Archive for October, 2020
Moving averages creating coronavirus confusion
Posted by mark in Uncategorized, Wellness on October 26, 2020
The statistics being reported on Covid-19 keep pouring in—far too much information by my reckoning. Per the nation’s top infectious disease expert, Dr. Anthony Fauci, I focus on positivity rates as a predictor of the ups and down of the coronavirus. However, the calculations for even this one statistic cause a great deal of controversy, especially in times like now with rising cases of Covid-19.
For example, as reported by The Las Vegas Review-Journal last week, positivity rates for the Nevada now vary by an astounding five-fold range depending on the source of the statistics. It doesn’t help that the State went from 7-day to 14-day moving averages, thus dampening down an upsurge.
“We’re trying to get that trend to be as smooth as possible, so that an end user can look at it and really follow that line and understand what’s happening.”
State of Nevada Chief Biostatistician Kyra Morgan, Nevada changed how it measures COVID’s impact. Here’s why., The Las Vegas Review-Journal, 10/22/20
My preference is 7 days over 14 days, but, in any case, I would always like to see the raw data graphed along with the smoothed curves. The Georgia Rural Health Innovation Center provided an enlightening primer on moving averages this summer just as State Covid-19 cases spiked. Notice how the 7-day averaging takes out most of the noise in the data. The 14-day approach goes a bit too far in my opinion—blunting the spike at the end.
I advise that you pay attention to the nuances behind Covid-19 statistics, in particular the moving averages and how they get shifted from time to time.
PS My favorite method for smoothing is exponentially weighted moving averages. See it explained at this NIST Engineering Statistics Handbook post. It is quite easy to generate with a simple spreadsheet. With a smoothing constant of 0.2 (my preference) you get an averaging similar to a moving average of 5 periods, but it is far more responsive to more current results.
Twenty declared plenty: How slow will we go?
As reported earlier this week by the Center of the American Experiment, motorists face a new 20 mph speed limit in Minneapolis and St. Paul. City authorities figure on a significant reduction in neighborhood traffic fatalities, based on the statistic that a person hit at 35 mph is three times as likely to die as someone hit at 25 mph (they are reducing limits another 5 mph to 20 mph out of an abundance of caution, presumably). Prior to a new law that came into effect a year ago, the Minnesota Department of Transportation (MnDOT) set speed limits based on engineering and traffic studies. But now cities need not involve MnDOT when setting traffic laws for residential streets.*
The lowering of speed limits in the Twin Cities follows a trend in USA metro areas from coast to coast as evidenced by this Seattle Department of Transportation post last December (check out the animated graphic showing how a person’s chance of surviving being hit by a car decreases drastically with faster speeds).
My thoughts:
- If 20 mph on residential streets got enforced, that would be a relief for those like me with young children at home (grandchildren in my case). However, I doubt this will happen, especially with cutbacks in police after the troubles in Minneapolis earlier this year. The lower limits will only work with plentiful speed bumps (more appropriately known as “sleeping policemen” in UK).
- Being an engineer, I worry about taking experts on traffic safety out of the loop in favor of politicians making sweeping edicts with no regard for varying factors for individual streets.
- What are the economic trade-offs of the added time needed to travel at slower speeds versus the increased safety? Is 20 mph optimal?
“Typically, drivers travel 8 to 10 mph above the posted speed limit with a perception that the posted speed limit is a minimum, not a maximum [and] when the posted speed limit is reduced, drivers do not obey the new limit or even pay attention to it unless there is significant enforcement.”
Research Brief: Review of Current Practices for Setting Posted Speed Limits, April 2019, AAA Foundation for Traffic Safety.
One thing for sure, I find it excruciating to drive at 20 mph for any distance. The seems to slow to me.
*Focus on New Laws: Cities Authorized to Set Certain Speed Limits, July 22, 2019, League of Minnesota Cities.
Statisticians earn residuals by airing errors
Posted by mark in design of experiments, pop on October 10, 2020
A new book by David S. Salsburg provides a series of Cautionary Tales in Designed Experiments. Salsburg wrote the classic The Lady Tasting Tea, which I read with great delight. I passed along the titular story (quite amazing!) in a book review (article #4) for the July 2004 DOE FAQ Alert.
Salsburg’s cautionary tales offer a quick read with minimal mathematics on what can go wrong with poorly designed or badly managed experiments—mainly medical. I especially liked his story of the Lanarkshire Milk Experiment of 1930, which attempted to test whether pasteurization removed all the “good”. Another funny bit from Salsburg, also related in The Lady Tasting Tea and passed only by me in my review, stems from his time doing clinical research at Pfizer when a manager complained about him making too many “errors”. He changed this statistical term to “residuals” to make everyone happy.
With all the controversy now about clinical trials of Covid-19 vaccines and the associated politics, Cautionary Tales in Designed Experiments offers a welcome look with a light touch at how far science progressed over the past century in their experimental protocols.
“It is the well-designed randomized experiment that provides the final ‘proof’ of the finding. The terminology often differs from field to field. Atomic physicists look for “six sigma” deviations, structure-activity chemists look for a high percentage of variance accounted for, and medical scientists describe the “specificity” and “sensitivity” of measurements. But all of it starts with statistically based design of experiments.”
David S. Salsburg, conclusion to Cautionary Tales in Designed Experiments