COVID‐19 in Chihuahua, Mexico: Assessing its spatial behaviour through the inverse distance weighted interpolation technique
Bibliographic record
Abstract
Abstract This study focused on the spatial evolution of COVID‐19 in the state of Chihuahua, Mexico. Data were retrieved from governmental databases and analyzed by means of GIS, applying the inverse distance weighted (IDW) method. The period of December 2019 through November 2021 was split into eight seasons. The root mean square error (RMSE) was used to assess the reliability of the interpolations, showing acceptable values (RSME < 25). During the period, the municipalities of Juarez and Chihuahua reached the highest number of confirmed cases and deaths, Juarez being the main hotspot of contagion (37.2% of confirmed cases; 46.9% of deaths). Four waves of contagion were identified during the evaluated period, with Fall 2020 being the strongest season. Since Fall 2020, the spread of the disease was more often observed in municipalities with the highest human mobility. Although the spread of COVID‐19 decreased after Spring 2021, in Fall 2021 records indicated a continuous increase in cases in the state. That could be due to a relaxation of the implementation of sanitary measures, as well as to the propagation of novel COVID‐19 variants having an elevated infectious level. Geospatial techniques allowed for an understanding of the spatial spread of COVID‐19 and could be useful for its control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".