It's almost time!! Follow along with the hashtag #COVIDdynamics as we head into the first day of our three-part series on #SARSCoV2 #COVID19 Dynamics & Evolution, with this 1-day virtual conference covering the *latest* science behind the pandemic!!

We start today with a session on #COVID19 Epidemiology! with @nataliexdean @JustinLessler @StephenKissler & @kathysleung covering topics like vaccine evaluation , #TestTraceIsolate , geographic disparities , and the D614G mutation ...

#COVID19 #SARSCoV2 #COVIDdynamics

First up is @nataliexdean - " #COVID19 Vaccine Evaluation" - reminding us the data does not exist yet! However, what should we be expecting in the data coming in the next few months?
Many trials to assure safety, immune response, & primary outcome (prevention)

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Many vaccines in development.
RNA & DNA vaccines are designed for pandemics as are 'plug & play' - faster in theory. However - no licensed vaccines & require several doses & little evidence of efficacy.

Other methods have more history, but all uncertain at moment
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Important message from @nataliexdean "There is more we can do in the downtime between epidemics to help our response during epidemics."
Though here this is in context of vaccines - impossible to overstate how true this is generally!!!

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#COVID19 trails should:
- Ideally include elderly (high risk)
- Randomization (vaccine vs placebo or established vaccine)
- Primary endpoints ( #COVID19 disease of any severity)
- Some vaccines may prevent severe disease but not infection - still important to know!

How to make trials work?
- Smart enrolment (high risk , big numbers, hot-spots )
- Use combination of models to predict good modelling sites
- Multi-site trials as epidemic prevalence wanes & waxes across globe?

#COVIDdynamics

Now we have @JustinLessler, "What makes #TestTraceIsolate work: a modelling framework & evaluation tool"
Points out it's 1 of options available with low social & economic cost - & has been used effectively in some places. But efficacy can be impacted by resources

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Each of 3 groups (Tested, Quarantined & Community) will have different reproductive numbers (R):
Testing, if mostly at symptoms, may only prevent a few transmissions. But their contacts (Quarantine) can be caught early, pre-symptom - preventing more transmissions.

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Of course, real transmission more complex - diffs in how asymptomatic & symptomatic tested & traced & how they transmit.

If low % of cases isolated, delays don't matter so much. As succeed in isolating higher % of cases, delays count more.

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But really critical to remember that nobody is doing #TestTraceIsolate alone! Hard to control through this alone, but "it can make your job much easier in terms of other control measure"

In other words, it lets us get more 'normal' while containing cases!
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And now, @StephenKissler, talking about "Geographic disparities in #SARSCoV2 prevalence are predicted by reductions in commuting-style mobility"

'Taking stock' of what happened in NYC & trying to understand variations in hospitalizations, deaths, & prevalence

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'The story of disparities' has been a big one of the #COVID19 pandemic, says @StephenKissler.

By NYC borough, hospitalizations & deaths 2x as high in Bronx as in Manhattan - huge disparities. Could be difference in symptoms, or true prevalence.

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Can we get semi-random testing - in a group not presenting for #COVID19 symptoms? Ex: women delivering. Showed fair positivity rate - many asymptomatic /at time/. How do we use this to estimate pop prevalence? Must consider false s, as well.

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This testing showed same disparities - much higher in Bronx & South Queens than Manhattan. Why?
Was ability to distance important? Could look at mobility data generally as proxy for distancing...

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But this proved complicated! As mobility in borough went up - but could be walks, groceries, etc. So instead, look at community - leaving borough in morning & returning at night. And yes - commuting correlates with prevalence.

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However, correlation doesn't imply causation! #COVID19 outcomes reflect many disparities. Need to support neighbourhoods where distancing most difficult (eg essential workers).

#COVIDdynamics