Ct values can be used to estimate epidemic dynamics UPDATE! Ct values are expected to change depending on whether the epidemic is growing or declining, and we harness this to estimate the epidemic trajectory. Lots of cool new analyses and methods!

Highlights:
- Cts from symptom-based surveillance change over time, but the effect is weaker
- Methods to infer incidence using single cross-sections of Cts
- Unbiased by changing testing coverage
- Gaussian process (wiggly line) model for incidence tracking using Ct values
2/12
This work is *JOINTLY led* with @LeekShaffer and PI’d by @michaelmina_lab. Thank you also to the ever insightful @mlipsitch and to coauthors @SanjatKanjilal @gabriel_stacey and @nialljlennon. 3/12
Premise: times since infection depend on the epidemic trajectory. Distributions of randomly sampled viral loads proxy times since infection. With calibration, Ct values can estimate growth rate. We focus on qPCR in SARS-CoV-2, but the principle applies to any outbreak. 4/12
Result 1: viral loads are shifted higher (Cts lower) during epidemic growth and lower (Cts higher) during decline when individuals are sampled *based on the onset of symptoms*. We simulated linelist data under symptom-based surveillance and looked at TSI and Cts over time. 5/12
This is crucial when considering virulence in emerging SARS-CoV-2 variants. Lower Cts over time do not *necessarily* mean newly dominant variants have higher virulence. If incidence of a new variant is increasing, then we expect to see more recent infections and lower Cts. 6/12
**However, the effect is smaller than under random surveillance, so I would not rule out the possibility of increased virulence.** But important to consider. Thank you to @charliewhittak for chatting through this! 7/12
Result 2: we reconstructed the epidemic curve using single-cross sectional samples from well-observed nursing homes, finding that single cross sections using the full Ct distribution provided similar insights to point prevalence across three sample times. 8/12
Result 3: we compared Ct-based to case-count based methods when testing is changing. Rt estimates are biased when testing is increasing or decreasing (not a problem with the method, just the data!). Our method uses the Ct distribution so does not care about test numbers. 9/12
Result 4: we use multiple cross-sectional samples to reconstruct incidence without making assumptions about the trajectory shape (a Gaussian “wiggly” process model). We can track the incidence curve in MA using routinely collected hospital tests. 10/12
… and here is a gif that reminds me of a nematode worm. Every week we add on a new cross section of Cts and accurately track true incidence (in simulation, red line). 11/12
Conclusion: we are generating loads of (semi) quantitative data in the form of Cts. We can harness these to get unbiased estimates of the epidemic trajectory. Hopefully these ideas will help public health surveillance efforts and interpret data in the light of new variants. 12/12

More from Science

@mugecevik is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
1/


The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/

The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/


The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/


The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
All modern research questions frame your mindset and self-frame research paradigm. Broad thinking: how little of everything can a citizen survive on; how cheap can your upkeep be? /1


When an American patient lands in an Austrian hospital for a back problem, a doctor tells him to perform a set of exercises.

- How many?
- Do you have anything else to do? /2

This interchange illustrates two mindsets colliding at bedside. How little can I get away with vs there is no limit to effort when it comes to your wellness. /3

When you were robbed of movement, somebody started selling you exercise. To understand that digging a ditch, to build a house, or to carry a child around, or waking to your grandparents for an hour is not the same as jogging on a treadmill... will reveal what research hides.
/4

When I talk about doing a purposeful activity outdoors, I look at complexity of movement, purpose, meaning, sun, and air, even an opportunity to meet a neighbor... that is now reduced to a calcium pill, vitamin D, an antidepressant, an osteoporosis shot, and an oxygen tank. /5

You May Also Like

@franciscodeasis https://t.co/OuQaBRFPu7
Unfortunately the "This work includes the identification of viral sequences in bat samples, and has resulted in the isolation of three bat SARS-related coronaviruses that are now used as reagents to test therapeutics and vaccines." were BEFORE the


chimeric infectious clone grants were there.https://t.co/DAArwFkz6v is in 2017, Rs4231.
https://t.co/UgXygDjYbW is in 2016, RsSHC014 and RsWIV16.
https://t.co/krO69CsJ94 is in 2013, RsWIV1. notice that this is before the beginning of the project

starting in 2016. Also remember that they told about only 3 isolates/live viruses. RsSHC014 is a live infectious clone that is just as alive as those other "Isolates".

P.D. somehow is able to use funds that he have yet recieved yet, and send results and sequences from late 2019 back in time into 2015,2013 and 2016!

https://t.co/4wC7k1Lh54 Ref 3: Why ALL your pangolin samples were PCR negative? to avoid deep sequencing and accidentally reveal Paguma Larvata and Oryctolagus Cuniculus?