Am concerned that implicit use of uninformed priors has severely limited pandemic responses:
(i) Inaction is favored over action.
(ii) Information void is soon filled by misinformation.
From masks to immunity to vaccines; let's not keep making the same class of mistake. 🧵
Ex 1: No evidence of being airborne, despite this being a respiratory illness.
Just because airborne spread wasn't fully vetted does not mean it wasn't likely. Mask use delays are a consequence of remaining 'uninformed' about routes despite many examples (choir/etc.).
Ex 2: No evidence of protection from reinfection, despite the nearly universal absence of reinfections.
Without looking to SARS-1/MERS, then proactive steps to leverage and expand sero testing and interventions were missed (including surveys for missed infections).
Ex 3: No evidence of protection from transmission w/vaccines, only reduction of severe illness.
Thankfully, it seems shift is happening, but messaging must balance the impacts on hesitancy if the message remains that there is no evidence of reduction in infection/transmission.
Ex 4: No evidence asymptomatic transmission is major route.
The use of symptom based checks predominates despite ample evidence of asymptomatic/presymptomatic spread. Recognizing this route shifts messaging to mask wearing + testing (still under-utilized as mitigation).
Ex 5: Testing is not proven as a means of mitigation.
Even simple models reveal how this could work; and those few innovators who did so have made a difference. The CDC could have shaped the landscape by value-ing testing as mitigation given early successes.
In total, the burden of proof is higher if one starts off with completely uninformed priors, so much so that it can paralyze action and public understanding. Yes, one must proceed carefully, but doing so w/completely uninformed priors misrepresents the dynamics at play.
Perhaps this is too meta, but don't think so.
Am hopeful that the vaccine story can shift quickly from 'no evidence that vaccines reduce transmission' which, if predominates, will almost certainly be proven wrong and almost certainly negatively impact vaccine uptake.
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Hard to reconcile aspired branding with institutional values expressed through budgets, see Governor proposal that "the Department of Public Health would receive $7 million less in total state funds" when comparing FY22 to FY21 (see @GaBudget analysis)
"Instead, the Commissioner’s presentation indicated that the state’s pandemic response in its entirety would be funded solely by federal dollars. It signals that this pandemic is not Georgia’s problem and public health more broadly is not Georgia’s problem."
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.
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).
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.
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).
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.
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
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.
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.
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.