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Temporary isolation wards have been introduced to meet demands for airborne-infection-isolation-rooms (AIIRs) during the COVID-19 pandemic. Environmental sampling and outbreak investigation was conducted in temporary isolation wards converted from general wards and/or prefabricated containers, in order to evaluate the ability of such temporary isolation wards to safely manage COVID-19 cases over a period of sustained use. Environmental sampling for SARS-CoV-2 RNA was conducted in temporary isolation ward rooms constructed from pre-fabricated containers (N = 20) or converted from normal-pressure general wards (N = 47). Whole genome sequencing (WGS) was utilized to ascertain health care-associated transmission when clusters were reported amongst HCWs working in isolation areas from July 2020 to December 2021. A total of 355 environmental swabs were collected; 22.4% (15/67) of patients had at least one positive environmental sample. Patients housed in temporary isolation ward rooms constructed from pre-fabricated containers (adjusted-odds-ratio, aOR = 10.46, 95% CI = 3.89-58.91, P =.008) had greater odds of detectable environmental contamination, with positive environmental samples obtained from the toilet area (60.0%, 12/20) and patient equipment, including electronic devices used for patient communication (8/20, 40.0%). A single HCW cluster was reported amongst staff working in the temporary isolation ward constructed from pre-fabricated containers; however, health care-associated transmission was deemed unlikely based on WGS and/or epidemiological investigations. Environmental contamination with SARS-CoV-2 RNA was observed in temporary isolation wards, particularly from the toilet area and smartphones used for patient communication. However, despite intensive surveillance, no healthcare-associated transmission was detected in temporary isolation wards over 18 months of prolonged usage, demonstrating their capacity for sustained use during succeeding pandemic waves. [ABSTRACT FROM AUTHOR]

Given the role of caring for people with a wide variety of firearm-related injuries across the continuum of care, physiatrists particularly understand the medical and public health consequences of firearm-related injuries. Data from the Centers for Disease Control and Prevention (CDC) in 2018 showed that ~120.5 firearms were sold in the marketplace for every 100 people in the United States. This article describes several root causes of firearm-related injuries, how firearm-related injuries affect physiatric practice, and opportunities to advocate for firearm-related injury prevention. As for H.R. 5717, some of the important points of this proposed legislation include raising the minimum purchase age of firearms from age 18 to 21; removing civil liability limitations of firearm manufacturers; and promoting research on firearms safety and firearm violence prevention. [Extracted from the article]

The main objective of this paper is to further improve the current time-series prediction (forecasting) algorithms based on hybrids between machine learning and nature-inspired algorithms. After the recent COVID-19 outbreak, almost all countries were forced to impose strict measures and regulations in order to control the virus spread. Predicting the number of new cases is crucial when evaluating which measures should be implemented. The improved forecasting approach was then used to predict the number of the COVID-19 cases. The proposed prediction model represents a hybridized approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics. The enhanced beetle antennae search is utilized to determine the parameters of the adaptive neuro-fuzzy inference system and to improve the overall performance of the prediction model. First, an enhanced beetle antennae search algorithm has been implemented that overcomes deficiencies of its original version. The enhanced algorithm was tested and validated against a wider set of benchmark functions and proved that it substantially outperforms original implementation. Afterwards, the proposed hybrid method for COVID-19 cases prediction was then evaluated using the World Health Organization's official data on the COVID-19 outbreak in China. The proposed method has been compared against several existing state-of-the-art approaches that were tested on the same datasets. The proposed CESBAS-ANFIS achieved R 2 score of 0.9763, which is relatively high when compared to the R 2 value of 0.9645, achieved by FPASSA-ANFIS. To further evaluate the robustness of the proposed method, it has also been validated against two different datasets of weekly influenza confirmed cases in China and the USA. Simulation results and the comparative analysis show that the proposed hybrid method managed to outscore other sophisticated approaches that were tested on the same datasets and proved to be a useful tool for time-series prediction. • Proposed hybrid ML and SI method for predicting new COVID-19 cases. • The exploration process of BAS metaheuristics was improved. • Proposed approach CESBAS was first tested on standard benchmarks. • The CESBAS was utilized for updating parameters of ANFIS. • Hybridized CESBAS-ANFIS approach was applied for predicting COVID-19 cases and showed better performance than other approaches. [ABSTRACT FROM AUTHOR]