Abstract
The unit cell from the McNemar’s 2x2 Table denotes the week with col (1, 2) and the Public Health Region with Row (1, 2). We calculate the standard normal statistic (z) for A(H1), A(H3), Influenza B. Each one categorical unit is in fact a pair of matched-pair data within its own partial table. The Cochran-Mantel-Haenszel Test collapses these partial tables to summate these 2n observations in a 2x2 x n contingency table to yield the marginal counts of the McNemar’s test.
 The open data for Europe/Asia began this SARS-CoV2 pandemic, from week 3 to week 14, with the normal statistic (z) entering into an identical collapse mode. These all assumed the same “V” curve as the general collapse pattern and they rippled together without overlapping. During this period China applied mandatory lockdown and they mandated masks. We should strive to be more evidence-based so that we can convince more of the general public to accept the public health measures to survive.
Highlights
The null hypothesis is that the moment to moment changing or non-changing westerly winds have no relation with the distribution of the virus isolations within the geographic areas. This becomes a test for the matched-pair relationship
The McNemar’s Test and the Wald test (Proportional Odds Model) On Figure 1 for Canada, Figure 4 and Figure 8 for the US, Figure 5 for North America, Figure 6 for Europe/Asia, and Figure 7 for the Southern Hemisphere, we tested the matched-pair model generally with the standardized normal test statistic z of the McNemar’s Test on the upper left and compared this with the approximate z based on the Proportional Odds Model on the upper right
We present the best available collateral evidence in the form the simultaneous isolations for the influenza viruses (A(H1), A(H3) and Influenza B), when the lockdown was applied to every person in Wuhan (Figure 6) to stop the transmissions of Covid-19
Summary
The SARS-CoV-1 epidemic in 2002-2003 provided the background of this study when this started in Hong Kong. According to the prevalent concept of the “missing data” at the time, our findings relevant to the parameter’s “pneumonia” and the “history of contact” with influenza-like-illness were most surprising This is the Graph for Parameters “Pneumonia”. This graph monitored the “ɑ
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