Investigating the Presence of SARS-CoV-2 on the Surfaces, Fomites, and in Indoor Air of a Referral COVID-19 Hospital, Shiraz, Iran

Background: Coronavirus disease (COVID-19) is an immensely transmissible viral infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study aimed to assess the presence of SARS-CoV-2 in the indoor air, on the surfaces, and on the fomites of a COVID-19 referral hospital in Shiraz, Iran. Methods: In this cross-sectional study, indoor air sampling was conducted utilizing a standard midget impinger containing 15 ml of viral transfer medium (VTM) equipped with a sampling pump with a flow rate of 10 L min-1 for 60 minutes. Surfaces and fomites were sampled using sterile polyester swabs. The real-time reverse transcription-polymerase chain reaction (rRT-PCR) was utilized to detect SARS-CoV-2. Results: The RNA of SARS-CoV-2 was detected in about 41.2% indoor air and 32% swab samples. Four out of the six (66.7%) indoor air samples up to a distance of 2 meters from the patient's bed in intensive care units (ICU-1, ICU-3), accident and emergency (A&E-2), and negative pressure rooms were positive for SARS-CoV-2 RNA. All air samples within 2 to 5 meters of the patient's bed were negative. Conclusion: This study's results did not support the airborne SARS-CoV-2 transmission;However, it showed contamination of surfaces and fomites in the studied hospital's wards. © 2023 Authors. All rights reserved.

Forecasting the Condition Severity of COVID-19 in Terms of Prevalence in Iran

Since the advent of the Coronavirus disease 2019 (COVID-19) pandemic, one of the most significant attempts toward coping with this disease was predicting the severity condition of this pandemic in terms of prevalence. In this way, the authorities can make better and more informative decisions. Machine Learning has great potential to solve such data-driven problems. In this article, the issue of predicting the future COVID-19 prevalence severity as a classification task has been investigated. We employed four state-of-the-art predictive models, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Self-Attention mechanism, in order to predict the COVID-19 prevalence severity in Iran using the gathered dataset by Johns Hopkins University. The main challenge we have faced was data scarcity because of the limited duration of the pandemic. Besides, as we are considering a classification task, data imbalance was an issue due to imposing a conventional criterion to divide each day into three classes regarding the state of severity. The final results suggest that although the performance of all models was acceptable, Self-Attention with an accuracy of 95% was the superb architecture to deal with this classification task. © 2022 IEEE.

Particle Size, Mass Concentration, and Microbiota in Dental Aerosols.

Many dental procedures are considered aerosol-generating procedures that may put the dental operator and patients at risk for cross-infection due to contamination from nasal secretions and saliva. This aerosol, depending on the size of the particles, may stay suspended in the air for hours. The primary objective of the study was to characterize the size and concentrations of particles emitted from 7 different dental procedures, as well as estimate the contribution of the nasal and salivary fluids of the patient to the microbiota in the emitted bioaerosol. This cross-sectional study was conducted in an open-concept dental clinic with multiple operators at the same time. Particle size characterization and mass and particle concentrations were done by using 2 direct reading instruments: Dust-Trak DRX (Model 8534) and optical particle sizer (Model 3330). Active bioaerosol sampling was done before and during procedures. Bayesian modeling (SourceTracker2) of long-reads of the 16S ribosomal DNA was used to estimate the contribution of the patients' nasal and salivary fluids to the bioaerosol. Aerosols in most dental procedures were sub-PM1 dominant. Orthodontic debonding and denture adjustment consistently demonstrated more particles in the PM1, PM2.5, PM4, and PM10 ranges. The microbiota in bioaerosol samples were significantly different from saliva and nasal samples in both membership and abundance (P < 0.05) but not different from preoperative ambient air samples. A median of 80.15% of operator exposure was attributable to sources other than the patients' salivary or nasal fluids. Median operator's exposure from patients' fluids ranged from 1.45% to 2.75%. Corridor microbiota showed more patients' nasal bioaerosols than oral bioaerosols. High-volume saliva ejector and saliva ejector were effective in reducing bioaerosol escape. Patient nasal and salivary fluids are minor contributors to the operator's bioaerosol exposure, which has important implications for COVID-19. Control of bioaerosolization of nasal fluids warrants further investigation.