Announcing: brief report for Fall 2020 intervention surveillance @GeorgiaTech represents the work of many, released to help inform, guide, and improve efforts to use viral testing as part of integrative mitigation.

A thread follows...

medrxiv.org/content/10.110…
Key messages:

Viral testing can mitigate outbreaks, when used at scale.

Expect outbreaks to be heterogeneous (both 'good' and 'bad' news with respect to control).

Passive testing is not enough, we recommend using infectious data to reinforce testing/control.

...
Key metapoint: models helped inform the scale and frequency of testing, but the point of intervention was not to score theoretical points (e.g., post-hoc matching of models and data don't help stop cases here and now).

...
Instead, it is critical to develop cognitively simple approaches to response, enabling individual decision-makers to react intuitively and rapidly (i.e., develop instruments but let humans fly).

For example: reinforce testing/quarantine/contact tracing in incipient clusters.
Point 1: Viral testing can mitigate outbreaks, when used at scale.

Take-away for policymakers, more testing implies more documented cases in the near term but fewer actual cases in the long-term.
Soon after Fall 2020 term, a case cluster was identified in a Greek house, and then more cases in other Greek houses and adjacent residences. Rather than view such events passively, we responded by increasing testing, messaging, and contact tracing.
Hence, although we found many cases, the process of identifying cases and/or quarantining individuals came early enough that we rapidly went from 4% positivity to <1% (compared to UGA which saw post-entry rates approach 9%).
Point 2. Expect outbreaks to be heterogeneous.

As was documented in multiple town halls in the Fall, we observed a 75-25 rule (i.e., subset of houses/halls with disproportionate share of cases), e.g.,:

figshare.com/articles/prese…
This is challenging, because we would have been hard pressed to predict the 'seed' cluster in advance. But, once identified, the clustering of cases means that targeted responses can be more effective than in a 'homogeneous' mixing model where transmission is equal everywhere.
Hence, the bad news of large localized outbreaks can be 'good' news, if one works to use testing to identify locales and reinforce mitigation efforts.
Point 3. Passive testing is not enough, we recommend using infectious data to reinforce testing.

This does not get enough attention in models, policy, or press. Testing must be a form of active response.
My advice to colleges/firms starting to return to work w/testing plans: look at the data... every night and use as a guide to adjust responses. Don't try and score theoretical points, try to make a practical difference.
To do so, keep in mind that under-testing usually implies that a few cases are the tip of a cluster-berg. Formally speaking this is contained in hypergeometric distributions (but even using simple guidelines of connected spread are enough to initiate reinforcement testing).
Message out to localized halls/houses (or floors), with an expectation of testing a few days after identification of case cluster. Hence, when testing becomes active, you can learn from information and help mitigate.
In August 2020, GT re-opened in a difficult context (elevated spread in Georgia and the South, generally), because of work that had started months on the lab, logistics, and analytics side.
We continue to see ~1% positivity, continue to provide free, fast, and simple saliva-based testing and aim for transparency in reporting cases.
And one last point: testing is key, but is not a panacea.

One of the precursors that made this possible, was a summer effort by many to make sure that masks were required on campus.

ajc.com/blog/get-schoo…
That early win set the stage for an integrated approach, because the benefits of less intensive testing (we have capacity for weekly testing) increases w/NPIs.
Full details of the report available via @medrxivpreprint , and deeply grateful to work of @genomestake, Michael Shannon, JulieAnne Williamson and dozens of others who made (and continue to make) this intervention possible.

medrxiv.org/content/10.110…
p.s. Editors interested in seeing this in a journal, feel free to ping (seriously), at present the #Covid19 publication review process is broken in many ways, so delighted to engage to reduce wasted time/effort and find a good fit to vet results and enhance sharing w/community.
p.p.s. See mytest.gatech.edu for more details and info.

/end of thread

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More from @joshuasweitz

16 Jul 20
This thread is on #Covid19, heterogeneity, herd immunity, and the roots of SIR models; why mathematical choices we often take for granted have profound effects on interpreting unfolding epidemics.

@arxiv manuscript: arxiv.org/abs/2005.04704
Code: github.com/aapeterson/pow…

1/n
Key take-away: our mathematical analysis of the *joint* dynamics of heterogeneity and infection reveals that the force of infection can reduce to a simple form: I x S x S (or variants thereof) rather than I x S.
This nonlinear change in epidemic models may have significant consequences to long-term predictions and lead to super-slowing down of epidemics (including reduced herd immunity thresholds).
Read 23 tweets
14 Jun 20
A challenge for college/university re-opening plans.

Assume there are approximately the same # of circulating cases in August as there are now, a reasonable assumption given plateaus.

If so, how many expected positive #COVID19 cases will there be when classes begin?

A thread
First, although answers differ by state, let's start with a national metric... ~270K cases in the past 14 days reported via @COVID19Tracking, which given 10:1 under-reporting could mean 2.7M new cases in the past 2 weeks (or more).
Not all will be infectious at the same period, so we might get to ~1M active circulating case (conservatively, assuming 4-5 days of typical infectiousness).

Answers will vary based on geographic and socioeconomic profile of students.
Read 15 tweets
1 May 20
One more COVID-19 related modeling update for the week; this time more conceptual in scope, but something that has been on my mind for weeks: peaks - whether they are ahead of us, behind us, or whether they here at all.
To start -- a perusal of any number of sites suggests that fatalities have gone up rapidly in many places, but then have lingered, via plateaus and long shoulders, here is a subset of country-curves from @FT

ft.com/coronavirus-la…
The y-axis is key insofar that large impacts of #COVID19 with over 200,000 reported global fatalities still means that the vast majority of individuals remain susceptible.
Read 16 tweets
29 Apr 20
Today, we are releasing an update on GA-level forecasts: a Metapopulation AGe-structured Epidemiological (MAGE) model for COVID-19 in Georgia, USA.

The central projections can be seen in the figures with a full report available here:

weitzgroup.github.io/MAGEmodel_covi…
MAGE is an age-structured COVID19 epidemic model extending a SEIR framework to include hospitalization, demography, and commuting information for all of GA’s 159 counties.

MAGE development was led by @BeckettStephen w/key contributions from @marian_dm12 and @friendly_cities.
We initialized the model by fitting to data for Georgia on March 28, 2020 (including existing county-level heterogeneity); and projected forward in time under a scenario with a 50% reduction in COVID-19 transmission rates due to social-distancing interventions.
Read 11 tweets
17 Apr 20
How many people have been infected with #COVID19 in Georgia?

A: ~17,000, at least according to the GA DPH:
dph.georgia.gov/covid-19-daily…

But, it is likely far more.

I will use this thread to explain why I would not be surprised if actual GA numbers exceed 150,000+.
First, we have been working on GA-level scenarios since mid March, building upon the work of @neil_ferguson and others. Our models seed a COVID19 epidemic in GA until simulations appear to be “close” to the reports of cases, hospitalizations, and deaths.
But, getting reasonable models to get “close” to data was hard. The simulations could get close to hospitalizations and deaths OR cases BUT NOT all at once.

It could be that our model assumptions were wrong. Indeed, we know these are uncertain assumptions.
Read 14 tweets
14 Apr 20
The @BORUSG apparently made another (quieter) budgetary choice: it will not roll back the special institutional fee in FY 2021.

This means that graduate students will continue to pay ~$3000/yr in fees out of modest stipends:

usg.edu/assets/regents…
The @BORUSG budget tells us what the system values.

And what Chancellor Wrigley and @BORUSG Board Members have decided they can ignore.

And how out of step we remain compared to peer institutions.
A year ago, I spoke out publicly in @AJCGetSchooled on the need to roll back these fees:

ajc.com/blog/get-schoo…
Read 11 tweets

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