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Since the COVID-19 outbreak conspiracy theories have been going around social media about links between  COVID-19 and the introduction of the  fifth generation of wireless communications technologies (i.e. 5G).  Some even claim that exposure to 5G is the real cause of the pandemic; either because it causes COVID-19 or  because the pandemic is just a cover-up by governments and industry to hide that they have switched on 5G.  As ridiculous as that sounds, this has been gaining ground and has been directly linked to the burning down of  communication masts (5G or not…) in the UK and elsewhere. There is an interesting discussion as to why this  conspiracy theory has become popular <link>.  In terms of scientific evidence, there is none that 5G causes COVID-19 and the ‘it’s a 5G cover-up by the  world government’ is too silly to discuss. There are some studies out there, mainly based on isolated cells  and some small animal studies, that suggest radiofrequency radiation (from mobile phones) can impact on  the immune system. The strength of the scientific basis or this is best summarized by Professor Carpenter;  well-known in ‘EMF world’:  Another well-known name in ‘EMF world’, Prof Leszczynski, highlighted Martin L. Pall and Arthur R.  Firstenberg as two of the main culprits that resulted, amongst other misunderstandings of the scientific  evidence, also fuelled the flames of this conspiracy <link>. Moreover, and this is an important point, these  individuals and groups moved an entirely reasonable question about some of the gaps in knowledge and  enquiries for more research in this area towards the crackpot science bin that also holds antivaxxers,  chemtrails, homeopathy and the likes.   It is relatively easy to understand why this theory was always going to be highly implausible. Some of the  countries hardest hit by COVID-19 were Iran, Spain and France, neither of which as far as I know have 5G. Unfortunately, all this social media hype drew in some others who, given their training, really should know  better. Dr Maria Havas, who previously peddled another failed idea of ‘dirty electricity’, is one of these. She recently published ‘Is there an association between covid-19 cases/deaths and 5G in the United States?’ on her blog (here). And of course, this got enthusiastically shared around twitter (and possibly other social media). As many of these studies, it is based on simple ecological (i.e. at group level) correlations of two variables: in this particular case a direct comparison of the number of COVID-19 cases and deaths in US States where 5G has been activated with US States that don’t have 5G (yet, I suppose). And the conclusion seems quite straightforward, as shown in a Table copied from her blog: The same number of tests have been done in both sets of States, but he number of case in US States with 5G is almost twice as high as States without 5G and the number of deaths is even 126% higher. She also conducted a T-test to show that this difference was statistically significant (interestingly, this only holds for a 1-tailed test, indicating that she completely discounts the possibility of 5G having a positive effect). Let’s have a look at the raw data on which this is based (Excel table also copied from her blog): Now I have only been to a couple of States in the United States, but eye-balling this Figure suggests to me  that 5G was not introduced ‘at random’ across the US. Again, I haven’t been there, but Alabama, Mississippi  and Kansas just seem different from, say, Florida, New York and California. That also makes sense from a  business point of view, because why would you go through the efforts of investing in building up a new  technological network in a place that is largely agricultural and where barely anybody lives?  These kind of ecological correlation analyses are always quite difficult to interpret, in that it is not too  difficult to come up with other reasons why you would observe this correlation. On twitter, my first guess –  given the above idea of where you would introduce such a new technology – was the above correlation was  confounded by ‘urbanization’.     * Magda Havas was kind enough to link to the primary sources that she used (here and here), so it was  straightforward to obtain the same data (well, not completely the same: the COVID-19 numbers had been  updated so I will be using data from a couple of days later). That gave me this Figure for COVID-19 related  deaths (I am not doing tests or cases because these are too unreliable and directly result from various  policies rather than the disease itself), which is pretty much identical to the Figure above:  The mean number of deaths in States with 5G was 136 and in those without 5G 60. This gives a ratio of 127%;  nearly identical to the original data.   * So starting from pretty much the same point of departure, I run a basic log-rate model (because it is numbers  of deaths per million citizens this is the correct model specification. I don’t think Magda Havas did that in  her T-test above though), and get an 82% higher death rate in States with 5G compared to those without (2-  tailed p-value ~ 0.06). This is somewhat lower than the straightforward comparison of averages, but as  mentioned above based on a more appropriate model…and it still shows a serious excess mortality risk.  I hypothesized that this correlation was confounded by something else and thought that ‘urbanization’ was a  likely factor. Urbanization rates for US States are easy to obtain (don’t worry I will provide the dataset and R  script at the end of this blog for you to play with), and I ran that model.   The big reveal of this blog is that when adjusting the correlation reported by Havas for the State  urbanization rate the correlation mostly disappears:  The excess risk has been reduced to about 34%  and the p-value is 0.36 (or, for the connoisseur, the observed difference between 5G and non-5G  States was not significantly different).  Nonetheless, although not statistically significant there is still an excess risk of 34% which lies between -28%  and +147% with 95% certainty. It is straightforward to look into this a bit more. There is more State-wide data  available that could potentially contribute to this difference. To explore this, I also linked the data for  median household income in each State, the percentages of non-Hispanic whites, States’ medium age (which  has a non-linear correlation with COVID-19 death rate), and the population density. Adjusting for all those  factors results in a 48% (-19%, +147%) excess mortality in States with 5G, but this difference again was not  statistically significant (p-value ~ 0.21). In fact, there are a couple of States that are clearly different from  the rest (outliers; Utah, New York, District of Colombia and Hawaii). When these are removed from the  analyses, the difference between 5G and non-5G States is less than 1% in favour of the 5G States (p-value ~  0.97). I suppose this is why Havas did not submit the work for peer-review. She must have known that the first  reviewer to look at this would point this out. I read an argument somewhere that apparently “we have to  start somewhere”. This is of course correct: we have to start somewhere, but don’t show it to anyone else  until we have done a good job. I am sure Havas is aware of bias, confounding and multivariable statistical  analyses, so there question is “Why did she decide to put this stuff online, if not to join Pall and Firstenberg  and fuel the fire of the conspiracy?”.  * * My blog could end here, but there is some more academic work that is easily done and will improve our  inferences. We can use a method called ‘inverse propensity weighting’. There is a lot of literature on this,  but in (very short) summary: each State is weighted by the inverse of the probability with which the mobile  phone operators would have selected that State for 5G. The analysis is then balanced  in such a way as if the  operators had allocated 5G randomly. That is really beneficial because it makes the study look somewhat like  the gold standard for such tests: the randomized controlled experiments (at least with respect to the  variables in the exposure propensity model, and yes it is a bit more complex. Point taken). Anyhow, I did this  using all factors above as well as additionally ‘population size’ (because I thought maybe operators went for  the largest States first).  Because this now kind of a random experiment the difference can be directly compared without adjustment  for other factors (like Havas did in the first Table in this blog). And wow, the effect has reduced to as little as  +10% with a p-value of 0.75.  A final further improvement is ‘doubly robust adjustment’, and these “best” results show there is actually  no evidence of any causal effect from 5G anymore: The difference is less than 1% and the p-value is  0.975.  With this, I think we can comfortably say that this conspiracy theory has been debunked. These analyses can of course be done with better data and possibly better models, so that’s why the dataset and R script can be downloaded here and here. It is also quite straightforward to repeat these analyses for other countries. I am looking forward to your contributions: just email or DM me on twitter if you have done this. * Addendum Someone combining the spatial data on COVID-19 and 5G and looking somewhat amateurish look at the  correlation was always going to happen, and it would have been straightforward to do it correctly. We  suggested this about 2 weeks ago to the mobile phone operators. This would have addressed the issues we  are facing now. Unfortunately, the operators didn’t trust independent research. In fact, they hired a ‘product defence’ consultancy agency to do a review for them – very disappointing (and hopefully a lesson learned).  
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5G and COVID-19: Fact or Fiction?
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