Geostatistical Analysis of SARS-CoV-2 Positive Cases in the United States
Bibliographic record
Abstract
Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com. This update of the Zenodo dataset (version 5) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. These datasets have been previously published in earlier versions of this archive (versions 2, 3 and 4): <strong>Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (May25-Aug2-v4).zip” </strong>– results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one week timespans (from version 4 of this Zenodo archive). Results are reported from the initial relaxation of distance constraints on Memorial Day weekend 2020 for ten consecutive 1-week intervals (May 25th through to August 2nd 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States. <strong>Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Jul13-v3).zip" </strong>– the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24<sup>th</sup> through July 13<sup>th</sup> 2020 (from version 3 of this Zenodo archive). <strong>Archive #3: "3.Initial Geostat. of SARS-CoV-2 in the US(v2).zip" </strong>– the results from the geostatistical analyses performed for version 2 of this Zenodo archive which analyzed COVID-19 case data prior to any case correction step. These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive week-long Space-Time Hot Spot analysis; ‘Archives #2 and #3’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archives #2 and #3’], Spatial Autocorrelation (Global Moran's I) [‘Archives #2 and #3’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’]. The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 5).docx") details the contents of each file and folder within these three archives, and gives general interpretations of these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".