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Record W4316038229 · doi:10.1016/j.vaccine.2023.01.021

Decline in reported measles cases in Italy in the COVID-19 era, January 2020 – July 2022: The need to prevent a resurgence upon lifting non-pharmaceutical pandemic measures

2023· article· en· W4316038229 on OpenAlexaboutno aff
Giacomo Facchin, Antonino Bella, Martina Del Manso, Maria Cristina Rota, Antonietta Filia

Bibliographic record

VenueVaccine · 2023
Typearticle
Languageen
FieldComputer Science
TopicCOVID-19 Digital Contact Tracing
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicMeaslesCoronavirus disease 2019 (COVID-19)MedicineVaccinationOutbreakTransmission (telecommunications)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakQuarter (Canadian coin)PediatricsEnvironmental healthVirologyDiseaseInfectious disease (medical specialty)GeographyInternal medicine

Abstract

fetched live from OpenAlex

From January 2020 to July 2022, 120 measles cases were reported to the Italian national surveillance system, of which 105 had symptom onset in 2020, nine in 2021 and six in the first seven months of 2022. This represents a sharp decline compared to the time period immediately preceding the COVID-19 pandemic, most likely due to the non-pharmaceutical interventions implemented to prevent SARS-CoV2 transmission. Of 105 cases reported in 2020, 103 acquired the infection before a national lockdown was instituted on 9 March 2020. Overall, one quarter of cases reported at least one complication. As non-pharmaceutical pandemic measures are being eased worldwide, and considering measles seasonality, infectiousness, and its potential severity, it is important that countries ensure high vaccination coverage and close immunity gaps, to avoid risk of future outbreaks.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.101
GPT teacher head0.378
Teacher spread0.277 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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