These would seem to be some important results:
To model COVID-19 spread, we use an SEIR agent-based model on a graph, which takes into account several important real-life attributes of COVID-19: super-spreaders, realistic epidemiological parameters of the disease, testing and quarantine policies. We find that mass-testing is much less effective than testing the symptomatic and contact tracing, and some blend of these with social distancing is required to achieve suppression. We also find that the fat tail of the degree distribution matters a lot for epidemic growth, and many standard models do not account for this. Additionally, the average reproduction number for individuals, equivalent in many models to R0, is not an upper bound for the effective reproduction number, R. Even with an expectation of less than one new case per person, our model shows that exponential spread is possible. The parameter which closely predicts growth rate is the ratio between 2nd to 1st moments of the degree distribution. We provide mathematical arguments to argue that certain results of our simulations hold in more general settings.
And from the body of the paper:
To create containment, we need to test 30% of the population every day. If we only test 10% of the population every day, we get 34% of the population infected – no containment (blue bars).
As for test and trace:
Even with 100% of contacts traced and tested, still mass-testing of just over 10% of the population daily is required for containment.
The authors are not anti-testing (though relatively skeptical about mass testing compared to some of its adherents), but rather think a combination is required in what is a very tough fight:
Our simulations suggest some social distancing (short of lockdown), testing of symptomatics and contact tracing are the way to go.
That is all from a new paper by Ofir Reich, Guy Shalev, and Tom Kalvari, from Google, Google, and Tel Aviv University, respectively. Here is a related tweetstorm. With this research, I feel we are finally getting somewhere.