Dependency of sanitation infrastructure on the discharge of faecal coliform and SARS-CoV-2 viral RNA in wastewater from COVID and non-COVID hospitals in Dhaka, Bangladesh.

The detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA in wastewater can be used as an indicator of the presence of SARS-CoV-2 infection in specific catchment areas. We conducted a hospital-based study to explore wastewater management in healthcare facilities and analyzed SARS-CoV-2 RNA in the hospital wastewater in Dhaka city during the Coronavirus disease (COVID-19) outbreak between September 2020-January 2021. We selected three COVID-hospitals, two non-COVID-hospitals, and one non-COVID-hospital with COVID wards, conducted spot-checks of the sanitation systems (i.e., toilets, drainage, and septic-tank), and collected 90 untreated wastewater effluent samples (68 from COVID and 22 from non-COVID hospitals). E. coli was detected using a membrane filtration technique and reported as colony forming unit (CFU). SARS-CoV-2 RNA was detected using the iTaq Universal Probes One-Step kit for RT-qPCR amplification of the SARS-CoV-2 ORF1ab and N gene targets and quantified for SARS-CoV-2 genome equivalent copies (GEC) per mL of sample. None of the six hospitals had a primary wastewater treatment facility; two COVID hospitals had functional septic tanks, and the rest of the hospitals had either broken onsite systems or no containment of wastewater. Overall, 100 % of wastewater samples were positive with a high concentration of E. coli (mean = 7.0 log10 CFU/100 mL). Overall, 67 % (60/90) samples were positive for SARS-CoV-2. The highest SARS-CoV-2 concentrations (median: 141 GEC/mL; range: 13-18,214) were detected in wastewater from COVID-hospitals, and in non-COVID-hospitals, the median SARS-CoV-2 concentration was 108 GEC/mL (range: 30-1829). Our results indicate that high concentrations of E. coli and SARS-CoV-2 were discharged through the hospital wastewater (both COVID and non-COVID) without treatment into the ambient water bodies. Although there is no evidence for transmission of SARS-CoV-2 via wastewater, this study highlights the significant risk posed by wastewater from health care facilities in Dhaka for the many other diseases that are spread via faecal oral route. Hospitals in low-income settings could function as sentinel sites to monitor outbreaks through wastewater-based epidemiological surveillance systems. Hospitals should aim to adopt the appropriate wastewater treatment technologies to reduce the discharge of pathogens into the environment and mitigate environmental exposures.

Driving maneuver classification from time series data: a rule based machine learning approach.

Drivers' improper driving behavior plays a vital role in road accidents. Different approaches have been proposed to classify and evaluate driving performance to ensure road safety. However, most of the techniques are based on neural networks which work like a black box and make the logical reasoning behind the classification decision unclear. In this paper, we propose a rule-based machine learning technique using a sequential covering algorithm to classify the driving maneuvers from time-series data. In the sequential covering algorithm, the impact of each rule is measured as the metrics of coverage and accuracy, where the coverage and accuracy indicate the amount of covered and correctly identified instances in a maneuver class, respectively. The final ruleset for each maneuver class is formed with only the significant rules. In this way, the rules are learned in an unsupervised manner and only the best performance of the rules are included in the ruleset. The set of rules is also optimized by pruning based on the performance of the test data. Application of the proposed system is beneficial compared to the traditional machine learning and deep learning approaches which typically require a larger dataset and higher computational time and complexity.