Flavinkins (@Flavinkins)

https://gab.com/Flavinkins/posts/108739763559645668 https://gab.com/Flavinkins/posts/108723816541534100 The Hankou station was the #3 highest location on social media check-in in Wuhan, making up 8% all such check-ins. It is within Worobey’s 1% KDE contour. As it also serves as the first location connecting the international and air traffic from the Tianhe international airport with the bus, metro traffic within Wuhan and the train traffic to other locations in Hubei (and to an extent other locations in China too, especially with busier times of air travel), it serves as the distribution point of all traffic not only through itself but also through the Tianhe international airport, centering the epidemiological contribution of the two facilities combined—with a combined social media check-in number of 37185 (it have to handle all the traffic from people who checks in at both the Hankou station itself and the Tianhe airport) and being a public transport hub that sees people from all across the line 2 of the Wuhan metro, it become the #1 highest contributor to “case spreading nearby” in any epidemic in Wuhan, and almost the guaranteed first superspreading location for those that started abroad, or starting anywhere on line 2. (You need To first go through line 2, before changing to line 7 or line 4 if you want to reach the Wuchang or Wuhan railway station, if you start from the Tianhe airport or the WIV (Wuchang headquarters)……) https://www.sundayguardianlive.com/news/probe-wuhan-metro-line-2-spread-pandemic Include the effect of annex D5, and you have the reason why the “market unlinked” case “epicenter” is closer to the market to the “market linked” case epicenter. Train stations are liminal spaces. When you ask a case/person as where he/she worked at or visited recently, specific public transportation stations (especially the metro station part of the Hankou railway station, the busiest metro station on line 2 with average daily throughput (in and out of the metro station) of 135000+ persons daily + 85000+ persons daily going through the rails in the train station) would hardly come up as significant or be reported from memory. The same effect also breaks up any clusters of infectious disease that spreads from them, as even very large superspreading clusters within such a station would be mostly among strangers (not connected either by work or by home) and they would show no epidemiological link to each other at hospital admission part from geographical proximity from each other on the order of 1-2km. Recognition of an outbreak especially given how EID surveillance operates in China and the fact that it happened during the flu season in Wuhan (too much background signal to distinguish EID without consulting epidemiology) would only happen once the infection have spread to the nearest (and the only) wet market (with a stable, mostly elder population in close contact at work that is conductive to both the efficient spread and recognition of an outbreak of SARS-CoV-2, especially without a specific test) that was put under EID surveillance in Wuhan. https://gab.com/Flavinkins/posts/108939607348559500 While it is statistically very likely that households near a major transport hub will be visited through that transport hub (creates an “unconnected case”, only ones that would show up on search would be the ones in the “neighborhood of Huanan market” or in the cluster that is likely to visit “several hospitals(near Huanan market)” if they weren’t indirectly connected to the market by contact with a market case), any specific person living or working in any specific landmark near them is still only going to visit his/her own or family household, which is most likely several kilometers away from the landmark and will not be significantly overpresented in the vicinity of the landmark. Consequently, the “epicenter” of market-linked cases is further away from that of market-unlinked cases. https://gab.com/Flavinkins/posts/108923557475080584 “While other biases are certainly at work in the contour maps, the absence of any mention of it while referencing the social media check-in data from Sina Visitor System amounts to a certain cherry-picking of the data to fit a certain narrative.”