On May 10, Mr. @BorisJohnson proposed a simple equation to determine #COVID Alert Level. He then explained how the reopening policy could be shaped based on this "Threat Level".
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* Fast forward to tweet # 11 if you already know Dimensional Analysis.
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See this @wikipedia article for a technical description:
en.wikipedia.org/wiki/Dimension…
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In physics, there are seven fundamental dimensions. The most commonly used dimensions are Mass (M), Length (L), and Time (T).
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Newton's second law is often written as F = ma, or Force = mass × acceleration. Acceleration is the rate of change of velocity:
a = v/t.
Velocity = distance / time, so:
[v] = L/T,
where [ ] denotes dimension (or physical unit) of a variable.
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[a] = (L/T)/T=L/T^2.
Therefore, F = ma tells us that:
[F] = ML/T^2.
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[E] = M (L/T)^2 = ML^2/T^2,
Note that c is the speed of light, which means it has the same dimension as velocity:
[c] = [v] = L/T.
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However, an equation with incompatible dimensions CANNOT be correct.
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Covid Alert Level (C) is dimensionless, i.e., it has the dimension of a pure number:
[C] = 1.
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[day] = [time] = T, so:
[i] = P/T.
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[C] = [R] + [i],
or
1 = 1/T + P/T.
This is mathematically impossible for the same reason that one cannot write the following equation:
Wall = Snake + Oil.
3 variables with 3 different units!
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doi.org/10.1016/j.ecol…
This indicator combines many variables to determine the sustainability state of a society.
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It is also unnecessary, because in the epidemic system, we have simple, clear variables that can guide the reopening policy/decisions: DAILY NEW CASES & DAILY DEATHS.
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hindawi.com/journals/cmmm/…
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Below, I will provide examples of countries that are past the rapid transmission phase of #coronavirus, countries that are close to success, & countries that are far from ready to reopen.
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dw.com/en/spike-in-so…
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#BritishColumbia #Vancouver can safely reopen as it has ~10 daily new cases.
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Politicians should not confuse people with incorrect equations & ad hoc indicators when daily new cases can show our safety status so clearly.
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A big thank you to @bing for the plots and datasets. And thank you all for reading my thread!
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