Occurrence and distribution of azithromycin in wastewater treatment plants, seawater, and sediments of the northern part of the Persian Gulf around Bushehr port: A comparison with Pre-COVID 19 pandemic.

One of the environmental effects of COVID 19 is the contamination of ecosystems with antibiotics due to their high consumption to treat this disease. Many years ago, the distribution of antibiotics including azithromycin (Azi) in wastewater treatment plants in Bushehr city, seawater, and sediment of the Persian Gulf has been investigated. As Azi has been prescribed to COVID 19 patients, contamination of the environment with this drug can also be assumed. Thus, we decided to examine this hypothesis by repeating our previous study during COVID 19 period. We collected wastewater samples from influent, effluent, and different units of three wastewater treatment plants (WWTPs) including one municipal WWTP (Plant A) and two hospital-WWTPs (Plant B and C). Seawater and adjusted sediments were gathered from 8 stations located in the Persian Gulf in two seasons to evaluate the special and temporal variation. The results showed a huge growth of Azi pollution in all studied matrixes. The mean Azi values in the influent of Plant A, B, and C were 145 ng/L, 110 ng/L, and 896 ng/L, which represented an 9, 6, and 48-time increase compared with those obtained in 2017 (before COVID 19). The Azi removal efficiency had a different behavior compared to before COVID 19. The mean concentration of Azi in seawater and sediment samples was 9 ng/L and 6 ng/g, which was 3 and 4-fold higher than the previous study. Opposed to our former study, the Azi amount in the aqueous phase was less subjected to temporal seasonal variations. Our observations indicated the wide distribution of Azi in the environment and a future threat of intense growth of antibiotic resistance in ecosystems.

Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach.

We designed a forecasting model to determine which frontline health workers are most likely to be infected by COVID-19 among 220 nurses. We used multivariate regression analysis and different classification algorithms to assess the effect of several covariates, including exposure to COVID-19 patients, access to personal protective equipment, proper use of personal protective equipment, adherence to hand hygiene principles, stressfulness, and training on the risk of a nurse being infected. Access to personal protective equipment and training were associated with a 0.19- and 1.66-point lower score in being infected by COVID-19. Exposure to COVID-19 cases and being stressed of COVID-19 infection were associated with a 0.016- and 9.3-point higher probability of being infected by COVID-19. Furthermore, an artificial neural network with 75.8% (95% confidence interval, 72.1-78.9) validation accuracy and 76.6% (95% confidence interval, 73.1-78.6) overall accuracy could classify normal and infected nurses. The neural network can help managers and policymakers determine which frontline health workers are most likely to be infected by COVID-19.