THE “FLU SEASONS” AND THE MISSING DATA: A MATCHED-PAIR ANALYSIS FOR THE PANDEMIC SEASON 2019_2020

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.Background: The matched-pair analysis does not compare between viruses or the different laboratory practices. Each virus among n is match-paired with itself in the two responses of its own partial table. The Cochran-Mantel-Haenszel Test summates all these partial tables to arrive at the same marginal counts of the McNemar's Test.This test statistic is likewise used for the Rasch model and for the Transmission Dysequilibrium Test. Methods And Results: We used col (1, 2) =1wk for N_America, Europe/Asia and forS_America/Africa/Australia/New_Zealand. For Canada Row1/Row2 was (BC- Manitoba)/ (Ontario-Atlantic). For the US Row1/Row2 was Regions (7-10)/ (1-6). We performed simultaneous Proportional Odds Comparison of Margins (4x4 Table). We sequentially deleted Regions 1, 10, (9-10), (8-10), (1-4) and (1- 5) to define the effects of the missing data. And we surveyed for ILI pneumonias in Hong Kong for matched-pair regression. A(H1) and A(H3) surged/resurged with condition numbers (multicollinearity)=