(PDF) Correlation of COVID-19 US Map and US Geoelectric field Map. First impression, Facts, and Hypotheses (researchgate.net)
There could be a correlation - without any causal relation.
i.e. the arrival of the stork and childbirths.
I can't imagine any causal relation.
The cause of Covid-19 is described here:
https://www.researchgate.net/post/Covid-19_Erythropoietin_EPO_deficiency_syndrome2
https://www.researchgate.net/post/PostCovid_syndrome_continued_Erythropoietin_EPO_deficiency_syndrome
Dear Klaus, thank you. Every point of view matters.
Regards, Anatoly
Your link is broken, here is what I assume is the correct URL: Preprint Correlation of COVID-19 US Map and US Geoelectric field Map....
First, you would have to establish that change in magnetic field measured in nanoteslas would cause any biological effect ( remember the 'cell phones causing cancer fad a while back"? The US Navy did a study of life time cancer prevalence of military specialties like electrical technicians, power network maintainers, who had exposures several orders of magnitude more than the ordinary population, and they were actually healthier ). The most intense magnetic field an ordinary person encounters is from an MRI: "Perhaps the safest component of the MRI exam is the static magnetic field. By examination of the current literature, and within the limits of our knowledge, the only health hazards significantly associated with the exposure to static magnetic fields are related to the presence of ferromagnetic materials or cardiac pacemakers in patients."
Nanoteslas, an MRI is between 1.5T and up to 7.0T ( GE ). I get a 1000x change in exposure simply by stepping closer to a microwave.
The second is the 'appearance' of the COVID mapping. The appearance is based on the areas of the counties that make up the map. See "Empirical Studies on the Visual Perception of Spatial Patterns in Choropleth Maps", Journal of Cartography and Geographic Information ( https://bit.ly/3LBT5eh ). Basically “dark-is-more bias” (i.e., the intuitive ranking of color lightness), the “area-size bias” (i.e., the neglect of small areas, since these are less dominant in perception than larger ones). The map shows 'areas', so units should be 'whatever' per area. If one wants to use 'rate' as the input variable, it should be converted using the population density for the given area. For a Untied States map, it's 'made worse' because the areas have a lot of variation at the state and county levels:
- Los Angeles County alone has 10 million people, (roughly) more than ten US states combined ( Wyoming, Vermont, Alaska, North Dakota, South Dakota, Delaware, Rhode Island, Montana, Maine, New Hampshire ), - - Kalawao County is 12 sq miles and San Bernardino County 20,105 sq mi. So when some one is interpreting a map and making comparisons, and areas are the basis, it is things ( events, pop counts, bushels, etc. ) per area so comparisons can be made.
The US Census has gridded population count maps of the US to avoid this issue.
The third is the totally incommensurable units in time, resolution, sampling, space and interval, etc. Did you read the documentation of the accumulation of Covid statistics? Especially latency from actual infection, patient admitted, a diagnosis made, cases for the health facility, then reporting up through the various levels until a final statistics? At one point the delay ranged from 1 week to 8 weeks or more, depending on the jurisdiction, now it is about 1 to 2 weeks. Most of the cases are in a very small area of the Covid map ( https://www.census.gov/library/stories/2017/10/big-and-small-counties.html ) ... and one-fifth of COVID-19 infections are asymptomatic throughout the course of infection (NIH), and not everybody that had symptoms became an official statistic.
I could similarly address the NOAA map - did you notice where the measuring stations were located and the interpolation method used to generate the heatmap? There is considerable literature around 'Cognitive Cartography', and how well people can compare similarities and differences and how to experiment with that ( https://m.xkcd.com/2256/ ) - in general, people are terrible at it.