ABSTRACT
BACKGROUND: Understanding factors associated with SARS-CoV-2 exposure risk in the hospital setting may help improve infection control measures for prevention. AIM: To monitor SARS-CoV-2 exposure risk among healthcare workers and to identify risk factors associated with SARS-CoV-2 detection. METHODS: Surface and air samples were collected longitudinally over 14 months spanning 2020-2022 at the Emergency Department (ED) of a teaching hospital in Hong Kong. SARS-CoV-2 viral RNA was detected by real-time reverse-transcription polymerase chain reaction. Ecological factors associated with SARS-CoV-2 detection were analysed by logistic regression. A sero-epidemiological study was conducted in January-April 2021 to monitor SARS-CoV-2 seroprevalence. A questionnaire was used to collect information on job nature and use of personal protective equipment (PPE) of the participants. FINDINGS: SARS-CoV-2 RNA was detected at low frequencies from surfaces (0.7%, N = 2562) and air samples (1.6%, N = 128). Crowding was identified as the main risk factor, as weekly ED attendance (OR = 1.002, P=0.04) and sampling after peak-hours of ED attendance (OR = 5.216, P=0.03) were associated with the detection of SARS-CoV-2 viral RNA from surfaces. The low exposure risk was corroborated by the zero seropositive rate among 281 participants by April 2021. CONCLUSION: Crowding may introduce SARS-CoV-2 into the ED through increased attendances. Multiple factors may have contributed to the low contamination of SARS-CoV-2 in the ED, including hospital infection control measures for screening ED attendees, high PPE compliance among healthcare workers, and various public health and social measures implemented to reduce community transmission in Hong Kong where a dynamic zero COVID-19 policy was adopted.
ABSTRACT
For sustainable food production. In agriculture, crop yields are increasingly affected by warmer temperatures, and pest infestations caused by climate change have increased agricultural losses. Increasing local production is important to reduce our dependence on imported food and provide a buffer in case of supply disruptions such as those caused by the COVID-19 pandemic. To increase food security, it is important to optimize agricultural yields, despite the high costs associated with factors such as supplemental feeding, pest control measures, or operating costs. We present a Machine Vision method (MV) with Adversarial Autoencoder (AAE) as an approach to crop yield optimization. Predicted leaf area is projected from initial germination to early vegetative stages. Generative machine learning models are analyzed to determine a suitable architecture for crop yield prediction. Images of romaine lettuce grown over time under different conditions (e.g., light intensity) are used as the data set. Preliminary results show that the model created is able to predict an image with sufficient accuracy based on a single condition. With our method, corrective actions can be taken early, and yields recover from initial below-average values. Further work can be done to extend the model to other conditions such as moisture, strength of available sunlight, or soil nutrient content.