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Record W7128542085 · doi:10.64903/1480-6800-25.1.63

Trend Analysis and Spatial Distribution of COVID-19 Cases in Jordan

2022· article· W7128542085 on OpenAlexvenueno aff
Sattam Al Shogoor, Eman Almhadeen, I. M. Oroud

Bibliographic record

VenueArab world geographer · 2022
Typearticle
Language
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsTrend analysisChristian ministryDistribution (mathematics)Public healthSpatial distributionCapital cityOutbreak

Abstract

fetched live from OpenAlex

Understanding the pattern of the COVID-19 outbreak and its spread is essential to guide public health control measures. The present study aims to analyze the spatial distribution of COVID-19 and its temporal trends in Jordan using GIS. The total confirmed official cases of COVID-19 in 2021 reached 778,306, with 8635 deaths, as reported by the Ministry of Health in Jordan. The capital of Jordan, Amman had reports of 356,613 Covid-19 cases, while the confirmed cases in the governorates Irbid, and Zarqa, were 134,424 and 76,971, respectively. With respect to incidence, the top three governorates were Madaba at 109 per one thousand infections, Tafilah at 101, and the Balqa at 95.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.008
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.083
GPT teacher head0.364
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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