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Hm. I really thought it might be that people with higher income search ME/CFS more, as they might have better access to resources that would make them aware of what ME/CFS is. And maybe states with larger proportions of Northern European or British ancestry would have higher average income. But...

This page seems to suggest that if you are looking at a simple search term, then it will show trends just for that specific term in that language. But if you search by a "Topic", then it tracks trends across different translated versions of that topic. -...

This was showing lots of UK countries at the top, and I realized that it may be because people could report multiple ancestries. From an overview of these datasets: I (and I think Murph) used B04006, which counts up to two ancestries per person. So, for example, the correlations for Scottish...

I agree with you and Yann, and I think the most likely reason for the correlation is for some reason more awareness of ME/CFS in people with British ancestry. But I think it's a fun idea that has a small chance of actually being about prevalence.

Hmm, I'm not sure. The values in the plots are only relative to the other time periods within the same state, so we can't compare volume between states just from looking at the individual state plots. It might be that the search volume is consistently high throughout the year, but the spike of...

Definitely doesn't necessarily hold. But I thought it was possible that people who speak another language at home might either be less likely to have heard the term ME/CFS, or less likely to search for it in English. So I wanted to rule that out as an explanation for states with higher British...

I was wondering if it might be that states with larger populations with UK ancestry have a larger English speaking population, so might be more likely to search for "ME/CFS". I found census data for "Language spoken at home" for each state...

For some reason Mississippi has extremely high searches for MS, making the regression not very reliable: Without Mississippi: So not a very strong correlation between MS searches and ME/CFS searches. Here is the regression predicting proportion with English ancestry from MS searches, and a...

Effects of predicted Khamisiyah exposure on default mode network resting state functional connectivity in Gulf War Veterans Chao, Linda L.; Torrisi, Salvatore Introduction Potentially more than 100,000 US troops were exposed to organophosphorus (OP) nerve agents when an ammunition bunker at...

Very interesting! I tried to see if I could replicate it. Data I used: Google Trends (3/23/25 - 3/23/26): https://trends.google.com/trends/explore?q=/m/0dctd&geo=US&legacy&hl=en Ancestry data from US Census Bureau (listed as the data source for the website you referenced in your article)...

Persistent class switch toward spike-specific IgG4 antibodies after repeated SARS-CoV-2 mRNA vaccination in post-acute infection syndrome: an exploratory cohort study Ana M. Espino, Carlimar Ocasio-Malavé, Riseilly Ramos, Ramon Sanmartin, Jesus Castro-Marrero Abstract Myalgic...

Wow, that's a very long and specific time frame! You don't experience this anymore, though?

Thanks! Yes, I try to always keep a mindset of every experience being a learning experience, even if a task isn't "successful" (see my favorite quote). I've learned a lot about the basics of time series analysis, and also now at least don't have to worry that I'm missing some very large...

Yes, maybe that could produce interesting results. I'm not sure what you mean, but my analysis basically consisted of trying to make sure the time series were more or less stationary, and then just doing a basic granger causality test using the Python statsmodels VAR package. The actual code...

Ah, sorry, after seeing your post, I realized that the large image above with all the time series did not actually include all the raw nutrients in the first column. It was after the initial filtering for null data. I updated it with the full list of nutrients. Iodine is there, but I filtered it...

Thanks! I'm mostly out of my depth and got completely exhausted from it. It took me two months after the analysis to post this because I could barely think about it after all the energy expended trying to do this correctly in the weeks/months before the analysis. Unfortunately, I'm not sure...

I got a couple cool charts out of it though. Here is a heatmap showing correlation between daily intake of different nutrients. For example, all the nutrient pairs in the dark red area have high correlations because they are all amino acids, so tend to all be high whenever I eat more protein...

Long story short, I did not find any significant associations, looking for associations of nutrient intake with time upright, when analyzing a year of nutrient data In January, after recording a year of foods eaten, as well as tracking time upright and amount of time sleeping every day, I tried...

This seems to suggest higher genetic influence for early onset cases. I guess we should expect this in most conditions. Those who have genes increasing risk of a condition are born with that risk factor, so onset can happen early on. Environmental risk factors can emerge at any time in a...

It looks like it's linked at the bottom (direct link to doc file). Here is the list of autoimmune diseases from Supplementary Table S1 and the sentences that reference the table: