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Record W4387563986 · doi:10.1002/jmv.29166

Spatiotemporal cluster of mpox in men who have sex with men: A modeling study in 83 countries

2023· article· en· W4387563986 on OpenAlexaboutno aff
Weijing Shang, Guiying Cao, Yu Wu, Liangyu Kang, Yaping Wang, Peng Gao, Jue Liu, Min Liu

Bibliographic record

VenueJournal of Medical Virology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsMen who have sex with menDemographyLatin AmericansCluster (spacecraft)GeographyHuman immunodeficiency virus (HIV)MedicineVirologyPolitical science

Abstract

fetched live from OpenAlex

Mpox outbroke globally during 2022-2023, with more than 90% of cases occurring in men who have sex with men (MSM). However, the spatiotemporal distribution of mpox is not well established yet. This study aimed to explore the spatiotemporal clustering of mpox cases in MSM worldwide. We obtained the numbers of mpox cases from Our World in Data, the number of MSM from the Joint United Nations Programme on HIV/AIDS (UNAIDS), UNAIDS DATA 2021 and UNAIDS Global AIDS Update 2022 and literature. We evaluated the spatiotemporal cluster of mpox in MSM using retrospective space-time analyses method. The total number of mpox cases was 85 795 during May 1, 2022 to March 31, 2023. The most likely cluster was Spain (likelihood ratio = 4764.97; p < 0.001), with a cluster period from July 26 to August 14, 2022. There were 11 secondary clusters, which included 46 countries located in western Europe, eastern and northern South America, northern Europe, Canada, Central Africa, southern and central Europe, Latin America, Turkey, Dominican Republic, New Zealand, and Australia. The findings may inform current and future control strategies of mpox and might provide references for the identification of the spatiotemporal distribution of new and emerging infectious diseases in specific populations.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.313
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2023
Admission routes1
Has abstractyes

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