Detection and Persistence of SARS-CoV-2 in Air Samples from Admission Areas of Confirmed COVID-19 Patients: A Multicenter Study
Background: Implementing efficient infection control measures, and preventing nosocomial transmission is vital to have understand the transmission mechanism of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) within the hospital setting. Aim of the Study: to determine the sources of COVID-19 infection and the duration of the virus's presence in the working environment. This will allow the establishment of infection control measures and the timing of isolation, as well as the reduction of nosocomial infections among personnel working in healthcare and patients. Methodology: Our research is a multicentric observational study that was carried out in Egypt within the General Organization of Teaching Hospitals and Institutes. A total of 216 air samples were collected from intensive care units (ICUs) while 20 patients with confirmed SARS-CoV-2 infection were admitted for a period of four consecutive days between the months of May 2022 and April 2024. Results: Based on the findings, it was determined that 208 (96.5%) of the air samples were negative, while only 3.7% demonstrated positive results. The percentage of positive samples in the total samples increased from 3% on day 3 to 13% on day 4. Conclusion: Because the environment surrounding SARS-CoV-2 patients is a possible source of virus transmission for at least four days after admission, it is imperative that patients and healthcare personnel take the necessary precautions to prevent the spread of infection. These precautions include thorough surface disinfection, air filtering, and basic isolation practices.
Authors
- Nuhad K. Ibrahim (ORCID: https://orcid.org/0000-0001-8660-243X)
- Mohamed Abdelghaffar
- Sabina Fattah
- Amira Tohamey Mersal
Institutions
- Al-Azhar University (EG)
- General Organization For Teaching Hospitals and Institutes (EG)
Publication Details
- Journal
- Egyptian Academic Journal of Biological Sciences. B, Zoology
- Published
- 2025-06-08
- DOI
- https://doi.org/10.21608/eajbsz.2025.431923
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00