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Record W3167091319 · doi:10.31579/2690-4861/109

Trends, patterns, and incidence rate of seasonal influenza among Dubai population and some associated factors, 2017-2-19

2021· article· en· W3167091319 on OpenAlexaboutno aff
Hamid Hussain

Bibliographic record

VenueInternational Journal of Clinical Case Reports and Reviews · 2021
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)DemographySeasonal influenzaPopulationAge groupsMedicineQuarter (Canadian coin)SeasonalityDiseaseGeographyEnvironmental healthCoronavirus disease 2019 (COVID-19)Internal medicineBiologyInfectious disease (medical specialty)

Abstract

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Background: WHO estimates that seasonal influenza may result in 290 000-650 000 deaths each year due to respiratory diseases alone. In addition, affected more than 10% of total population annually worldwide, Seasonal influenza is highly contagious disease; spreads easily, with rapid transmission in crowded areas including schools and nursing homes. Objectives: To Study incidence rate, trends and patterns of seasonal influenza among Dubai population for the period 2017-2019, to Study some of the associated factors. Materials & Subjects: A retrospective records review study was carried out of convenience sample of 29158 confirmed seasonal influenza case reported in Emirate of Dubai for the period 2017-2019. All age groups, genders, nationalities, occupations, education and seasons were considered. Findings: The study showed that 53.42% of total seasonal influenza cases were among male groups in Dubai, almost 50% % of the cases were among age group less than 15 years old, and almost one quarter of cases were among the age group between 30-40 years old, the present study showed that 54.37% were among Asian groups, 14.59% of the seasonal influenza incidence in Dubai during 2017-2019 were among Emirati population and 18.79% were among Arabs groups .As per occupation, the study showed that 30.74% of total seasonal influenza cases were among students in Dubai, on the other hand the study revealed that 84.53% of the total seasonal influenza cases during 2017-2019 were handled at outpatient level, yet 15.47% were sever enough cases to be admitted and treated at inpatient facilities. Incidence rate per 100000 population were increased respectively from 2017 through out 2019 (168, 297,466). The study revealed as well that the rate as per nationality the seasonal influenza incidence rate in Dubai from 2017=2019 650/100000 among Jordanian living in Dubai,, almost 50% % of the cases were among age group less than 15 years old, and almost one quarter of cases were among the age group between 30-40 years old, the present study showed that 54.37% were among Asian groups, 14.59% of the seasonal influenza incidence in Dubai during 2017-2019 were among Emirati population and 19.71% were among Arabs groups . The study showed that 30.74% of total seasonal influenza were students in Dubai, 84.53% of the total seasonal influenza cases during 2017-2019 were managed at outpatient. yet 15.47% were sever enough cases to be admitted and treated at inpatient level of different health care facilities in Dubai. Incidence among Egyptian was 557/100000, while among Emirates, 325 /100000, Incidence rate of seasonal influenza 2017-2019 according to age distributions showed that 30.7%among students, and 7.8%among children preschool age, and 5.22%among housewives. The present study showed that the incidence rate of seasonal influenza in Dubai in 2017-2019 as per moth distributions was the highest, 21.4%in November followed by 18.2%in December, and the least was 2%in July. Conclusions: incidence rate of seasonal influenza in Dubai keep increasing during the last three years, the highest rates significantly come from children segment of population specially students and elderly group as well, the period from October to end of February of each years.

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)0.003
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 score0.026
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.116
GPT teacher head0.454
Teacher spread0.338 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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