Excited to share my latest preprint with @LeeKShaffer and @BillHanage, “Perfect as the Enemy of the Good: Using Low-Sensitivity Tests to Mitigate SARS-CoV-2 Outbreaks” in which we show how the math of superspreading events can improve contact tracing 1/
https://dash.harvard.edu/handle/1/37363184
The key idea is: if A is sick and has contacted B, B is probably still fine, but if you also know that A has infected C then there's a much better chance that B has been infected. Superspreading (or overdispersion) means that infection _events_ are correlated 2/
Here's where testing comes in: because B and C being infected is correlated, you don't need a test that gets both of them right all the time. Either one testing positive gives you information. So a low sensitivity test on all of A's contacts is almost as good as a perfect one. 3/
This suggests a strategy of "transmission tracing" where the main focus is isolating all of the contacts of anyone who is believed to have transmitted the infection 4/
Transmission tracing provides a means to substantially reduce the burden of quarantine/isolation and successive contact tracing while still identifying the vast majority of transmission events and, especially, superspreading events 5/
Interestingly, the main limitation of this approach is the speed and ubiquity of testing, not the sensitivity. So a $2 test that takes an hour, even if it only catches 50% of cases, can be over 90% as good as a perfect test. 6/
Moreover, if superspreading events arise in part because of higher viral loads in the index individual, they are less likely to test negative. So these analyses may be underestimating the sensitivity of tests in detecting individuals capable of initiating superspreading events 8/
By taking the mathematics of superspreading, particularly the correlation between transmission events, into account, we can hopefully use more tailored contact tracing to better control outbreaks with less cost to individuals. 9/9
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