Impact of COVID‑19 pandemic restrictions and subsequent relaxation on the prevalence of respiratory virus hospitalizations in children

BACKGROUND

The COVID-19 pandemic and the consequently adopted worldwide control measures have resulted in global changes in the epidemiology and severity of other respiratory viruses. We compared the number and severity of viral acute lower respiratory tract infection (ALRTI) hospitalizations and determined changes in causative respiratory pathogens before, during, and after the pandemic among young children in Qatar.

METHODS

In this single-center retrospective study, we reviewed data of children ≤ 36 months old who were admitted to Sidra Medicine in Qatar with a viral ALRTI during winter seasons (September-April) between 2019 and 2023. The study period was divided into three distinct seasons based on the pandemic-imposed restrictions as follows: (1) the period between September 2019 and April 2020 was considered the pre-COVID-19 pandemic season; (2) the periods between September 2020 and April 2021, and the period between January and April 2022 were considered the COVID-19 pandemic seasons; and (3) the periods between September 2022 and April 2023 was considered the post-COVID-19 pandemic season.

RESULTS

During the COVID-19 season, 77 patients were admitted, compared with 153 patients during the pre-COVID-19 season and 230 patients during the post-COVID-19 season. RSV was the dominant virus during the pre-COVID-19 season, with a detection rate of 50.9%. RSV infection rate dropped significantly during the COVID-19 season to 10.4% and then increased again during the post-COVID-19 season to 29.1% (P < 0.001). Rhinovirus was the dominant virus during the COVID-19 (39.1%) and post-COVID-19 seasons (61%) compared to the pre-COVID-19 season (31.4%) (P < 0.001). The average length of hospital stay was significantly longer in the post-COVID-19 season than in the pre-COVID-19 and COVID-19 seasons (P < 0.001). No significant differences in the pediatric intensive care unit (PICU) admission rate (P = 0.22), PICU length of stay (p = 0.479), or respiratory support requirements were detected between the three seasons.

CONCLUSION

Our study showed reduced viral ALRTI hospitalizations in Qatar during the COVID-19 pandemic with reduced RSV detection. An increase in viral ALRTI hospitalizations accompanied by a resurgence of RSV circulation following the relaxation of COVID-19 restrictions was observed without changes in disease severity.

Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions

One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based on various machine learning (ML) tools. Six traditional and tree-based ML algorithms were analyzed for the prediction in the current study. A rigorous feature importance study using RFECV method and relevant statistical analysis was conducted to identify the parameters that significantly contributed to the prediction. Among 16 input variables, the fluid velocity, mass flow rate, and pipe diameter were evaluated as the top predictors to estimate the frictional pressure losses. The significance of the contributing parameters was further validated by estimation error trend analyses. A comprehensive assessment of the regression models demonstrated an ensemble of the top three regressors to excel over all other ML and theoretical models. The ensemble regressor showcased exceptional performance, as evidenced by its high R2 value of 99.7 % and an AUC-ROC score of 98 %. These results were statistically significant, as there was a noticeable difference (within a 95 % confidence interval) compared to the estimations of the three base models. In terms of estimation error, the ensemble model outperformed the top base regressor by demonstrating improvements of 6.6 %, 11.1 %, and 12.75 % for the RMSE, MAE, and CV_MSE evaluation metrics, respectively. The precise and robust estimations achieved by the best regression model in this study further highlight the effectiveness of AI in the field of pipeline engineering.

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations

Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals' disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94.

Automated Detection of Substance-Use Status and Related Information from Clinical Text

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability.