turn depends on the epidemic growth rate. Therefore, with appropriate calibration, the distribution of observed Ct values is informative of epidemic dynamics. 11/25pic.twitter.com/5QJuhoM8We
Show this thread -
This is a simple cartoon, but it shows how epidemic dynamics -> time since infection distribution -> viral load distribution -> observed Ct values. How these steps link might change depending on eg. the instrument used to measure Ct values, but the general principle holds. 12/25
Show this thread -
To demonstrate this, we simulated infections from an SEIR model. For each infected person, we simulated Ct values based on when in their infection course they were sampled. R(t) is clearly correlated with the median and skew of the detectable Ct distribution. 13/25pic.twitter.com/sqvw1YwzDI
Show this thread -
What about real data? We compared estimated R(t) (using EpiNow2) to the distribution of measured Ct values from Brigham & Women’s Hospital, MA since April. Despite a bit more noise, we saw the same relationship as in our theoretical predictions! 14/25pic.twitter.com/8D26FlCaPg
Show this thread -
Finally, (and this is still WIP), we used this relationship to write down a model to infer the epidemic growth rate using the distribution of Ct values observed on a particular day. 15/25
Show this thread -
The model has two parts: 1) a viral kinetics model describing the mean and variance of Ct values on each day post infection and 2) a model describing the relative probability of infection on each day prior to the observation day. 16/25
Show this thread -
We found that with a single cross section of detectable Cts, we could correctly identify if the epidemic was growing or declining. They’re noisy estimates, but bear in mind this is showing longitudinal dynamics from a *single cross section* of data. 17/25pic.twitter.com/i8wn2cKz9B
Show this thread -
Also, to account for uncertainty in the viral kinetics trajectory, we used a Bayesian approach and placed informative priors on most of the model parameters rather than fixing them. If we knew these parameters with more certainty, these estimates would be more constrained. 18/25
Show this thread -
We’ve shown how the distribution of Cts changes during an epidemic, which reflects the trajectory. We propose that incorporating Ct values epidemic dynamics inference can retain more information than simply assessing “how many new PCR positive tests did we report today?” 19/25
Show this thread -
We can test whether the epidemic is growing or declining based on a single cross-section of viral load measurements. This could be a valuable tool to discern if rising case counts are due to true rises in incidence or just changing testing capacity, as some have suggested. 20/25
Show this thread -
We hypothesize this method would be less susceptible to bias than R(t) estimation using case counts, which are hindered by reporting delays and testing capacity. The information here comes from the *distribution* of Cts rather than the (potentially biased) number of cases. 21/25
Show this thread -
For anyone still skeptical, I refer to the large literature on using antibody titers to infer epidemic dynamics. Antibody titers -> time since infection -> incidence. This is exactly the same idea, just using viral loads. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007840 … 22/25
Show this thread -
There are some exciting extensions that we’re working on, so stay tuned for more data and some tighter posteriors! All code here: https://github.com/jameshay218/ct_dynamics_preprint … ... though we're not able to share the raw BWH data at this time. 23/25
Show this thread -
I foolishly put this at the end, but this is a joint endeavor with Lee Kennedy-Shaffer (not on twitter), led by
@michaelmina_lab, with invaluable input from@SanjatKanjilal and@mlipsitch 24/25Show this thread -
… and I’ll stop there before someone has to start saying “THANK you, Mr. Vice President” over and over again until I shut up. (Topical reference, not suggesting that I’m important
).
25/25Show this thread
End of conversation
New conversation -
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.