Incidence of delirium in the critical care unit and risk factors in the Central Region, Saudi Arabia
Bibliographic record
Abstract
Objectives The first case of Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) was identified on March 21, 2020, in Uganda. The number of cases increased to 8,287 by September 30, 2020. By May throughout June, most of the cases were predominantly imported cases of truck drivers from neighbouring countries. Uganda responded with various restrictions and interventions including lockdown, physical distancing, hand hygiene, and use of face masks in public, to control the growth rate of the outbreak. By end of September 2020, Uganda had transitioned into community transmissions and most of the reported cases were locals contacts and alerts. This study assessed risks associated with SARS-CoV-2 in Uganda, and presents estimates of the reproduction ratio in real time. An optimal control analysis was performed to determine how long the current mitigation measures such as controlling the exposure in communities, rapid detection, confirmation and contact tracing, partial lockdown of the vulnerable groups and control at the porous boarders, could be implemented and at what cost. Methods The daily confirmed cases of SARS-CoV-2 in Uganda were extracted from publicly available sources. Using the data, relative risks for age, gender, and geographical location were determined. Four approaches were used to forecast SARS-CoV-2 in Uganda namely linear exponential, nonlinear exponential, logistic and a deterministic model. The discrete logistic model and the next generation matrix method were used to estimate the effective reproduction number. Results Results showed that women were at a higher risk of acquiring SARS-CoV-2 than the men, and the population attributable risk of SARS-CoV-2 to women was 42.22%. Most of the women affected by SARS-CoV-2 were likely contacts of cargo truck drivers at the boarders, where high infection rates were reported. Although most deaths in Uganda were in the age group of 60-69, the highest case fatality rate per 1000 was attributable the age group of 80-89, followed by 70-79. Geographically, Amuru had the highest relative risk compared to the national risk to SARS-CoV-2. For the case of mitigation scenarios, washing hands with 70% com pliance and regular hand washing of 6 times a day, was the most effective and sustainable to reduce SARS- CoV-2 exposure. This was followed by public wearing of face masks if at least 60% of the population complied, and physical distancing by 60% of the population. If schools, bars and churches were opened without compliance, i.e., no distancing, no handwashing and no public wearing of face masks, to mitigation measures, the highest incidence was observed, leading to a big replacement number. If mitigation measures are not followed by the population, then there will be high incidences and prevalence of the virus in the population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".