mathematics (3)

Lies, Damned Lies, and Statistics...


Which states have dropped mask mandates and why, Marlene Lenthang, Yahoo News

Topics: Biology, COVID-19, Dark Humor, Existentialism, Mathematics, Politics

Figures often beguile me, particularly when I have the arranging of them myself; in which case the remark attributed to Disraeli would often apply with justice and force: “There are three kinds of lies: lies, damned lies, and statistics.”

Mark Twain, also:,_damned_lies,_and_statistics

A follow-up to Tuesday's post: VOC...

‘No Thank You, Mr. President’: GOP States Still End Mask Mandates Despite Covid-19 Rise And Warnings From Biden, CDC, Alison Durkee, Forbes Business, April 2, 2021

Having some "fun" with mathematics. It's dark humor for all you young libertarians.

The current US COVID deaths are 573, 988 from

The current US population is 332,494,997 from Each link updates minute-by-minute, so by the time you read this, these figures will have changed.

(US COVID deaths/current US population) x 100 = 0.17%. Round up to 0.2%.

That's pretty low.

For the "freedom-loving libertarians" spring breaking in Miami, or Fort Lauderdale, Florida, and Corpus Christi, Texas - a thought experiment:

100,000 of you are about to dive into the ocean.

There is a 0.2% = 0.2/100 chance some of you will get devoured by sharks.

100,000 x (0.2/100) = 200 dead spring breakers.

So, out of 100,000 - 200 = 99,800, or 99.8% have a very good chance of not becoming "chicken of the sea," and surviving your spring break. The dilemma is, there will still be blood in the water. Blood that carries pathogens that despite your "Y" swimming lessons and the saline environment, you might ingest red tide, and suffer the consequences.

The problem is, your 0.2% chance is not zero. Under normal circumstances (and pandemics are once-in-a-century "not normal"), there's no libertarian case for this:


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COVID, and Math...


Image Source: Link below

Topics: Biology, Chemistry, COVID-19, Mathematics, Physics

The year 2020 has been defined by the COVID-19 pandemic: The novel coronavirus responsible for it has infected millions of people and caused more than a million deaths. Like HIV, Zika, Ebola, and many influenza strains, the coronavirus made the evolutionary jump from animals to humans before wreaking widespread havoc. The battle to control it continues. When a disease outbreak is identified—usually through an anomalous spike in cases with similar symptoms—scientists rush to understand the new illness. What type of microbe causes the infection? Where did it come from? How does the infection spread? What are its symptoms? What drugs could treat it? In the current epidemic, science has proceeded at a frenetic pace. Virus genomes are quickly sequenced and analyzed, case and death numbers are visualized daily, and hundreds of preprints are shared every day.

Some scientists rush for their microscopes and lab coats to study a new infection; others leap for their calculators and code. A handful of metrics can characterize a new outbreak, guide public health responses, and inform complex models that can forecast the epidemic’s trajectory. Infectious disease epidemiologists, mathematical biologists, biostatisticians, and others with similar expertise try to answer several questions: How quickly is the infection spreading? What fraction of transmission must be blocked to control the spread? How long is someone infectious? How likely are infected people to be hospitalized or die?

Physics is often considered the most mathematical science, but theory and rigorous mathematical analysis also underlie ecology, evolutionary biology, and epidemiology.1 Ideas and people constantly flow between physics and those fields. In fact, the idea of using mathematics to understand infectious disease spread is older than germ theory itself. Daniel Bernoulli of fluid-mechanics fame devised a model to predict the benefit of smallpox inoculations2 in 1760, and Nobel Prize-winning physician Ronald Ross created mathematical models to encourage the use of mosquito control to reduce malaria transmission.3 Some of today’s most prolific infectious disease modelers originally trained as physicists, including Neil Ferguson of Imperial College London, an adviser to the UK government on its COVID-19 response, and Vittoria Colizza of Sorbonne University in Paris, a leader in network modeling of disease spread.

This article introduces the essential mathematical quantities that characterize an outbreak, summarizes how scientists calculate those numbers, and clarify the nuances in interpreting them. For COVID-19, estimates of those quantities are being shared, debated, and updated daily. Physicists are used to distilling real-world complexity into meaningful, parsimonious models, and they can serve as allies in communicating those ideas to the public.

The math behind epidemics, Alison Hill, Physics Today

Alison Hill is an assistant professor in the Institute for Computational Medicine and the infectious disease dynamics group at Johns Hopkins University in Baltimore, Maryland. She is also a visiting scholar at Harvard University in Cambridge, Massachusetts.

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5G Caveat Emptor...
New 5G antennas (left) are smaller than 4G ones (right). Upcoming 5G networks will use higher-frequency radio spectrum, which will provide more bandwidth and enable the faster data-transfer rates that new technologies, such as autonomous vehicles, smart energy grids, and internet-of-things devices, will demand. (Photos by KPhrom/


Topics: Electromagnetic Radiation, Mathematics, Stochastic Modeling, Research, Satellite, Weather

The fight is on over 5G. Telecommunication companies and the US government promote the latest mobile broadband because it will provide faster data-transfer rates than the current broadband communication standard. Faster, more reliable digital communication is needed for the newest technologies—autonomous vehicles, internet-of-things devices, and smart energy grids, among others. But meteorologists, US science agencies, and other countries worry that strong 5G signals, if not properly regulated, may interfere with satellites that are crucial to weather forecasting.
Today’s 4G network, nearly a decade old, moves data by bouncing radio waves between cell towers and devices such as smartphones. A 5G network would operate similarly but use a wider frequency range and more bandwidth, which would increase data-transfer rates by an order of magnitude. The higher-frequency signals proposed for 5G can’t travel through buildings like their lower-frequency 4G counterparts, but specialized antenna arrays would transmit the 5G signal across long distances. Earlier this year, two telecom companies in South Korea launched small 5G networks using busy lower-frequency bands, and Verizon deployed a 5G test in Chicago at the higher-frequency 28 GHz band.
Widespread 5G deployment will depend on building a new infrastructure of antennas that operate in high-frequency radio bands. Telecom companies and US regulators support 24 GHz for 5G networks because of its greater bandwidth and because the 1–6 GHz radio spectrum is already crowded with 4G, digital TV, radar, and other applications. (The 24 GHz band spans 24.25–24.45 GHz and 24.75–25.25 GHz.)


Fifth-generation broadband wireless threatens weather forecasting
Alex Lopatka, Physics Today

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