This week we look for answers into why the powerful consistent correlation to Total Test Results and Cumulative Deaths in the U.S. as well as the review of inconsistent data in reference to pandemic strategies. For those that want to take a shot at why the unusual correlation, please comment. Primary Data Sources; CovidTrackingProject and Our World in Data Language Python in Jupyter lab Primary Packages scipy, numpy, pandas, statsmodel, matplotlib, datetime, seaborn #mortalitycorrelations #ourworldindata #covidtrackingproject For those addressing the data correlation (From Covid Tracking Project) Total test results API field name: totalTestResults At the national level, this metric is a summary statistic which—because it sums figures from states reporting tests in test encounters with those reporting tests in specimens and in people—is an aggregate calculation of heterogeneous figures. Therefore, it should be contextualized as, at best, an estimate of national testing performance. In most states, the totalTestResults field is currently computed by adding positive and negative values because, historically, some states do not report totals, and to work around different reporting cadences for cases and tests. In Colorado, Delaware, the District of Columbia, Florida, Hawaii, Minnesota, Nevada, New York, North Dakota, Rhode Island, Virginia, Washington, and Wisconsin, where reliable testing encounters figures are available with a complete time series, we directly report those figures in this field. In Alabama, Alaska, Arkansas, Georgia, Indiana, Kentucky, Maryland, Massachusetts, Missouri, Nebraska, New Hampshire, Utah, Vermont, and Wyoming, where reliable specimens figures are available with a complete time series, we directly report those figures in this field. In Arizona, Idaho, and South Dakota, where reliable unique people figures are available with a complete time series, we directly report those figures in this field. We are in the process of switching all states over to use directly reported total figures, using a policy of preferring testing encounters, specimens, and people, in that order.
#mortalitycorrelations #ourworldindata #covidtrackingproject #scipy #pandas #pythonprogramming #numpy #coviddata#sarscov2 #conspiracy #datamanipulation #matplotlib
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