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12th International Conference on Information Technology in Medicine and Education, ITME 2022 ; : 9-13, 2022.
Article in English | Scopus | ID: covidwho-2320734
Aerosol and Air Quality Research ; 23(4), 2023.
Article in English | Web of Science | ID: covidwho-2311554
Water (Switzerland) ; 15(6), 2023.
Article in English | Scopus | ID: covidwho-2295944
6th International Conference on Information Technology, InCIT 2022 ; : 59-63, 2022.
Article in English | Scopus | ID: covidwho-2291887
43rd Asian Conference on Remote Sensing, ACRS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2253669
Journal of Advanced Transportation ; 2023, 2023.
Article in English | ProQuest Central | ID: covidwho-2287626
Marine Pollution Bulletin ; Part A. 185 (no pagination), 2022.
Article in English | EMBASE | ID: covidwho-2287552
17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023 ; 2023.
Article in English | Scopus | ID: covidwho-2287416
European Journal of Interdisciplinary Studies ; 14(2):63-81, 2022.
Article in English | ProQuest Central | ID: covidwho-2217978
5th International Conference on Computer Science and Software Engineering, CSSE 2022 ; : 610-614, 2022.
Article in English | Scopus | ID: covidwho-2194138
Sci Total Environ ; 866: 161467, 2023 Mar 25.
Article in English | MEDLINE | ID: covidwho-2165842


Wastewater-based epidemiology has proven to be a supportive tool to better comprehend the dynamics of the COVID-19 pandemic. As the disease moves into endemic stage, the surveillance at wastewater sub-catchments such as pump station and manholes is providing a novel mechanism to examine the reemergence and to take measures that can prevent the spread. However, there is still a lack of understanding when it comes to wastewater-based epidemiology implementation at the smaller intra-city level for better granularity in data, and dilution effect of rain precipitation at pump stations. For this study, grab samples were collected from six areas of Seattle between March-October 2021. These sampling sites comprised five manholes and one pump station with population ranging from 2580 to 39,502 per manhole/pump station. The wastewater samples were analyzed for SARS-CoV-2 RNA concentrations, and we also obtained the daily COVID-19 cases (from individual clinical testing) for each corresponding sewershed, which ranged from 1 to 12 and the daily incidence varied between 3 and 64 per 100,000 of population. Rain precipitation lowered viral RNA levels and sensitivity of viral detection but wastewater total ammonia (NH4+-N) and phosphate (PO43--P) were shown as potential chemical indicators to calibrate/level out the dilution effect. These chemicals showed the potential in improving the wastewater surveillance capacity of COVID-19.

COVID-19 , SARS-CoV-2 , Humans , COVID-19/epidemiology , Wastewater , Wastewater-Based Epidemiological Monitoring , Calibration , Pandemics , RNA, Viral
Sustainability ; 14(19):11830, 2022.
Article in English | ProQuest Central | ID: covidwho-2066372
Life (Basel) ; 12(9)2022 Sep 19.
Article in English | MEDLINE | ID: covidwho-2043842


COVID-19 affects several human genes, each with its own p-value. The combination of drugs associated with these genes with small p-values may lead to an estimation of the combined p-value between COVID-19 and some drug combinations, thereby increasing the effectiveness of these combinations in defeating the disease. Based on human genes, we introduced a new machine learning method that offers an effective drug combination with low combined p-values between them and COVID-19. This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Additionally, at a significance level of less than 0.01, the combination of interleukin 1 receptor antagonist protein + camostat + azithromycin + tocilizumab + oseltamivir with a significance level of 0.006, and the combination of interleukin 1 receptor antagonist protein + camostat + chloroquine + favipiravir + tocilizumab7 with corticosteroid + camostat + oseltamivir + remdesivir + tocilizumab at a significant level of 0.009 are effective in the treatment of patients with COVID-19 and are also recommended. The results of this study provide sets of effective drug combinations for the treatment of patients with COVID-19. In addition, the new artificial intelligence used in the RAIN method could provide a forward-looking approach to clinical trial studies, which could also be used effectively in the treatment of diseases such as cancer.

Hervormde Teologiese Studies ; 78(4), 2022.
Article in English | ProQuest Central | ID: covidwho-2040085