If you are at #RSS2021Conf this morning, check out our invited session on Contagious disease #statistics and #misinterpretation of #COVID dynamics and data with @AdamJKucharski and @AyisSalma
11:40am, Exchange 10
First speaker is @AyisSalma, reflecting on the subtle but important difference of what we think and what data are.
#COVID19 statistics are easily misinterpreted
Statistical anomalies in calculations of #COVID19 infections and deaths meant it appeared that no-one ever recovered!
And these figures influenced government #publichealth decisions
The choice of denominator (population or subset of the population) is very important in #COVID19 research.
Confounding and collider bias impacting on estimates must also be considered
Can your #ArtificialIntelligence differentiate #cats 😸 from #COVID19?
Not always it seems!
deepai.org/publication/ca…
The second speaker is @AdamJKucharski on Making sense of #pandemic dynamics
Observed data are not very useful in epidemics. Data are incomplete, out of date, and often #biased.
Hindsight is a wonderful thing, but what can be done in real time?
Early methods included sequential #montecarlo modelling of the R statistic and infection rates.
But the estimate depends on the data source and must be interpreted in terms of data lag and under reporting
Scenarios can be modelled using a range of data sources and assumptions.
But it is it essential to #communicate the difference between #forecasts and #scenarios
❓What WILL happen vs what COULD happen?
Estimating R requires:
- Duration ⏳
- Opportunities 🤝
- Transition probability 🎲
- Susceptibility 🤒
But which data sources to use? And what really drives R?
Final reflections we can all agree with!
Modelling outbreaks and pandemics needs to be:
- Fast ⏩⏰
- Open 🧐
- Peer-reviewed 📖🤓
- Collaborative 🤝👫
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